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Journal articles on the topic 'An audio file'
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Silitonga, Parasian D. P., and Irene Sri Morina. "Compression and Decompression of Audio Files Using the Arithmetic Coding Method." Scientific Journal of Informatics 6, no. 1 (2019): 73–81. http://dx.doi.org/10.15294/sji.v6i1.17839.
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Audio file size is relatively larger when compared to files with text format. Large files can cause various obstacles in the form of large space requirements for storage and a long enough time in the shipping process. File compression is one solution that can be done to overcome the problem of large file sizes. Arithmetic coding is one algorithm that can be used to compress audio files. The arithmetic coding algorithm encodes the audio file and changes one row of input symbols with a floating point number and obtains the output of the encoding in the form of a number of values greater than 0 and smaller than 1. The process of compression and decompression of audio files in this study is done against several wave files. Wave files are standard audio file formats developed by Microsoft and IBM that are stored using PCM (Pulse Code Modulation) coding. The wave file compression ratio obtained in this study was 16.12 percent with an average compression process time of 45.89 seconds, while the average decompression time was 0.32 seconds.
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Sinaga, Helbert, Poltak Sihombing, and Handrizal Handrizal. "Perbandingan Algoritma Huffman Dan Run Length Encoding Untuk Kompresi File Audio." Talenta Conference Series: Science and Technology (ST) 1, no. 1 (2018): 010–15. http://dx.doi.org/10.32734/st.v1i1.183.
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Penelitian ini dilakukan untuk menganalisis perbandingan hasil kompresi dan dekompresi file audio*.mp3 dan *.wav. Kompresi dilakukan dengan mengurangi jumlah bit yang diperlukan untuk menyimpan atau mengirim file tersebut. Pada penelitian ini penulis menggunakan algoritma Huffman dan Run Length Encoding yang merupakan salah satu teknik kompresi yang bersifat lossless.Algoritma Huffman memiliki tiga tahapan untuk mengkompres data, yaitu pembentukan pohon, encoding dan decodingdan berkerja berdasarkan karakter per karakter. Sedangkan teknik run length ini bekerja berdasarkan sederetan karakter yang berurutan, yaitu hanya memindahkan pengulangan byte yang sama berturut-turut secara terus-menerus. Implementasi algoritma Huffman dan Run Length Encoding ini bertujuan untuk mengkompresi file audio *.mp3 dan *.wav sehingga ukuran file hasil kompresi lebih kecil dibandingkan file asli dimana parameter yang digunakan untuk mengukur kinerja algoritma ini adalah rasio kompresi, kompleksitas yang dihasilkan. Rasio kompresi file audio *.mp3 menggunakan Algoritma Huffman memiliki rata-rata 1.204% sedangkan RLE -94.44%, dan rasio kompresi file audio *.wav memiliki rata-rata 28.954 % sedangkan RLE -45.91%.
 This research was conducted to analyze the comparison of the results of compression and decompression of *.mp3 and *.wav audio files. Compression was completed by reducing the number of bits needed to save or send the file. In this study, the researcher used the Huffman algorithm and Run Length Encoding which is one of the lossless compression techniques. The Huffman algorithm has three stages to compress data, namely tree formation, encoding and decoding which work based on characters per character. On the other hand, the run length technique works based on a sequence of sequential characters that only move the repetitions of the same byte in succession continuously. The implementation of the Huffman algorithm and Run Length Encoding aimed to compress audio files *.mp3 and *.wav so that the size of the compressed file was smaller than the original file where the parameter used to measure the performance of this algorithm was the compression ratio, and the resulting complexity.*.Mp3 audio file compression ratio using Huffman Algorithm had an average of 1.204% while RLE -94.44%, and compression ratio *.wav audio files had an average of 28.954% while RLE -45.91%.
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Pasaribu, Noki Cahya Putra. "Analisis Implementasi Algoritma Elias Delta Codes Untuk Kompresi File Audio." Bulletin of Information System Research 2, no. 2 (2024): 64–72. https://doi.org/10.62866/bios.v2i2.148.
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The development of file sizes today is very diverse, some are small and large, one of the problems that is often encountered among the public is the large number of files stored in memory storage or hard disks on computers, Indeed, in this day and age, storage space already exists that can accommodate many files, but there are still those who do not have a large storage area, so that a lot of data is stored resulting in memory storage space and hard disks on the computer quickly full, and resulting in delays in the file delivery process, then the way that can be done is by compressing the file. One of the files sampled in this study is an audio file, then the solution to reducing the file size by compressing the file, by compressing the original size of the file will be different in size from the file that has been compressed, but it should be known that all compressed files also do not experience a reduction in size as well, when compressing requires a compression algorithm, in this study using the Elias delta codes algorithm, compressing using this algorithm, the original file with the file that has been compressed is different in size. The results of the research that has been done, in compressing audio files using the Elias delta codes algorithm has reduced the file size, proving that the compression technique carried out takes the hexadecimal value of the audio file as much as 22 reducing the file size 0.54% from the original mp3 file to 43.66 MB.
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Astuti, Tri Windi. "Implementasi Algoritma MD4 Pada Aplikasi Duplicate Audio Scanner." Jurnal Sains dan Teknologi Informasi 1, no. 4 (2022): 121–27. http://dx.doi.org/10.47065/jussi.v1i4.2293.
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Nowadays there are many applications that help the process of duplicating audio, of course this takes up more storage space. For that we need a method that can be used to identify the same or duplicate audio files. The solution that can be done to handle duplication of audio files or duplicates is to apply hash type cryptography techniques where the process of identifying the hash value of the audio file is carried out so that the resulting value is different from the original file. The hash type cryptographic algorithm used is the MD4 algorithm. The results obtained from the process of applying the MD4 algorithm are grouping audio files based on the same hash value, making it easier and faster for users to delete duplicate audio files
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Klimov, Roman Aleckseevich, and Azat Shavkatovich Yakupov. "Development of a System for Searching and Indexing the Content of Audio Recordings." Russian Digital Libraries Journal 26, no. 4 (2023): 483–97. https://doi.org/10.26907/1562-5419-2023-26-4-483-497.
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The article is devoted to the development of a search and indexing system for audio files using Automatic Speech Recognition (ASR) and Elasticsearch. Current Russian-language audio file transcription systems have been analyzed, and Whisper has been chosen as the best one. An algorithm for optimizing transcription speed using parallelization of file processing processes has been developed, and its effectiveness has been demonstrated. A microservice architecture-based system has been built, capable of indexing audio file content and their metadata for search purposes. The research results show that the proposed approach can be applied to create efficient and flexible systems for searching and analyzing audio information.
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Si, Ruochen, Masatoshi Arikawa, Hideki Kaji, Tianqi Xia, and Shibasaki Ryosuke. "No Sudden Audio Switch – Preventing discontinuous POI audio playing in LBS." Abstracts of the ICA 1 (July 15, 2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-337-2019.
