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Journal articles on the topic 'Network Music Performance'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 January 2025

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1

Kurtisi, Zefir, Xiaoyuan Gu, and Lars Wolf. "Enabling network-centric music performance in wide-area networks." Communications of the ACM 49, no. 11 (2006): 52–54. http://dx.doi.org/10.1145/1167838.1167862.

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2

Xiaoyuan Gu, M. Dick, Z. Kurtisi, U. Noyer, and L. Wolf. "Network-centric music performance: practice and experiments." IEEE Communications Magazine 43, no. 6 (2005): 86–93. http://dx.doi.org/10.1109/mcom.2005.1452835.

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3

Zhao, Jing. "Application of Wireless Sensor Network Technology in Multipoint Control in Music Performance Management System." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–11. http://dx.doi.org/10.1155/2022/2783944.

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Keeping in view the overwhelming characteristics of the wireless sensor networks, these networks are constantly being utilized in numerous domains such as industry, healthcare, and music. Aiming at the problem that there is a single-point and multipoint control in the music performance management system, this paper adopts the wireless sensor network multipoint control technology to realize the control of the music performance management system. The system uses TI’s CC2430 chip to design the hardware circuit, uses the TinyOS operating system as the software platform of the system, and designs t

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4

Fields, Kenneth. "Syneme: Live." Organised Sound 17, no. 1 (2012): 86–95. http://dx.doi.org/10.1017/s1355771811000549.

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Network music foregrounds the materials and processes of communication and in so doing repositions the acousmatic and other strata of electroacoustic music practice. The type of network music considered in this paper, at base defines a member of its category as music which undergoes an electrical-optical conversion, referring to its transport over fibre-optic research network backbones. A more compelling motivation for us is the realisation that network music entails the exploration of disjunct chronotopic frames (stated less poetically as ‘latency in the network’) using probes of sonic materi

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5

Li, Jing. "Transformation of Nonmultiple Cluster Music Cyclic Shift Topology to Music Performance Style." Complexity 2021 (April 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/5590503.

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Music is an abstract art form that uses sound as its means of expression. It has deeply affected our lives. This paper proposes a method for extracting segment features from nonmultiple cluster music files. We divide each piece of music into multiple segments and extract the features of each segment. The specific process includes nonmultiple cluster music file note extraction, main melody extraction, segment division, and segment feature extraction. The segment feature is extracted from a segment of a piece of music, contains the main melody and accompaniment information of the segment, and ca

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Renwick, Robin. "The Relation of Nodalism to Network Music Performance." International Journal of New Media, Technology and the Arts 11, no. 2 (2016): 21–28. http://dx.doi.org/10.18848/2326-9987/cgp/v11i02/21-28.

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Fan, Qiaozhen. "The Application of Minority Music Style Recognition Based on Deep Convolution Loop Neural Network." Wireless Communications and Mobile Computing 2022 (March 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/4556135.

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In recent years, with the development of Internet and digital audio technology, music information retrieval has gradually become a research hotspot. Due to the rise of deep learning and machine learning in recent years, as well as the rapid improvement of computer software and hardware performance, it has laid a good foundation for identifying different genres of music. Among them, the application of minority music style recognition is also an important research direction. At present, the application performance of minority music style recognition based on deep convolution loop neural network

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De Fretes, Daniel, and Nensi Listiowati. "Pertunjukan Musik dalam Perspektif Ekomusikologi." PROMUSIKA 8, no. 2 (2021): 109–22. http://dx.doi.org/10.24821/promusika.v8i2.4636.

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Artikel ini bertujuan untuk menelaah pertunjukan musik dari perspektif ekomusikologi sebagai refleksi dari pergeseran pertunjukan yang terjadi selama masa pandemi covid 19. Tatanan baru di era pandemi mengubah kodrat pertunjukan dari alam nyata ke jagat maya. Ekomusikologi adalah persinggungan diantara kajian musik dan kajian ekologi yang mengelaborasi bidang kajian musik, budaya, dan lingkungan secara multiperspektif. Studi kasus dalam kajian ini adalah konser serenade bunga bangsa di Auditorium Driyarkara Yogyakarta. Penelitian ini menggunakan kerangka analisis ekomusikologi pertunjukan musi

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Zhang, Kedong. "Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network." Wireless Communications and Mobile Computing 2021 (September 4, 2021): 1–7. http://dx.doi.org/10.1155/2021/9298654.

