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Relevant bibliographies by topics / Digital audio : Signal processing

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Academic literature on the topic 'Digital audio : Signal processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 March 2023

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Journal articles on the topic "Digital audio : Signal processing"

1

Borawake, Prof Dr M. P. "Audio Signal Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1495–96. http://dx.doi.org/10.22214/ijraset.2022.44063.

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Abstract: Audio Signal Processing is also known as Digital Analog Conversion (DAC). Sound waves are the most common example of longitudinal waves. The speed of sound waves is a particular medium depends on the properties of that temperature and the medium. Sound waves travel through air when the air elements vibrate to produce changes in pressure and density along the direction of the wave’s motion. It transforms the Analog Signal into Digital Signals, and then converted Digital Signals is sent to the Devices. Which can be used in Various things., Such as audio signal, RADAR, speed processing, voice recognition, entertainment industry, and to find defected in machines using audio signals or frequencies. The signals pay important role in our day-to-day communication, perception of environment, and entertainment. A joint time-frequency (TF) approach would be better choice to effectively process this signal. The theory of signal processing and its application to audio was largely developed at Bell Labs in the mid-20th century. Claude Shannon and Harry Nyquist’s early work on communication theory and pulse-code modulation (PCM) laid the foundations for the field.

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Snyder, James H., and John Strawn. "Digital Audio Signal Processing: An Anthology." Computer Music Journal 10, no. 2 (1986): 77. http://dx.doi.org/10.2307/3679489.

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Zhang, Jing Bo, Xiao Feng Wang, and Shu Fang Zhang. "Audio Signal Processing Based on FPGA." Advanced Materials Research 1049-1050 (October 2014): 1759–64. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1759.

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This paper presents a system of audio signal processing based on FPGA,the system uses audio codec chip LM4550 to A/D transform and D/A transform the input analog audio signal and output digital audio signal.Using FPGA as the high speed signal processor to realize volume adjustment and audio effect control,so it can output different style music.Meantime, the system designs a FFT computing module and control system of VGA display interface,to compute the digital audio signal which is A/D transformed,and real-time display the frequency spectrum of audio signal on VGA.

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Hongoh, Tsunehiko, and Hirotoshi Yamamoto. "Digital signal processing device and audio signal reproduction device." Journal of the Acoustical Society of America 120, no. 5 (2006): 2401. http://dx.doi.org/10.1121/1.2395106.

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Nasrulloh, Mohammad Dicky. "Designing a Digital Filter Based Crossover Audio System Using STM32L4." Jurnal Jartel: Jurnal Jaringan Telekomunikasi 9, no. 4 (December 25, 2019): 13–18. http://dx.doi.org/10.33795/jartel.v9i4.141.

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Analog telecommunication system equipment is now starting to develop and be replaced with digital telecommunication systems, one of them is in the audio signal processing. The focus of audio processing is audio crossover. Audio crossover in development there are still many who use analog systems. This analog system has disadvantages when adjusting the sound balance because it still uses analog filters to balance it. It is necessary to develop a technology that aims to create a digital-based crossover audio system using the STM32L4, so that by using this digital-based signal processing it is able to adjust the sound more specifically than the signal processing used analog based. This digital filter uses the Finite Impulse Response (FIR) method. Testing audio crossover using STM32L4 produces a digital-based crossover audio system design using a STM32L4 microcontroller with a voltage of 3.3V as power supply, mp3 player as sound input device, FIR filter as digital filter processing, LM386 as sound amplifier and speaker as sound output for crossover audio on rangelow frequency (200Hz to 4000Hz), high (2200Hz to 6000Hz), medium (200Hz to 4000Hz).

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Han, Xiuqin. "Acquisition and its Basic Processing Technology of Multimedia Vocal Signal." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 08 (November 12, 2019): 2058009. http://dx.doi.org/10.1142/s0218001420580094.