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<p><strong>Abstract.</strong> Many LBS applications provide automatic audio playing functions for introducing POI’s. Appropriate automatic audio playing can improve users’ expressions during traveling with LBS and in some degree prevent walking-smartphone. However, the problem of discontinuity of audio playing on the contrary affects users’ expressions. One main reason that cause the discontinuity of audio playing is the sudden switch of audios, usually caused by a user entering the geofence of another POI while the audio of current POI is under playing. This paper proposed a preliminary approach to prevent the sudden switch of the audio instruction of POI’s by making adjustments on the POI’s audios and geofences.</p><p>Current main approach for automatic POI audio playing is using a geofence of the POI as a spatial trigger to control the audio playing. This simple approach may cause sudden switch of audio playing, which causes negative users’ expressions. As shown in Figure 1, a user’s moving trajectory frequently crosses geofences of two POI’s, which will result in a frequent switch of POI’s audios with neither completely played. The inflexible audio files and geofences are the main reasons causes the sharp audio switches and we are going to solve the problem by making the audio files and geofences flexible.</p><p>Conventional POI audio is made of one integrated file. As shown in Figure 2, we separate the POI audio content into several audio files, with each file consists one sentence or one short paragraph, and we call it <i>composed audio</i>. The files of a composed audio will be played in sequence, and the basic rule of playing a composed audio is not stopping until one file is completely played, which ensures that the composed audio will stop naturally after a complete sentence or paragraph. We define four important events for automatic playing composed audios. <i>Start playing</i> means start to play a composed audio from the beginning. It usually happens when a user enters a geofence of a POI. <i>Stop playing</i> means to stop a composed audio when finishing playing the current audio file. It usually happens when a user enters the geofence of another POI. <i>Prepare playing</i> means to start playing a composed audio when finishing playing the composed audio that is under playing. It usually happens when enters the geofence of a second POI. <i>Cancel playing</i> means to cancel the prepare playing status of a composed audio. It happens when a composed audio of a POI is in prepare playing status but the user leaves the geofence of the current POI or enters the geofence of another POI before the composed audio starting playing.</p><p>By introducing buffers to a geofence (Tokita, Arikawa, Si et al, 2018), it makes a geofence flexible, and we note the geofences with buffers as the <i>buffered geofences</i>. As shown in Figure 3, here we add an outer buffer to the geofence in Figure 1 to improve stability of POI’s audio play. An object has two status related to the buffered geofence: <i>out</i> and <i>in</i>, and it has two events related to the buffered geofence: <i>enter</i> and <i>leave</i>. The enter event occurs when the object is out of the buffered geofence and crosses the geofence’s boundary; the leave event occurs when the object is in the buffered geofence and crosses the geofence’s outer buffer. The result is that even though the user is frequently crossing the boundaries of the buffered geofences, he/she only enters each buffered geofences once, thus the audios of each POI will play once and will not be frequently switched.</p><p>Given a POI set S={POI<sub>1</sub>, POI<sub>2</sub>…POI<sub>n</sub>}, as shown in Table 1, by combining the composed audio event and buffered geofence event, we are able to play POI’s audios naturally without sudden or switches of the audios.</p><p>In this paper we introduced an approach to prevent discontinuous POI’s audio playing by composed audios and buffered geofences. This research is still preliminary, and it needs more researches on how to separate POI audios: too fragmented separation of POI audio may still make users feel an unnatural finish of an audio, and too generalized separation may result in more time costed before stopping a composed audio. Also, the current rules for playing composed audios based on the buffered geofences are relatively simple, which may cause unbalanced audio play. For example, when a user enters the buffered geofence of POI_A and soon enters the buffered geofence of POI_B, the time for playing composed audio of POI_A will be short. In the future, we plan to estimate the overall time for candidate audios to be played and balance the time for each of them.</p>
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Simanjuntak, Sari Magdalena. "Analisis Perbandingan Kompresi File Audio Menggunakan Algoritma Shannon Fano Dengan Algoritma Fibonacci Code." Jurnal Kajian Ilmiah Teknologi Informasi dan Komputer 2, no. 1 (2024): 1–10. http://dx.doi.org/10.62866/jutik.v2i1.110.
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Compression is a technique used to reduce the size of the data that serves to reduce memory capacity and speed up the data transfer process. In this study, the method used is the Shannon Fano method and the Fibonacci Code. In the Shannon Fano method the compression process is carried out by knowing the frequency of occurrence of each symbol which will then be sorted from the largest to the smallest frequency, while the Fibonacci Code method uses the Fibonacci integer series to encode the bit value of the data or file to be compressed. The file to be compressed is an audio file with an MP3 extension, to perform compression in reducing the size of the audio file, the technique used is lossless. Lossless technique is a compression technique that can restore compressed files converted to their original form (decompression). The algorithms used in file compression both use lossless techniques. Files that have been compressed using the Shannon Fano and Fibonacci Code methods will be compared to find out which method is more efficient in compressing audio files. The results showed that the method of Shannon Fano and Fibonacci can compress audio files. Based on the analysis of the compression ratio results generated using the Shannon Fano algorithm, there was a 69% change using 48 data samples, while the compression ratio results generated using the Fibonacci algorithm experienced a 66% change using the same 48 data samples as the Shannon Fano algorithm
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Anggraeni, Lenita Cahya, Ahmad Fashiha Hastawan, and Djuniadi Djuniadi. "Implementasi Kompresi File MP3 dengan Menerapkan Algoritma Levenstein." Jurnal Ilmiah SINUS 22, no. 1 (2024): 95. http://dx.doi.org/10.30646/sinus.v22i1.752.
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MP3 is an audio format that is often used because the data stored in MP3 format resembles the actual data when recorded.Storage space requirements will also rise if you keep a large number of MP3 files. Because of this, MP3 files must be compressed in order to prevent the storage space from filling up too quickly. This is because the compressed data will be lower in size. The Lossless compression method can be used to compress audio. The author uses the Levenstein Algorithm to implement audio compression in MP3 format. Levenstein's algorithm compresses data without any loss. When a file is compressed using lossless compression, the resulting file has the exact same size as the original file upon decompression. The author will be able to assess the effectiveness of MP3 file compression by using the Levenstein Algorithm to compress MP3 files. This will allow for faster transmission times and less data storage space consumption because the compressed MP3 file will shrink from its initial large size. After compression, the MP3 file's size—which was originally 128 bits—was reduced to 104 bits, yielding an 81.25% compression ratio. The purpose of this computation is to determine whether the compressed files are similar enough to be either decompressed or restored to their original size.
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Naufal, Muhammad Fakhri, Rini Marwati, and Ririn Sispiyati. "Kriptografi Audio Menggunakan Transposisi dan Affine Cipher yang Dikembangkan dengan Algoritma Blum Blum Shub." Jurnal EurekaMatika 9, no. 1 (2021): 1–14. http://dx.doi.org/10.17509/jem.v9i1.32634.
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Di era informasi, teknologi berkembang dengan pesat dan kemudahan dalam bertukar informasi menjadi sangat mudah, namun dengan perkembangan tersebut timbul suatu masalah yaitu keamanan informasi tersebut terutama untuk suatu informasi rahasia. Salah satu bentuk informasi seperti file audio memerlukan sebuah mekanisme untuk mengamankan file audio tersebut, salah satunya dengan kriptografi. Teknik kriptografi audio seperti transposisi membuat data audio teracak sehingga suara yang dihasilkan file audio tersebut tidak dapat dipahami. Namun, untuk meningkatkan keamanan nilai data audio pada file audio diperlukan teknik enkripsi substitusi salah satunya yaitu Affine Cipher. Dengan melakukan pengembangan pada Affine Cipher menggunakan pembangkit bilangan acak semu Blum Blum Shub, dapat memberikan peningkatan yang cukup signifikan pada teknik kriptografi klasik ini. Hasil yang diperoleh dengan mengenkripsi file audio WAV menggunakan Python dapat mengamankan file audio sehingga menghasilkan suara acak dan file audio terenkripsi dapat didekripsi untuk mendapatkan informasi asli.
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Rusdianto, Rusdianto, Natalia Silalahi, and Norenta Sitohang. "Penerapan Algoritma Rabin-Public Key Untuk Pengamanan File Audio." Bulletin of Artificial Intelligence 2, no. 1 (2023): 100–103. http://dx.doi.org/10.62866/buai.v2i1.45.