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The music style classification technology can add style tags to music based on the content. When it comes to researching and implementing aspects like efficient organization, recruitment, and music resource recommendations, it is critical. Traditional music style classification methods use a wide range of acoustic characteristics. The design of characteristics necessitates musical knowledge and the characteristics of various classification tasks are not always consistent. The rapid development of neural networks and big data technology has provided a new way to better solve the problem of musi

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Bartlette, Christopher, Dave Headlam, Mark Bocko, and Gordana Velikic. "Effect of Network Latency on Interactive Musical Performance." Music Perception 24, no. 1 (2006): 49–62. http://dx.doi.org/10.1525/mp.2006.24.1.49.

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We investigate the effects of different levels of delay (or latency) on the coordination, pace and timing regularity of musicians who are in remote locationsâ&#x80;&#x94;a situation encountered in an interactive network performance. Two pairs of musicians performed two Mozart duets while isolated visually and connected through microphones and headphones. Different levels of latency (0, 20, 40, 50, 80, 100, 120, 150, and 200 ms) were introduced into the performing environment (musicians heard themselves in real time and only the other part delayed); the musicians performed the duets und

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Luo, Wanshu, and Bin Ning. "Toward Piano Teaching Evaluation Based on Neural Network." Scientific Programming 2022 (January 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/6328768.

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With the rise of piano teaching in recent years, many people participated in the team of learning steel playing. However, expensive piano teaching fees and its unique one-to-one teaching model have caused piano education resources to be very short, so learning piano performance has become a very extravagant event. The factors affecting music performance are varying, and there are many types of their evaluation such as rhythm, expressiveness, music, and style grasp. The computer is used to simulate this evaluation process to essentially identify the mathematical relationship between factors aff

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Millward, Peter, Paul Widdop, and Michael Halpin. "A ‘Different Class’? Homophily and Heterophily in the Social Class Networks of Britpop." Cultural Sociology 11, no. 3 (2017): 318–36. http://dx.doi.org/10.1177/1749975517712045.

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Social network analysis is increasingly recognised as a useful way to explore music scenes. In this article we examine the individuals who were the cultural workforce that comprised the ‘Britpop’ music scene of the 1990s. The focus of our analysis is homophily and heterophily to determine whether the clusters of friendships and working relationships of those who were ‘best connected’ in the scene were patterned by original social class position. We find that Britpop’s ‘whole network’ is heterophilic but that its ‘sub-networks’ are more likely to be social class homophilic. The sub-networks tha

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Gong, Xiaohui. "Research on Discrete Dynamic System Modeling of Vocal Performance Teaching Platform Based on Big Data Environment." Discrete Dynamics in Nature and Society 2022 (February 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/5111896.

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The traditional teaching model of national vocal music in colleges and universities has some problems, such as low quality of teaching, poor diversity of teaching, and low interest of students. Based on this, this study studies the innovation of the teaching model of national vocal music in colleges and universities based on the deep recurrent neural network algorithm and designs the teaching quality evaluation model based on the deep recurrent neural network algorithm. The collection of data and information is realized from the aspects of students’ class state, vocal music examination results

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Van Nort, Doug. "Creating systems for collaborative network‐based digital music performance." Journal of the Acoustical Society of America 124, no. 4 (2008): 2489. http://dx.doi.org/10.1121/1.4782782.

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15

Prashant Krishnan, V., S. Rajarajeswari, Venkat Krishnamohan, Vivek Chandra Sheel, and R. Deepak. "Music Generation Using Deep Learning Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3983–87. http://dx.doi.org/10.1166/jctn.2020.9003.

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This paper primarily aims to compare two deep learning techniques in the task of learning musical styles and generating novel musical content. Long Short Term Memory (LSTM), a supervised learning algorithm is used, which is a variation of the Recurrent Neural Network (RNN), frequently used for sequential data. Another technique explored is Generative Adversarial Networks (GAN), an unsupervised approach which is used to learn a distribution of a particular style, and novelly combine components to create sequences. The representation of data from the MIDI files as chord and note embedding are es

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Ding, Yang, Hongzheng Zhang, Wanmacairang Huang, Xiaoxiong Zhou, and Zhihan Shi. "Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture." Sensors 24, no. 21 (2024): 7021. http://dx.doi.org/10.3390/s24217021.

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In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features. Extensive experiments were conducted on the GTZAN dataset, comparing the proposed ECAS-CNN with traditional convolutional neural networks.

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17

Lin, Mengqian, and Rui Zhao. "A Study of Piano-Assisted Automated Accompaniment System Based on Heuristic Dynamic Planning." Computational Intelligence and Neuroscience 2022 (May 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/4999447.