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This paper briefly studies the method of collecting audio signals and the method of adding noise to audio signals. It comprehensively applies various basic knowledge of digital signal processing, and then performs spectrum analysis on noise-free frequency signals and spectral analysis of noise-added frequency signals, and filtering processing. Through theoretical derivation, the corresponding conclusions are drawn, and then MATLAB is used as a programming tool to carry out computer implementation to verify the conclusions derived. In the research process, the filter processing was completed by designing the IIR digital filter and the FIR digital filter, and MATLAB was used to draw the graphics and calculate and simulate some data in the whole design.

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Tsai, S. E., and S. M. Yang. "An Effective Watermarking Method Based on Energy Averaging in Audio Signals." Mathematical Problems in Engineering 2018 (June 25, 2018): 1–8. http://dx.doi.org/10.1155/2018/6420314.

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Methods based on discrete cosine transform (DCT) have been proposed for digital watermarking of audio signals; however, the watermark is often vulnerable to data compression and signal processing. This paper presents an effective audio watermarking method by energy averaging of DCT coefficients such that an audio signal with watermark is robust to data processing. The method is to divide an audio signal into segments by three parameters defining the segment length, the segment sequence of watermark location, and the frequency range of DCT coefficients for watermark location. An error correcting code is also integrated to improve audio signal quality after watermarking. Experimental results show that the method is robust to data compression and many other kinds of signal processing. No original signal is required for decoding the watermark. Comparison of watermarking performance with a recent work validates that the watermarking method has better audio quality and higher robustness.

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Lim, Dukhwan. "Digital Signal Processing in Audiology." Audiology and Speech Research 4, no. 1 (June 30, 2008): 5–10. http://dx.doi.org/10.21848/audiol.2008.4.1.5.

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Mercs, Laura, and Paul M. Embree. "Audio noise reduction system implemented through digital signal processing." Journal of the Acoustical Society of America 108, no. 2 (2000): 474. http://dx.doi.org/10.1121/1.429557.

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Liu, Bin, and Yan Ren. "A design of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012059. http://dx.doi.org/10.1088/1742-6596/2113/1/012059.

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Abstract This paper introduces a design scheme of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction. The green light beams from multiple high-power lasers simulate harp strings, use photoresistors as the signal receiving end, and use a signal conditioning system composed of analog circuits and LM393 comparators to collect and adjust the resistance signal of the laser sensor[1], and finally it is adjusted to a level signal that can be recognized by the CPU. After receiving the signal, the CPU core board analyzes the string signal, and sends control commands to the audio processing system through the industrial bus according to the analyzed digital signal. After receiving the control command, the audio processing system uses the audio signal reconstruction technology composed of multi-dimensional wavelet packets, deep learning and other algorithms to simulate the audio signals of various string music, so as to achieve the purposes of using the lasers as virtual strings and imitating musical instruments for musical performance.[2]

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Dissertations / Theses on the topic "Digital audio : Signal processing"

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Bland, Denise. "Alias-free signal processing of nonuniformly sampled signals." Thesis, University of Westminster, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.322992.

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Lindström, Fredric. "Digital signal processing methods and algorithms for audio conferencing systems /." Karlskrona : Department of Signal Processing, School of Engineering, Blekinge Institute of Technology, 2007. http://www.bth.se/fou/Forskinfo.nsf/allfirst2/9cc008f2fa400e82c12572bb00331533?OpenDocument.

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Balraj, Navaneethakrishnan. "AUTOMATED ACCIDENT DETECTION IN INTERSECTIONS VIA DIGITAL AUDIO SIGNAL PROCESSING." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-10212003-102715/.

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The aim of this thesis is to design a system for automated accident detection in intersections. The input to the system is a three-second audio signal. The system can be operated in two modes: two-class and multi-class. The output of the two-class system is a label of ?crash? or ?non-crash?. In the multi-class system, the output is the label of ?crash? or various non-crash incidents including ?pile drive?, ?brake?, and ?normal-traffic? sounds. The system designed has three main steps in processing the input audio signal. They are: feature extraction, feature optimization and classification. Five different methods of feature extraction are investigated and compared; they are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstrum transform and Mel frequency cepstral transform. Linear discriminant analysis (LDA) is used to optimize the features obtained in the feature extraction stage by linearly combining the features using different weights. Three types of statistical classifiers are investigated and compared: the nearest neighbor, nearest mean, and maximum likelihood methods. Data collected from Jackson, MS and Starkville, MS and the crash signals obtained from Texas Transportation Institute crash test facility are used to train and test the designed system. The results showed that the wavelet based feature extraction method with LDA and maximum likelihood classifier is the optimum design. This wavelet-based system is computationally inexpensive compared to other methods. The system produced classification accuracies of 95% to 100% when the input signal has a signal-to-noise-ratio of at least 0 decibels. These results show that the system is capable of effectively classifying ?crash? or ?non-crash? on a given input audio signal.