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Data encoding problems such as text files, images, audio and video are used more often as messages or information. However, with advances in technology that are increasingly developing, it is increasingly possible for messages or information to be stored in the form of other files such as images, audio and video. For example, a music recording company wants to release the latest album and they want to get a big profit from selling CDs or downloading legally (paid) through the site. Before the album officially goes out to the public, they want to make sure that no one party gets a leak or listens to the audio file first. One of the technical solutions that can be used to protect the confidentiality of audio files is cryptography by applying the Rabin-Public Key algorithm. The Rabin-p algorithm is an algorithm that implements an asymmetric key. An asymmetric key is a key that uses two types of keys, namely the public key which is used to encrypt the message and the secret key which is used to decrypt the message. Rabin-p is named rabin with an additional p which symbolizes that the proposed scheme uses only one prime p as the decryption key. The results for encoding audio files are based on an asymmetric cryptographic system that uses a public key and a private key for encoding audio files (the key used for the encryption process). and decryption) into a cipher so that the security and confidentiality of messages are maintained. Audio file analysis is the stage where analysis is carried out on any files that are processed in a design system or procedure, in this case the audio files to be encrypted and decrypted in cryptographic applications are audio files in wave format (*.WAV).
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Li, Zhen, and Qian Yi Yang. "The Research of Dynamic Sound of Multichannel System Based on Matlab." Applied Mechanics and Materials 602-605 (August 2014): 2569–71. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2569.
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With the emergence and development of 3D video technology, audiences have higher expectations to the sound effect while watching the 3D video. So we studied the dynamic sound effect under the existing audio standard of the multichannel. In this paper, the audio file of the sea wave was disposed by the Gaussian function through MATLAB and divided into several parts to be saved as different audio files. Then each audio file was sent to a channel to simulate a stereo sound field. Test result showed that the effect will provide a perfect experience for the audiences.
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Honsor, Oksana, and Yuriy Gonsor. "Identification of Birds' Voices Using Convolutional Neural Networks Based on Stft and Mel Spectrogram." Vìsnik Nacìonalʹnogo unìversitetu "Lʹvìvsʹka polìtehnìka". Serìâ Ìnformacìjnì sistemi ta merežì 14 (December 26, 2023): 297–311. http://dx.doi.org/10.23939/sisn2023.14.297.
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Threats to the climate and global changes in ecological processes remain an urgent problem throughout the world. Therefore, it is important to constantly monitor these changes, in particular, using non-standard approaches. This task can be implemented on the basis of research on bird migration information. One of the effective methods of studying bird migration is the auditory method, which needs improvement. That is why building a model based on machine learning methods that will help to accurately identify the presence of bird voices in an audio file for the purpose of studying bird migrations from a given area is an urgent problem. This paper examines ways of building a machine learning model based on the analysis of spectrograms, which will help to accurately identify the presence of bird voices in an audio file for the purpose of studying the migration of birds in a certain area. The research involves the collection and analysis of audio files that can be used to identify characteristics that will identify the sound of the files as birdsong or the absence of sound in the file. The use of the CNN model for the classification of the presence of bird voices in an audio file is demonstrated. Special attention is paid to the effectiveness and accuracy of the CNN model in the classification of sounds in audio files, which allows you to compare and choose the best classifier for a given type of file and model. Analysis of the effectiveness and accuracy of the CNN model in the classification of sounds in audio files showed that the use of Mel-spectrograms is better than the use of STFT-spectrograms for studying the classification of the presence of bird sounds in the environment. The classification accuracy of the model trained on the basis of Mel spectrograms was 72 %, which is 8 % better than the accuracy of the model trained on STFT spectrograms.
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Fakhri, La Jupriadi, Imam Riadi, and Anton Yudhana. "Investigasi File Carving pada Media Penyimpanan Menggunakan Framework Computer Forensic Investigative Process." Journal of Computer System and Informatics (JoSYC) 6, no. 1 (2024): 472–79. https://doi.org/10.47065/josyc.v6i1.6125.
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One of the uses of digital storage media in the digital era that is still popular today is the use of flash drives as a means of transferring data between computer devices. Flash disks are often used as evidence in digital investigation cases. The risk of losing data is one of the main problems that society often faces. Data loss occurs for various reasons, such as user error, device failure, malware attack, or criminal acts such as hacking. The file carving technique is used to recover lost or deleted files from digital storage media with Foremost software. However, with so many file types, it is sometimes difficult to choose which file types to recover and how to ensure the authenticity of the files. This study uses the Computer Forensic Investigative Process (CFIP) Framework on a flash drive, which is used as evidence in a criminal case. Foremost software is used to perform file carving techniques on flash drives. The results showed that the data acquisition process using DC3DD succeeded in producing digital evidence with a hash value that is identical to the original file. Foremost software successfully recovered various file types, such as 9 image files with jpg file type, 5 audio files with mp3 file type, and 5 document files with pdf file type. Foremost shows a high success rate, with file carving accuracy of 90% for image files, and 62.5% for audio files and documents. The average success rate of Foremost software in returning evidence is 73.08%.
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Janthakal, Sheetal. "Automatic Classification of Music Genres Using the Deep Learning Approach." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3573–77. http://dx.doi.org/10.22214/ijraset.2023.51683.
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Abstract: Music is divided into arbitrary groups known as genres. Music genre classification is a challenging task due to the subjective and ambiguous nature of musical genres. The existing systems for music genre classification suffer from low accuracy and poor generalization of new data. Therefore, there is a need to develop a robust and accurate machine-learning model that can overcome these challenges and classify music audio files into different genres with high accuracy. The main aim of the Music genre classification project is to develop a user-friendly application that accepts audio files as input and classifies the audio file into a particular category of sound to which they belong (to predict its genre) using machine learning models. This application automates the process to reduce manual error and time. It will take an audio file as input and categorizes each file into a particular category like audio belonging to Disco, hip-hop, etc. The final classification is obtained from the collection of individual data. This machine learning model makes use of Support Vector Machine(SVM) and Logistic Regression models. Both models will be integrated into a website to make the project easily accessible.
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Sumadi, Muhammad Taufiq, Achmad Nur Zahir S, and Faldi Faldi. "SECURE AUDIO FILES USING VIGENERE CIPHER AND PLAYFAIR CIPHER." Jurnal Teknik Informatika (Jutif) 5, no. 6 (2024): 1497–504. https://doi.org/10.52436/1.jutif.2024.5.6.1624.
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This study aims to maintain the confidentiality of audio files sent using a combination of the Playfair cipher and Vigenere cipher methods. In this research, the object of research is an audio file with the extension wave or *.wav. This research requires several stages, including Audio Data Analysis, Determination of System Architecture, Implementation, Testing, and Results Analysis. The results of this study indicate that in the Vigenere Cipher 256 Encryption in audio wave files, the audio messages conveyed sound unclear or have no meaning. From the 6 trial datasets based on analysis of MAE and PSNR, the average value of the encryption process at PSNR was 28.345, and MAE was 97.0625. The average value of the decryption process on PSNR and MAE is 0.0, indicating that the decryption process is successful. The speed of the encryption and decryption process is affected by the audio file's size, which means that the larger the file size, the longer the encryption and decryption time.
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Zeena, N. Al-Kateeb, and J. Mohammed Saja. "Encrypting an audio file based on integer wavelet transform and hand geometry." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 2012–17. https://doi.org/10.12928/TELKOMNIKA.v18i4.14216.
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A new algorithm suggested for audio file encryption and decryption utilizing integer wavelet transform to take advantage of the property for adaptive context-based lossless audio coding. In addition, biometrics are used to give a significant level of classification and unwavering quality because the procedure has numerous qualities and points of interest. The offered algorithm utilized many properties of hand geometry estimations as keys to encode and decode the audio file. Many tests were carried out on a set of audio files and quality metrics such as mean square error and correlations were calculated which in turn confirmed the efficiency and quality of the work.
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Tanwar, Rohit, Kulvinder Singh, Mazdak Zamani, Amit Verma, and Prashant Kumar. "An Optimized Approach for Secure Data Transmission Using Spread Spectrum Audio Steganography, Chaos Theory, and Social Impact Theory Optimizer." Journal of Computer Networks and Communications 2019 (September 8, 2019): 1–10. http://dx.doi.org/10.1155/2019/5124364.
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Being easy to understand and simple to implement, substitution technique of performing steganography has gain wide popularity among users as well as attackers. Steganography is categorized into different types based on the carrier file being used for embedding data. The audio file is focused on hiding data in this paper. Human has associated an acute degree of sensitivity to additive random noise. An individual is able to detect noise in an audio file as low as one part in 10 million. Given this limitation, it seems that concealing information within audio files would be a pointless exercise. Human auditory system (HAS) experiences an interesting behavior known as masking effect, which says that the threshold of hearing of one type of sound is affected by the presence of another type of sound. Because of this property, it is possible to hide some data inside an audio file without being noticed. In this paper, the research problem for optimizing the audio steganography technique is laid down. In the end, a methodology is proposed that effectively resolves the stated research problem and finally the implementation results are analyzed to ensure the effectiveness of the given solution.
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Sembiring, Zulfikar. "PERBANDINGAN METODE LOW BIT CODINGDENGAN PHASE CODING PADA DIGITAL AUDIO WATERMARKING." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 1, no. 1 (2017): 1. http://dx.doi.org/10.31289/jite.v1i1.569.
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<p>Penggunaan file audio sebagai media distribusi informasi digital sangat populer sekarang ini karena semakin canggihnya perangkat keras maupun perangkat lunak yang dapat mengolah file audio digital tersebut. Ditambah lagi dengan semakin mudahnya akses internet dimana saja baik melalui perangkat mobile ataupun tidak. Karena semakin banyaknya penggunaan file audio digital oleh perorangan atau perusahaan misalnya dalam produksi musik, atau video klip maka semakin sulit untuk menentukan keaslian suatu file audio digital dan sulitnya mencegah tingkat tindak pencurian atau pembajakan yang sangat merugikan pihak pememilik hak cipta. Ada beberapa metode dalam menentukan keaslian file audio digital dan mencegah tindak pembajakan terhadap media digital, yaitu digital watermarking. Pada jurnal ini akan dibahas dua buah metode watermarking yaitu metode low bit coding dan phase coding. Tujuannya ialah untuk mengetahui beberapa kelebihan dan kekurangan dalam penerapannya kedalam file audio digital. Karena ada beberapa aspek yang harus diketahui dalam menentukan baik atau tidaknya tingkat pengamanan pada file audio digital. Sehingga kita dapat menentukan metode yang mana yang pantas digunakan dalam menentukan keaslian file audio digital dalam mencegah tindak pencurian atau pembajakan.</p><strong>Kata kunci :</strong>audio, watermarking, low bit coding, phase coding
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Achmad Aditya Ashadul. "Aplikasi Steganografi Berbasis Android Menggunakan End of File dengan Enkripsi Rivest Code 4." KRESNA: Jurnal Riset dan Pengabdian Masyarakat 2, no. 2 (2022): 240–46. http://dx.doi.org/10.36080/jk.v2i2.45.
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Tujuan penelitian untuk menyembunyikan file audio melalui media video menggunakan metode EOF (End of File) dan enkripsi Rivest Code 4. Metode EOF (End of File) digunakan sebagai teknik penyembunyian pesan dalam steganografi dengan menyisipkan pesan pada akhir file media penampung. File audio yang dienkrip menggunakan Rivest Code 4 (RC4) akan disisipkan pada bagian akhir file video sebagai media penampung. Hasil penelitian ini aplikasi steganografi berbasis android yang dapat menjaga kerahasiaan file audio dengan tetap mempertahankan kualitas file video sebagai media penampung.
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Selihat, Maali, Belal Abuhaja, and Khalid Alkaabneh. "Secure audio file indexing based on hidden markov model (HMM) on the cloud storage." International Journal Artificial Intelligent and Informatics 2, no. 1 (2021): 1–8. http://dx.doi.org/10.33292/ijarlit.v2i1.30.
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With the introduction of many social media applications and the exponential growth of the number of people using such applications to exchange Audio files as a main way of conveying confidential messages relaying on public telecommunications and networks. The need arise to secure audio data files and preserve the integrity of the message while traversing public networks and when the data is at rest in the cloud. Therefore, in this research, to ensure confidentiality and integrity of the audio files while reducing storage space several algorithms have been devised. To achieve this, we utilized Public key infrastructure and Hash functions along with Hidden Markov Models (HMM). The results show a significant drop in the storage space needed while remarkable reduction in network transmission time. When comparing the original audio file size with the converted file size after applying HMM, the results show a variation between 0% and 10% only, with over 50% reduction in the storage space in some cases.
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Pakan, Prisca, and Rocky Yefrenes Dillak. "KLASIFIKASI MUSIK MENGGUNAKAN POLYNOMIAL NEURAL NETWORK." Jurnal Ilmiah Flash 3, no. 2 (2017): 94. http://dx.doi.org/10.32511/jiflash.v3i2.144.
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Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukanklasifikasi terhadap jenis musik berdasarkan file audio dengan format wav menggunakan algoritmaRidge Polynomial Neural Network (RPNN). Pengklasifikasian file audio ke dalam suatu kelompokatau kelas, memerlukan ciri atau fitur dari file audio tersebut. Metode ekstrak fitur yang digunakanuntuk memperoleh ciri atau fitur dari file yang dimaksud adalah Spectral Centroid (SC), SortTime Energy (STE) dan Zero Crossing Rate (ZCR) yang diturunkan dalam domain waktu (timedomain) yang merupakan salah satu komponen data audio. Berdasarkan hasil dari penelitian inimenunjukkan bahwa pendekatan yang diusulkan mampu melakukan klasifikasi terhadap jenis musikberdasarkan file audio berformat wav dengan akurasi sebesar 90%
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Pakan, Prisca, and Rocky Yefrenes Dillak. "KLASIFIKASI MUSIK MENGGUNAKAN POLYNOMIAL NEURAL NETWORK." Jurnal Ilmiah Flash 3, no. 2 (2017): 94. http://dx.doi.org/10.32511/flash.v3i2.144.
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Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukanklasifikasi terhadap jenis musik berdasarkan file audio dengan format wav menggunakan algoritmaRidge Polynomial Neural Network (RPNN). Pengklasifikasian file audio ke dalam suatu kelompokatau kelas, memerlukan ciri atau fitur dari file audio tersebut. Metode ekstrak fitur yang digunakanuntuk memperoleh ciri atau fitur dari file yang dimaksud adalah Spectral Centroid (SC), SortTime Energy (STE) dan Zero Crossing Rate (ZCR) yang diturunkan dalam domain waktu (timedomain) yang merupakan salah satu komponen data audio. Berdasarkan hasil dari penelitian inimenunjukkan bahwa pendekatan yang diusulkan mampu melakukan klasifikasi terhadap jenis musikberdasarkan file audio berformat wav dengan akurasi sebesar 90%
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Paramita, Cinantya, and Usman Sudibyo. "Kriptografi Audio MP3 Menggunakan RSA dan Transposisi Kolom." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (2021): 483–88. http://dx.doi.org/10.29207/resti.v5i3.2996.
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Mp3 is one form of audio file extension that is widely used today. With a variety of uses in a variety of mp3 systems become one of the audio extensions that are commonly found in technology systems of the Internet of Things era. However, with the many uses of the .mp3 file extension, there is a new problem, namely the security of the data itself. From these problems, the author aims to examine the security of the mp3 file by designing cryptographic science-based applications. The cryptographic algorithm used in the application is a combination of the asymmetric RSA 2048 algorithm and symmetric columnic transpositions. RSA 2048 algorithm was chosen because it has a key length in accordance with NIST standards in securing data. By combining the two algorithms, the application system will have the ability to manage mp3 files and encrypt mp3 files with the results of data that cannot be played like mp3 files in general. This application system will be developed by prototype method which is the best method in developing a system with trial and error in algorithm development.