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Amphlett, Robert W. "Multiprocessor techniques for high quality digital audio." Thesis, University of Bristol, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337273.

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Ekström, Mattias. "Acoustic feedback suppression in audio mixer for PA applications." Thesis, Umeå universitet, Institutionen för fysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136841.

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When a speaker is addressing an audience, a PA system consisting of a microphone and a loudspeaker is often used. If the microphone picks up too much of the loudspeaker energy, acoustic feedback in the form of an unwanted characteristic howling can occur. Limes Audio is a software company that specializes in improving sound quality in digital communications, mainly conference telephony, and has developed a reference product, the Magneto mixer, to demonstrate the capability of their software TrueVoice. The company now wishes to expand the field of usage for the Magneto mixer to enable it to work as a microphone mixer in PA scenarios, and for this, a feedback suppression feature is needed. This master’s thesis aims at surveying the market and the literature in the field and specifying the requirements for a feedback suppression feature. Three methods for suppressing howling feedback are evaluated through simulations and compared in terms of maximum stable gain (MSG) and subjective listening experience. The method that performed the best based on these criteria was acoustic feedback cancellation with a 5 Hz frequency shift on the loudspeaker signal. This method makes use of an adaptive filter to model the acoustic feedback path and to remove the feedback component from the microphone signal. In the simulations, the method was able to increase the stable gain by approximately 10 dB while maintaining a good sound quality.
När en talare talar för en publik används ofta ett PA system bestående av en mikrofon och en högtalare. Om mikrofonen tar upp för mycket av ljudet från högtalaren finns en överhängande risk för akustisk rundgång i form av ett karaktäristiskt oönskat tjut. Limes Audio är ett företag som utvecklar mjukvara för att förbättra ljudkvaliten i digital kommunikation, främst inom konferenstelefoni. De har utvecklat en demonstrationsprodukt, Magnetomixern, som kan användas som en konferenstelefon för att demonstrera deras programvara TrueVoice. Företaget önskar nu utveckla Magnetomixern till att även fungera som en ljudmixer för PA-scenarion, eller konferenstelefoni där intern ljudförstärkning i rummet behövs, och för detta behövs en funktion för att ta bort eventuell rundgång. Detta examensarbete har som mål att lägga grunden för en sådan funktion i Magnetomixern genom att undersöka marknaden och litteraturen på området. Tre metoder för att eliminera rundgång utvärderas i simuleringar och jämförs beträffande maximal stabil förstärkning (MSG) och subjektiv ljudkvalitet. Metoden ”Acoustic feedback cancellation” tillsammans med ett 5 Hz frekvensskifte på högtalarsignalen gav högst MSG och bäst ljudkvalitet. Metoden använder ett adaptivt filter för att approximera den akustiska återkopplingsvägen mellan högtalare och mikrofon samt tar bort rundgångskomponenter från mikrofonsignalen. I simuleringarna kunde metoden öka den maximala stabila förstärkningen med upp till 10 dB medan en god ljudkvalitet på talet bibehölls.

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Chiu, Leung Kin. "Efficient audio signal processing for embedded systems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44775.

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We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.

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Lipstreu, William F. "Digital Signal Processing Laboratory Using Real-Time Implementations of Audio Applications." Cleveland, Ohio : Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1240836810.

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Lanciani, Christopher A. "Compressed-domain processing of MPEG audio signals." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13760.

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Trinkaus, Trevor R. "Perceptual coding of audio and diverse speech signals." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/13883.

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Vemulapalli, Smita. "Audio-video based handwritten mathematical content recognition." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45958.