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Enjelita Purba, Ginta. "Kompresi File Advanced Audio Coding (AAC) Menggunakan Metode Lempel Ziv Oberhumer (LZO)." Journal of Computing and Informatics Research 2, no. 1 (2022): 30–36. http://dx.doi.org/10.47065/comforch.v2i1.379.
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In an age of increasingly sophisticated technology such as today. The need for greater storage capacity is responsible for the emergence of various compression techniques. The more file is stored, the greater the capacity. The enormous size of file results in an inefficient memory and slow-motion process of file transfer. Getting older file storage results in more and more file piling up. As technology progresses today. And thus, a growing ability to process file is utilizing a compression method that is expected to address the problem, it will be necessary to minimize storage by using Lempel Ziv Oberhumer methods to compress Advanced Audio Coding(AAC) files to narrow the size of the beets. So by applying an Lempel Ziv Oberhumer to compression the file could reduce storage capacity optimally.
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Manna, Neelanjan. "Quantum Proof Encryption Technology." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 254–55. http://dx.doi.org/10.22214/ijraset.2021.38792.
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Abstract: Nowadays we use text passwords to encrypt a file. This research paper proposes to use multimedia files like images videos, audio files and even applications as the password key to encrypt sensitive information. This algorithm can encrypt bulk data as well as single data sets. Keywords: steganography, multimedia file as key, Quantum computer, cryptography, Quantum computer proof encryption.
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Fajri, Muhammad. "Penerapan Digital Signature Untuk Identitas File Audio Dengan Metode Snefru." Management of Information System Journal 1, no. 1 (2022): 19–28. http://dx.doi.org/10.47065/mis.v1i1.397.
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In the era of information technology that is developing very rapidly, the use of signatures has been widely applied digitally through digital signatures. Digital signatures along with the times have led to the need for authentication of data or files that are used digitally. Its use also aims to avoid counterfeiting or interference. Currently, the use of digital signatures has been widely applied to software distribution, financial transactions, file transfers. The crime of falsifying audio files is a serious problem in various fields. Audio authenticity testing is important and significant in all social areas, especially when audio is used as evidence for conclusions in courts, the basis for judicial decision-making, and corporate reports. Forgery of audio files will cause losses that cannot be estimated. One solution to the problems mentioned above is to perform the process of assigning an identity to the audio file, so that it can be seen that the audio cannot be manipulated. The SNEFRU method is a hash function to detect changes in digital audio. SNEFRU has several variants, varying in number of operands and hash size. The supported hash sizes are 128 and 256 bits. The number of passes in the final 2-pass variant source of SNEFRU is two passes, while a more secure 4-pass version is also available. After the previous attack was published, the 8-pass version was introduced as well. This 8-pass version is still considered safe.
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Nurasyiah. "Perancangan Aplikasi Kompresi File Audio dengan Algoritma Aritmetic Coding." JUKI : Jurnal Komputer dan Informatika 3, no. 1 (2021): 25–34. http://dx.doi.org/10.53842/juki.v3i1.38.
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Information exchange nowadays requires speed in sending information. The speed of this transmission depends on the size of the information. One solution to the above problem is compression. There are lots of data compression methods available today, but in this thesis we will discuss the working principles of the Arithmetic Coding algorithm with an implementation using Visual Basic 6.0. This algorithm performance analysis aims to determine the performance of this algorithm in * .MP3 and * .WAV audio files. In this system there are compression and decompression stages. The compression stage aims to compress the audio file size, while the decompression stage aims to restore the audio file size to its original size.
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Hassan, Najwan A., Farah Saad Al-Mukhtar, and Esraa H Ali. "Encrypt Audio File using Speech Audio File As a key." IOP Conference Series: Materials Science and Engineering 928 (November 19, 2020): 032066. http://dx.doi.org/10.1088/1757-899x/928/3/032066.
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Irawan, Bambang, and Dandung Trihatmojo. "Decentralized Trusted Storage of Audio-Video Log Data Based on Blockchain Technology and IPFS." International Journal of Science, Technology & Management 5, no. 2 (2024): 473–84. http://dx.doi.org/10.46729/ijstm.v5i2.1084.
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The development of communication and information technology has affected the world of television broadcasting in Indonesia. With the emergence of a new phenomenon, the convergence of digital media industry. The migration of analogue to digital television broadcasting has impacted various industries related to broadcasting. Especially for the sustainability of the television broadcasting community in the country. Station Tv x is one of the television communities, on the other hand the media industry has challenges in managing storage media consisting of audio and video data that has a large capacity. Audio video logs are needed as information on recording audio video files. Blockchain-based Interplenary File System (IPFS) technology is expected to be one of the alternatives that can be applied in the world of broadcasting, storage media and audio video file data distribution methods, data library security and data flexibility are one of the challenges faced in the television broadcasting industry. The purpose of this research is as an effort to decentralise audio video data in distributed storage media to be more optimal and secure. The results of this research can be used to distribute audio video data files in the data library at tv station x.
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Fatmawaty, Fatmawaty, and Mufty Mufty. "Analisis Perbandingan Kompresi File Wav Menggunakan Metode Huffman dan Run Length Encoding." Jurnal Teknologi Informasi dan Terapan 7, no. 1 (2020): 61–65. http://dx.doi.org/10.25047/jtit.v7i1.139.
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Saat ini terdapat berbagai macam pilihan format audio namun tidak banyak pengguna yang memahami perbedaan diantaranya. Format Audio (Audio Format) adalah medium penyimpanan data audio dan musik dalam bentuk fisik dan format rekaman sebuah konten audio. Dalam dunia komputer sering disebut dengan file audio format yang berarti sebuah bentuk penyimpanan data digital audio pada sistem komputer. Saat ini terdapat berbagai macam format audio contohnya adalah wave,Mp3, wma dan masih banyak format lainnya, namun dalam sistem operasi windows yang digunakan adalah file bertipe wave tipe ini banyak sekali digunakan untuk keperluan multimedia dan game di dalam sistem operasi windows. Namun jika kita ingin merekam suara sekualitas cd audio maka diperlukan suatu media penyimpanan yang sangat besar sehingga membuat boros penyimpanan data atau hard disk sehingga untuk mengurangi kapasitas penyimpanan tersebut dan juga untuk pengiriman file tersebut membutuhkan waktu yang lama sehingga kita harus mengkompres file tersebut sehingga penyimpanan lebih efesien dan pengiriman menjadi lebih cepat
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Muhammad Immawan Aulia, Panggah Widiandana, Wicaksono Yuli Sulistyo, Siti Hartinah, and Muhammad Azam Hasani. "Analisis Perbandingan Tool FTK Imager dan PhotoRec dalam Pemulihan Data Flashdrive Berbasis Metode Statik Forensik." JITU : Journal Informatic Technology And Communication 9, no. 1 (2025): 1–10. https://doi.org/10.36596/jitu.v9i1.1814.
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Penelitian ini bertujuan untuk mengevaluasi efektivitas dua tools digital forensik, yaitu FTK Imager dan PhotoRec, dalam proses recovery file dari hasil imaging data. Hasil penelitian menunjukkan bahwa FTK Imager berhasil melakukan recovery secara utuh terhadap enam file audio dan satu file teks, dengan format nama file dan nilai hash yang identik dengan bukti digital asli. Sebaliknya, PhotoRec hanya berhasil melakukan recovery terhadap empat file audio dan satu file teks, dengan dua file audio menunjukkan perbedaan nilai hash dibandingkan dengan aslinya. Temuan ini menunjukkan bahwa FTK Imager memiliki tingkat akurasi dan keandalan yang lebih tinggi dalam proses pemulihan data digital dibandingkan dengan PhotoRec, terutama dalam menjaga integritas data yang direkonstruksi.
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Jawahir, Ahmad, and Haviluddin Haviluddin. "An audio encryption using transposition method." International Journal of Advances in Intelligent Informatics 1, no. 2 (2015): 98. http://dx.doi.org/10.26555/ijain.v1i2.24.