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Recognizing handwritten mathematical content is a challenging problem, and more so when such content appears in classroom videos. However, given the fact that in such videos the handwritten text and the accompanying audio refer to the same content, a combination of video and audio based recognizer has the potential to significantly improve the content recognition accuracy. This dissertation, using a combination of video and audio based recognizers, focuses on improving the recognition accuracy associated with handwritten mathematical content in such videos. Our approach makes use of a video recognizer as the primary recognizer and a multi-stage assembly, developed as part of this research, is used to facilitate effective combination with an audio recognizer. Specifically, we address the following challenges related to audio-video based handwritten mathematical content recognition: (1) Video Preprocessing - generates a timestamped sequence of segmented characters from the classroom video in the face of occlusions and shadows caused by the instructor, (2) Ambiguity Detection - determines the subset of input characters that may have been incorrectly recognized by the video based recognizer and forwards this subset for disambiguation, (3) A/V Synchronization - establishes correspondence between the handwritten character and the spoken content, (4) A/V Combination - combines the synchronized outputs from the video and audio based recognizers and generates the final recognized character, and (5) Grammar Assisted A/V Based Mathematical Content Recognition - utilizes a base mathematical speech grammar for both character and structure disambiguation. Experiments conducted using videos recorded in a classroom-like environment demonstrate the significant improvements in recognition accuracy that can be achieved using our techniques.

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Books on the topic "Digital audio : Signal processing"

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Digital audio signal processing. 2nd ed. Chichester, West Sussex, England: Wiley, 2008.

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1950-, Strawn John, and Moore F. Richard, eds. Digital audio signal processing: An anthology. Madison, Wis: A-R Editions, 1985.

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1950-, Strawn John, and Moore F. Richard, eds. Digital audio signal processing: An anthology. Los Altos, Calif: W. Kaufmann, 1985.

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Li, Francis F., and Trevor J. Cox. Digital Signal Processing in Audio and Acoustical Engineering. Edited by Francis F. Li and Trevor J. Cox. Boca Raton : Taylor & Francis, a CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa, plc, [2019]: CRC Press, 2019. http://dx.doi.org/10.1201/9781315117881.

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Kahrs, Mark, and Karlheinz Brandenburg, eds. Applications of Digital Signal Processing to Audio and Acoustics. Boston: Kluwer Academic Publishers, 2002. http://dx.doi.org/10.1007/b117882.

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Mark, Kahrs, and Brandenburg Karlheinz 1954-, eds. Applications of digital signal processing to audio and acoustics. Boston: Kluwer, 1998.

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Inc, NetLibrary, ed. Applications of digital signal processing to audio and acoustics. New York: Kluwer Academic, 2002.

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Distefano, Eugenia M. A synchronization mechanism for audio digital signal processing applications. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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Udo, Zölzer, and Amatriain Xavier, eds. DAFX: Digital audio effects. Chichester: Wiley, 2002.

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Madisetti, V. Video, speech, and audio signal processing and associated standards. 2nd ed. Boca Raton, FL: CRC Press, 2010.

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Book chapters on the topic "Digital audio : Signal processing"

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Godsill, Simon J., and Peter J. W. Rayner. "Digital Signal Processing." In Digital Audio Restoration, 15–38. London: Springer London, 1998. http://dx.doi.org/10.1007/978-1-4471-1561-8_2.

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Miyara, Federico. "Digital Audio Editing." In Modern Acoustics and Signal Processing, 167–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55871-4_4.

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Uncini, Aurelio. "Digital Audio Effects." In Springer Topics in Signal Processing, 483–563. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14228-4_7.

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Strobel, Norbert, Sascha Spors, and Rudolf Rabenstein. "Joint Audio-Video Signal Processing for Object Localization and Tracking." In Digital Signal Processing, 203–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04619-7_10.

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Mandal, Mrinal Kr. "Digital Audio Processing." In Multimedia Signals and Systems, 239–56. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0265-4_10.

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Uncini, Aurelio. "Digital Filters for Audio Applications." In Springer Topics in Signal Processing, 177–230. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14228-4_3.

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Christensen, Mads G. "Digital Audio Signals." In Introduction to Audio Processing, 31–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11781-8_3.

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Zieliński, Tomasz P. "Audio Compression." In Starting Digital Signal Processing in Telecommunication Engineering, 405–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49256-4_15.

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Rumsey, Francis. "Digital Audio Recording Formats and Editing Principles." In Handbook of Signal Processing in Acoustics, 703–29. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_36.

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Sonoda, Kotaro, and Shu Noguchi. "Tally Based Digital Audio Watermarking." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 399–405. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63856-0_48.

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Conference papers on the topic "Digital audio : Signal processing"

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Sandler, M. "Digital signal processing for audio." In IEE Tutorial Seminar on DSP: Theory, Applications and Implementation. IEE, 1996. http://dx.doi.org/10.1049/ic:19960295.

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Bächer, Dieter. "Audio System Using Digital Signal Processing." In SAE International Congress and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1986. http://dx.doi.org/10.4271/860118.

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Perez, Mauricio, Rodolfo Coelho De Souza, and Regis Rossi Alves Faria. "Digital Design of Audio Signal Processing Using Time Delay." In Simpósio Brasileiro de Computação Musical. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbcm.2019.10449.

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This poster describes the design in PureData of some audio signals processes in real time like delay, echo, reverb, chorus, flanger e phaser. We analyze the technical characteristics of each process and the psychoacoustic effects produced by them in human perception and audio applications. A deeper comprehension of the consequences of sound processes based on delay lines helps the decision-making in professional audio applications such as the audio recording, mixing, besides music composition that employs sound effects in preprocessed or real-time.

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Stankovic, Isidora, Milos Dakovic, and Cornel Ioana. "Time-frequency signal reconstruction of nonsparse audio signals." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096044.

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Dutta, Malay Kishore, Phalguni Gupta, and Vinay K. Pathak. "Perceptible audio watermarking for digital right management control." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397484.

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Chiu, Leung Kin, David V. Anderson, and Benjamin Hoomes. "Audio output enhancement algorithms for piezoelectric loudspeakers." In 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE). IEEE, 2011. http://dx.doi.org/10.1109/dsp-spe.2011.5739232.

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Zhang, Linghua, Zhiyuan Huang, and Xulai Cao. "A digital audio watermarking algorithm based on block encoding." In Signal Processing (WCSP 2011). IEEE, 2011. http://dx.doi.org/10.1109/wcsp.2011.6096942.

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RuiMin Hu, Yong Zhang, and Haojun Ai. "Digital audio compression technology and AVS audio standard research." In 2005 International Symposium on Intelligent Signal Processing and Communication Systems. IEEE, 2005. http://dx.doi.org/10.1109/ispacs.2005.1595520.

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Chung, Myoung-bum, and Il-ju Ko. "Representative melodies retrieval using digital signal processing of audio." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253610.

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Luo, Da, Weiqi Luo, Rui Yang, and Jiwu Huang. "Compression history identification for digital audio signal." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288233.

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Reports on the topic "Digital audio : Signal processing"

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Thomas, J. B., and K. Steiglitz. Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1988. http://dx.doi.org/10.21236/ada203744.

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Roberts, Richard A. VLSI Implementations for Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1987. http://dx.doi.org/10.21236/ada189612.

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Chung, Y., L. Emery, and J. Kirchman. Digital signal processing for beam position feedback. Office of Scientific and Technical Information (OSTI), April 1992. http://dx.doi.org/10.2172/90669.

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Biglieri, Ezio, and Michele Elia. Applications of Signal Processing in Digital Communications. Fort Belvoir, VA: Defense Technical Information Center, January 1987. http://dx.doi.org/10.21236/ada190420.

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Elia, Michele. Applications of Signal Processing in Digital Communications. Fort Belvoir, VA: Defense Technical Information Center, November 1987. http://dx.doi.org/10.21236/ada190422.

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Willson, Jr, and Alan N. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada277617.

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Willson, Jr, and Alan N. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada286483.

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Willson, Alan N., and Jr. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250365.

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Willson, Jr, and Alan N. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada256654.

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Willson, Jr, and Alan N. VLSI for High-Speed Digital Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada260754.

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