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Encryption is a technique to secure sounds data from attackers. In this study, transposition technique that corresponds to a WAV file extension is used. The performance of the transposition technique is measured using the mean square error (MSE). In the test, the value of MSE of the original and encrypted audio files were compared; the original and decrypted audio files used the correct password is ‘SEMBILAN’ and the incorrect password is ‘DELAPAN’. The experimental results showed that the original and encrypted audio files, and the original and decrypted audio files used the correct password that has a value of MSE = 0, and with the incorrect one with a value of MSE 0.00000428 or ≠ 0. In other words, the transposition technique is able to ensure the security of audio data files.
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Pratiwi, Anggi. "Perancangan Aplikasi Kompresi File Audio Dengan Menerapkan Algoritma Additive Code." Journal Global Technology Computer 1, no. 3 (2022): 92–100. http://dx.doi.org/10.47065/jogtc.v1i3.2093.
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Proses kompresi data didasarkan pada kenyataan bahwa pada hampir semua jenis data selalu terdapat pengulangan pada komponen data yang dimilikinya, misalnya dalam suatu teks kalimat akan terdapat redudansi huruf alfabet dari huruf a sampai dengan huruf z . Kompresi data melalui proses encoding berusaha untuk menghilangkan unsur pengulangan ini dengan mengubahnya sedemikian rupa sehingga ukuran data menjadi lebih kecil. Dalam penelitian ini, dibangun sebuah aplikasi kompresi file audio dengan menggunakan algoritma Additive Code. Tool pengembangan sistem yang digunakan adalah Unified Modelling Language (UML). Dalam pembuatan aplikasi ini ditekankan untuk melakukan kompresi pada file audio berekstensi *.mp3. Pengujian aplikasi kompresi ini dilakukan dengan menguji file yang mempunyai ukuran berbeda-beda dengan nilai sampel rate dan bitrate yang sama. Disimpulkan bahwa file audio dengan ukuran yang besar membutuhkan waktu lebih lama dalam melakukan proses kompresi. File audio hasil kompresi tidak dapat dijalankan sebelum melewati proses dekompresi hal tersebut disebabkan terjadi perubahan struktur pada file audio saat proses dekompresi.
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Prof., Shemeena M1 Prof.J esna K. A2 &. Prof. Surjith S3. "IMPLEMENTATION OF AUDIO STEGANOGRAPHY USING LSB TECHNIQUE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY NACETEC' 19 (April 11, 2019): 201–6. https://doi.org/10.5281/zenodo.2636936.
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Steganographyis a technique of sending hidden data or secret messages over a public channel so that nobody can detect the presence of the secret messages.Speech signal can be used as a cover signal to hide very sensitive data in it. The proposed approach is criticalin military-warfare conditions, and in the defense related Information Communication. The covert nature is the desirable feature which declines any unauthorized user from getting sensitive information[1]. Steganography is a way of concealing data where secret messages are hidden inside computer files such as images, sound files, video files so that, no one except the sender and the receiver will guess the existence of information in it. Cryptography may also use in steganographywhere the message is first encrypted before it is hidden in another file. Generally, the messages appear like an image, sound or video so that the secret data transfer remains unsuspected.Steganography hides all evidence regarding the existence of communication. A simple technique which involves the embedding of information in the least significant bits of the cover-audio file is known as LSB Encoding Technique. Distortion will be minimum in this technique. The strength of steganography is in hiding the secret message by obscurity, hiding its existence in a non-secret file. The success of this technique depends on the ability to hide the message such that nobody would suspect it, the greatest effort must be to ensure that the message is not visible unless one knows what to look for.
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Mrs., Rohini C., A.Srikanth Mr., Prajwal Praneeth Kumar.R Mr., Basavaraj R.H Mr., and U. Vinay Mr. "Advanced Data Security Using Modulo Operator And Lsb Method." Journal of Scholastic Engineering Science and Management 2, no. 5 (2023): 1–12. https://doi.org/10.5281/zenodo.7896731.
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<strong>Steganography using the Least Significant Bit (LSB) method is a commonly used technique for hiding secret messages within digital media files such as images, audio, and video. The LSB method involves replacing the least significant bit of each pixel or sample value in the cover file with a bit of the secret message. One of the benefits of using the LSB method for steganography is that it is relatively easy to implement, and it allows for a large amount of data to be hidden within a cover file without significantly altering its quality. Additionally, the LSB method can be used for different types of digital media files, including images, audio, and video.</strong>
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Son, Yeongmin, and Jae Wan Park. "Detecting Forged Audio Files Using “Mixed Paste” Command: A Deep Learning Approach Based on Korean Phonemic Features." Sensors 24, no. 6 (2024): 1872. http://dx.doi.org/10.3390/s24061872.
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The ubiquity of smartphones today enables the widespread utilization of voice recording for diverse purposes. Consequently, the submission of voice recordings as digital evidence in legal proceedings has notably increased, alongside a rise in allegations of recording file forgery. This trend highlights the growing significance of audio file authentication. This study aims to develop a deep learning methodology capable of identifying forged files, particularly those altered using “Mixed Paste” commands, a technique not previously addressed. The proposed deep learning framework is a composite model, integrating a convolutional neural network and a long short-term memory model. It is designed based on the extraction of features from spectrograms and sequences of Korean consonant types. The training of this model utilizes an authentic dataset of forged audio recordings created on an iPhone, modified via “Mixed Paste”, and encoded. This hybrid model demonstrates a high accuracy rate of 97.5%. To validate the model’s efficacy, tests were conducted using various manipulated audio files. The findings reveal that the model’s effectiveness is not contingent on the smartphone model or the audio editing software employed. We anticipate that this research will advance the field of audio forensics through a novel hybrid model approach.
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Hakim, Azizhil, Zhya Anggraini, Dilla Sillfani, Renika Ayuni, and Achmad Fauzi. "PENERAPAN SUPER ENKRIPSI HILL CIPHER DAN RSA UNTUK PENGAMANAN DATA FILE AUDIO MP3." Jurnal Sistem Informasi Kaputama (JSIK) 9, no. 1 (2025): 55–64. https://doi.org/10.59697/jsik.v9i1.959.
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Digital data security, especially audio files, is a major issue in the modern era, especially with the increasing risk of hacking and file tapping. To protect audio files such as MP3 format, this study suggests a combination of Hill Cipher and RSA algorithms for the Super Encryption method. Hill Cipher uses a linear matrix to encrypt data at high speed, while RSA uses a symmetric key created by Hill cipher to encrypt. The combination of these two algorithms produces a strong double layer of security to protect audio files. The layered encryption process uses Hill Cipher as the initial stage before using RSA for additional security. This super encryption is very good for maintaining data confidentiality. This study helps develop a digital data security system based on cryptography with a focus on audio files. This method can be used for various things, such as maintaining sensitive voice communications, maintaining important recordings, and creating applications based on audio data. However, further development can be done to improve the scalability and efficiency of this method, especially for large audio data files or data in the form of.
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Shivaranjini, Mahesh Babu K, and Maheswar Reddy K. "Enhanced Recovery of AVI Files Using Recuva." International Research Journal of Innovations in Engineering and Technology 09, Special Issue (2025): 308–14. https://doi.org/10.47001/irjiet/2025.inspire50.
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This research focuses on advancing the data recovery process for fragmented AVI files on NTFS file systems using enhancements to Recuva, a popular recovery tool. Audio Video Interleave (AVI) files often suffer from fragmentation due to their large file size and frequent editing, making recovery challenging. The study emphasizes improvements in three core areas: identifying fragmented file segments, reconstructing incomplete or corrupted metadata, and recovering data in scenarios where portions of the file have been overwritten. By integrating these improvements, new algorithms were developed to analyze file patterns, reconstruct lost metadata, and recover partially overwritten fragments. Rigorous testing demonstrated a significant boost in recovery success rates under diverse fragmentation and overwriting scenarios. These advancements are expected to enhance data recovery solutions for both individual users and organizations handling critical multimedia files.
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Journal, Baghdad Science. "Steganography in Audio Using Wavelet and DES." Baghdad Science Journal 12, no. 2 (2015): 431–36. http://dx.doi.org/10.21123/bsj.12.2.431-436.
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In this paper, method of steganography in Audio is introduced for hiding secret data in audio media file (WAV). Hiding in audio becomes a challenging discipline, since the Human Auditory System is extremely sensitive. The proposed method is to embed the secret text message in frequency domain of audio file. The proposed method contained two stages: the first embedding phase and the second extraction phase. In embedding phase the audio file transformed from time domain to frequency domain using 1-level linear wavelet decomposition technique and only high frequency is used for hiding secreted message. The text message encrypted using Data Encryption Standard (DES) algorithm. Finally; the Least Significant bit (LSB) algorithm used to hide secret message in high frequency. The proposed approach tested in different sizes of audio file and showed the success of hiding according to (PSNR) equation.
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Ali, Rasha H. "Steganography in Audio Using Wavelet and DES." Baghdad Science Journal 12, no. 2 (2015): 431–36. http://dx.doi.org/10.21123/bsj.2015.12.2.431-436.
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In this paper, method of steganography in Audio is introduced for hiding secret data in audio media file (WAV). Hiding in audio becomes a challenging discipline, since the Human Auditory System is extremely sensitive. The proposed method is to embed the secret text message in frequency domain of audio file. The proposed method contained two stages: the first embedding phase and the second extraction phase. In embedding phase the audio file transformed from time domain to frequency domain using 1-level linear wavelet decomposition technique and only high frequency is used for hiding secreted message. The text message encrypted using Data Encryption Standard (DES) algorithm. Finally; the Least Significant bit (LSB) algorithm used to hide secret message in high frequency. The proposed approach tested in different sizes of audio file and showed the success of hiding according to (PSNR) equation.
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M. Nasution, Rezky. "Implementasi Metode Secure Hash Algorithm (SHA-1) Untuk Mendeteksi Orisinalitas File Audio." Bulletin of Computer Science Research 2, no. 3 (2022): 73–84. http://dx.doi.org/10.47065/bulletincsr.v2i3.140.
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Audio file is a means of information from one person to another. Audio files are very vulnerable to fraud, eavesdropping or data theft by irresponsible parties. In order to maintain the security of audio files, this can be done by using cryptographic techniques. Cryptography is one of the data security methods that can be used to maintain data authenticity, data confidentiality, and the authenticity of data transmission. SHA stands for Secure Hash Algorithm is a standard hash function published by NIST (National Institute of Standards and Technology). SHA is published with a digest size of 512 bits. SHA-1 will output 160 bits of the string and the output string is called a message digest. The length of the message digest can range from 160 to 512 bits depending on the algorithm. This study describes the security process for detecting the authenticity of audio files using the SHA-1 method in the form of detection so that confidential audio sent via public telecommunications cannot be changed or modified by unauthorized persons or unauthorized persons. This is done as an effort to minimize acts of fraud, hoaxes, or misuse of audio files.
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Dalhoum, Abdel Latif Abu. "One-Dimensional Audio Scrambling based on Cellular Automata." Modern Applied Science 13, no. 1 (2018): 136. http://dx.doi.org/10.5539/mas.v13n1p136.
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Digital audio scrambling is a process used in audio security applications. Scrambling of audio files breaks the correlation between adjacent samples in order to convert the original audio to an unintelligible format. Scrambling is used to protect the audio against wiretapping and illegal surveillance, in addition to being a step in security algorithms, such as watermarking and encryption algorithms. Cellular automata are models that are discrete in nature and depend on simple and local rules to achieve an interesting overall behavior. Two-dimensional cellular automata were previously proposed as a key generation mechanism to scramble audio files. The mechanism was built upon be researchers in the multimedia security field. This paper explores the use of one-dimensional cellular automata in audio scrambling, which simplifies the process as deploying two-dimensional cellular automata requires changing the dimension of the audio file and the one-dimensional cellular automata does not, additionally, elementary one-dimensional cellular automata requires less parameters to configure. The scrambling degree is used to evaluate the model effectiveness in breaking the correlation of adjacent samples. In the experiments, different parameters are taken into account including the cellular automata class, the iterations needed and the method used to calculate the cells at the boundary. Experiments show that the one-dimensional cellular automata are capable of scrambling the audio file without any dimensional change and the chaotic rules tested give the highest scrambling degree.
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Betsaida Situmorang, Tia. "Perancangan Aplikasi Kompresi File MP3 Dengan Menggunakan Algoritma Lempel Ziv Welch (LZW)." Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan 3, no. 2 (2024): 60–70. http://dx.doi.org/10.55338/justikpen.v3i2.87.
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File berbentuk suara banyak digunakan dalam kegiatan sehari-hari orang dalam mendukung aktifitasnya seperti penggunaan file suara dalam aplikasi pemutar musik baik secara daring maupun luring serta pada aplikasi audio streaming. Pemanfaatan tekniki kompresi suara sangatlah di butuhkan mengingat panjangnya durasi suara dan banyak nya file suara dalam ruang penyimpanan sehingga membutuh ruang penyimpanan yang banyak pula. Teknik kompresi merupakan teknik dimana kapasitas secara fisik file suara dikurangi dengan mengusahakan tidak mengurangi kualitas suara yang terdapat dalam file audio. File suara dengan ekstensi MP3 merupakan ekstensi file suara yang banyak digunakan saat ini, yang memiliki kepanjangan MPEG Audio Layer III. Menangani permasalah ruangan penyimpanan dan sekaligus menghemat penggunaan paket data pada saat melakukan pengaksesan sekaligus transimis file suara, maka diterapkan metode kompresi. Metode kompresi yang digunakan adalah Algoritma Lempel Ziv Welch (LZW). Algoritma Lempel Ziv Welch adalah algoritma kompresi yang menjaga kualitas dan mampu mengidentifkasi dan mengganti pola berulang dalam data. Penelitian ini diharapkan bisa dimanfaatkan untuk pengurangan ukuran data suatu file gambar dan video. Pada tahap awal yang akan dianalisa penulis ialah dengan mencari file Mp3 yang akan dikompres kemudian diubah menjadi nilai hexadecimal melalui aplikasi HxD. Setelah didapat nilai hexadecimal file Mp3 maka akan dilakukan proses kompresi dan diperoleh file hasil kompresi. Hasil kompresi file audio dengan algoritma LZW diperoleh nilai Compression Ratio 16%.
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Situmorang, Tia Betsaida. "Perancangan Aplikasi Kompresi File MP3 Dengan Menggunakan Algoritma Lempel Ziv Welch (LZW)." Jurnal Media Informatika 5, no. 1 (2023): 11–21. http://dx.doi.org/10.55338/jumin.v5i1.2199.
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File berbentuk suara banyak digunakan dalam kegiatan sehari-hari orang dalam mendukung aktifitasnya seperti penggunaan file suara dalam aplikasi pemutar musik baik secara daring maupun luring serta pada aplikasi audio streaming. Pemanfaatan tekniki kompresi suara sangatlah di butuhkan mengingat panjangnya durasi suara dan banyak nya file suara dalam ruang penyimpanan sehingga membutuh ruang penyimpanan yang banyak pula. Teknik kompresi merupakan teknik dimana kapasitas secara fisik file suara dikurangi dengan mengusahakan tidak mengurangi kualitas suara yang terdapat dalam file audio. File suara dengan ekstensi MP3 merupakan ekstensi file suara yang banyak digunakan saat ini, yang memiliki kepanjangan MPEG Audio Layer III. Menangani permasalah ruangan penyimpanan dan sekaligus menghemat penggunaan paket data pada saat melakukan pengaksesan sekaligus transimis file suara, maka diterapkan metode kompresi. Metode kompresi yang digunakan adalah Algoritma Lempel Ziv Welch (LZW). Algoritma Lempel Ziv Welch adalah algoritma kompresi yang menjaga kualitas dan mampu mengidentifkasi dan mengganti pola berulang dalam data. Penelitian ini diharapkan bisa dimanfaatkan untuk pengurangan ukuran data suatu file gambar dan video. Pada tahap awal yang akan dianalisa penulis ialah dengan mencari file Mp3 yang akan dikompres kemudian diubah menjadi nilai hexadecimal melalui aplikasi HxD. Setelah didapat nilai hexadecimal file Mp3 maka akan dilakukan proses kompresi dan diperoleh file hasil kompresi. Hasil kompresi file audio dengan algoritma LZW diperoleh nilai Compression Ratio 16%.
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Kurniawan, Ichwan, Much Rifqi Maulana, and Arochman Arochman. "ANALISIS MEMORY PERFORM BROWSER PADA PENGGUNAAN AUDIO WEB PROGRAMMING." IC-Tech 19, no. 1 (2024): 34–38. http://dx.doi.org/10.47775/ictech.v19i1.286.
Full textAbstract:
Sebuah game dikembangkan dengan melibatkan banyak asset, salah satunya pemanfaatan audio dalam pengembangan game. Pembuatan efek suara dalam sebuah game dilakukan dengan merekamnya secara terpisah dengan proses pengembangan game, hal ini dapat memakan waktu dan sangat tidak efisien untuk diselesaikan. Oleh karena itu, pembuatan efek suara dinamis diperlukan untuk mempersingkat waktu produksi dan menyelesaikan game lebih cepat. Dengan mengkombinasikan audio dengan pemrograman memungkinkan penyajian audio dalam sebuah game dapat disajikan secara dinamis, hal ini dikarenakan dengan algoritma pemrograman audio dapat dikontrol berdasarakan waktu, kejadian dan prilaku tertentu dalam sebuah game. Hasil dari Analisis perform memory browser dalam menjalankan file notasi suara mupun kode frekuensi notasi suara adalah jika dilihat perbadingan rata-rata browser google chorme pada saat penggunaan array memiliki rata-rata 405,92 kb untuk browser google chorme, hal ini menujukkan bahwa terdapat perbedaan yang sanyat besar terhadap penggunaan memory array dalam menjalankan file dan frekuensi. Penggunaan file audio menunjukkan bahwa dalam file load audio membutuhkan memory array yang cukup besar, sedangkan dalam penggunaan frekuensi audio menunjukkan bawah frekuensi load audio tidak membutuhkan memory array yang tidak begitu besar
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Asmara, I. Wayan Dana, Made Windu Antara Kesiman, and Ketut Agustini. "Pengembangan Aplikasi Kriptografi File Audio Dengan Algoritma Data Encryption Standard (DES)." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 1, no. 2 (2012): 130. http://dx.doi.org/10.23887/janapati.v1i2.9827.
Full textAbstract:
Keamanan data merupakan salah satu isu penting di era teknologi informasi dan komunikasi. Salah satu alternatif untuk menjaga keamanan data adalah dengan mengembangkan aplikasi yang menerapkan algoritma kriptografi. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah aplikasi kriptografi yang mengimplementasikan algoritma Data Encryption Standard (DES). Perancangan sistem aplikasi ini menggunakan UML (Unified Modeling Language) dengan enkripsi dan dekripsi file WAV sebagai rancangan utama. Proses enkripsi dan dekripsi ini menggunakan algoritma DES. Algoritma DES merupakan algoritma kriptografi simetri yaitu algoritma kriptografi yang menggunakan kunci yang sama untuk enkripsi dan dekripsi. Algortima DES bekerja pada blok data 64 bit dan menggunakan panjang kunci 64 bit. Proses-proses yang terdapat pada algoritma DES meliputi pembangkitan kunci internal, permutasi awal, enchipering, dan permutasi akhir. Implementasi Algoritma DES pada aplikasi kriptografi file audio menghasilkan sebuah perangkat lunak yang disebut dengan AudioEncryptor. Berdasarkan hasil pengujian perangkat lunak diperoleh bahwa AudioEncryptor mampu mengenkripsi file audio dengan baik. Suara yang dikeluarkan file audio terenkripsi tidak sama dengan suara sebenarnya, sehingga kerahasiaan informasi yang terkandung dalam file audio yang sudah dienkripsi sangat aman. Selain enkripsi dan dekripsi, AudioEncryptor juga dilengkapi dengan fasilitas record dan play audio yaitu fasilitas untuk merekam dan memutar file audio. Perangkat Lunak AudioEncryptor sendiri dikembangkan dengan menggunakan bahasa pemrograman Java yaitu pada lingkungan Java 2 Standard Edition (J2SE).
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Ma, Xiao Xing, and Di Wu. "Content-Based Multimedia Files Search Technology." Applied Mechanics and Materials 644-650 (September 2014): 1895–98. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1895.
Full textAbstract:
At present, digital multimedia files containing text, images, audio and video are four main formats. In the search for multimedia files, because dissemination of images, audio and video files on the Internet, is lack of uniform standards, the same content will be used to describe different keywords. And it contains the richer content, the larger the amount of file data, as thus the traditional theme keyword search technology cannot meet the needs of the way of images and audio and video files. Text search technology is the most mature and widely used. Content-based search engine of search technology is an important topic in the development of digital media files on Internet search.
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Venkateshaiah, Namitha Mangikuppe, and Manjula Govinakovi Rudrappa. "High-capacity steganography through audio fusion and fission." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 1 (2024): 643–52. https://doi.org/10.11591/ijeecs.v33.i1.pp643-652.
Full textAbstract:
Information security is required for two reasons, either to conceal the information completely or to prevent the misuse of the information by adding watermarks or metadata. Audio steganography uses audio signals to hide secret information. In the proposed audio steganography technique, cover audio files and secret audio files are transformed from time domain to wavelet domain using discrete wavelet transform, the secret audio file is transformed in two levels, leading to secure and high-capacity data hiding. 1% of the 2-level compressed secret is fused to 99% of the 1-level compressed cover. “Peak signal to noise ratio and mean squared error, Pearson’s correlation coefficient, spearman’s correlation coefficient, perceptual evaluation of speech quality and short-time objective intelligibility” are considered to assess the similarity of cover audio and stego audio and similarity of secret audio embedded, and secret audio retrieved. Results show that the stego audio signal is perceptually indistinguishable from the cover audio signal. The approach also passed the robustness test.
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Rangkuti, Ira Sarifah, and Edward Robinson Siagian. "Implementasi Penyembunyian Pesan Pada Audio Dengan Metode Bit-Plane Complexity Segmentation (BPCS)." JURIKOM (Jurnal Riset Komputer) 7, no. 2 (2020): 285. http://dx.doi.org/10.30865/jurikom.v7i2.2088.
Full textAbstract:
Cryptography is the science used to maintain the confidentiality of messages, by scrambling messages that are illegible. However, the results of randomization can raise suspicions that confidential communications are being carried out. Steganography can be used to overcome these problems. The trick is the message is inserted in the audio file by the Bit-Plane method. then add a message behind the file. To prevent messages from being read, the message is encrypted first with the Bit-Plane method before inserting. Application design results can be used to hide secret messages that have been encrypted with the Bit-Plane method to audio files, so as to avoid suspicion of confidential communications
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Shimoda, Akihiro, Yue Li, Hana Hayashi, and Naoki Kondo. "Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model." PLOS ONE 16, no. 7 (2021): e0253988. http://dx.doi.org/10.1371/journal.pone.0253988.
Full textAbstract:
Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.