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In this paper, a piano-assisted automated accompaniment system is designed and applied to a practical process using a heuristic dynamic planning approach. In this paper, we aim at the generation of piano vocal weaves in accompaniment from the perspective of assisting pop song writing, build an accompaniment piano generation tool through a set of systematic algorithm design and programming, and realize the generation of recognizable and numerous weaving styles within a controlled range under the same system. The mainstream music detection neural network approaches usually convert the problem in

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18

Patston, Lucy L. M., and Lynette J. Tippett. "The Effect of Background Music on Cognitive Performance in Musicians and Nonmusicians." Music Perception 29, no. 2 (2011): 173–83. http://dx.doi.org/10.1525/mp.2011.29.2.173.

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there is debate about the extent of overlap between music and language processing in the brain and whether these processes are functionally independent in expert musicians. A language comprehension task and a visuospatial search task were administered to 36 expert musicians and 36 matched nonmusicians in conditions of silence and piano music played correctly and incorrectly. Musicians performed more poorly on the language comprehension task in the presence of background music compared to silence, but there was no effect of background music on the musicians' performance on the visuospatial task

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19

Ben, Lide. "Breaking the Gap between Musicology and Music Performance An Analysis of the Study of Chinese Music Performance Practice." Communications in Humanities Research 3, no. 1 (2023): 1042–46. http://dx.doi.org/10.54254/2753-7064/3/2022807.

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In 2011, Cambridge University held an international academic conference-- performance studies network international conference, and proposed to establish the discipline of performance musicology in 2014, which made it possible to solve the old contradiction between musicology and music performance. This paper explains the possibility and inevitability of the common maturity of musicology and music performance, and seeks more breakthrough points to break the barrier. Through literature analysis, this paper constructs the core value of the practicality of music performance research from the pers

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20

Kim-Boyle, David. "Network Musics: Play, Engagement and the Democratization of Performance." Contemporary Music Review 28, no. 4-5 (2009): 363–75. http://dx.doi.org/10.1080/07494460903422198.

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21

Wang, Dan. "Analysis of Sentiment and Personalised Recommendation in Musical Performance." Computational Intelligence and Neuroscience 2022 (June 2, 2022): 1–6. http://dx.doi.org/10.1155/2022/2778181.

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Music performance research is a comprehensive study of aspects such as emotional analysis and personalisation in music performance, which help to add richness and creativity to the art of music performance. The labels in this paper in collaborative annotation contain rich personalised descriptive information as well as item content information and can therefore be used to help provide better recommendations. The algorithm is based on bipartite graph node structure similarity and restarted random wandering. It analyses the connection between users, items, and tags in the music social network, f

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22

Aswale, Swati, Dr Prabhat Chandra Shrivastava, Dr Ratnesh Ranjan, and Seema Shende. "Indian Classical Music Recognition using Deep Convolution Neural Network." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 73–82. http://dx.doi.org/10.37391/ijeer.120112.

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A divine approach to communicate feelings about the world occurs through music. There is a huge variety in the language of music. One of the principal variables of Indian social legacy is classical music. Hindustani and Carnatic are the two primary subgenres of Indian classical music. Models have been trained and taught to distinguish between Carnatic and Hindustani songs. This paper presents Indian classical music recognition based on multiple acoustic features (MAF) consisting of various statistical, spectral, and time domain features. The MAF provides the changes in intonation, timbre, pros

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23

Aswale, Swati, Dr Prabhat Chandra Shrivastava, Dr Ratnesh Ranjan, and Seema Shende. "Indian Classical Music Recognition using Deep Convolution Neural Network." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 73–82. http://dx.doi.org/10.37391/10.37391/ijeer.120112.

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A divine approach to communicate feelings about the world occurs through music. There is a huge variety in the language of music. One of the principal variables of Indian social legacy is classical music. Hindustani and Carnatic are the two primary subgenres of Indian classical music. Models have been trained and taught to distinguish between Carnatic and Hindustani songs. This paper presents Indian classical music recognition based on multiple acoustic features (MAF) consisting of various statistical, spectral, and time domain features. The MAF provides the changes in intonation, timbre, pros

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24

Li, Suipeng, and Dan Shen. "Wireless Music Playing Buzzer Sensor-Assisted Music Tone Adaptive Control." Journal of Sensors 2022 (February 2, 2022): 1–11. http://dx.doi.org/10.1155/2022/9002533.

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Aiming at the problem of adaptive change of auxiliary music tones, this paper proposes a MAC protocol with a common music tone listening/sleeping type based on a wireless music buzzer sensor. First of all, the new MAC protocol adopts network-wide synchronization, and all sensor nodes in the entire network use the same scheduling table, so that the entire network nodes enter the music tone listening period and the sleep period at the same time. Secondly, the node adaptively adjusts the duty cycle of the node according to the number of data packets in the sending queue, increases the node’s musi

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25

Liu, Xueying. "Research on Piano Performance Optimization Based on Big Data and BP Neural Network Technology." Computational Intelligence and Neuroscience 2022 (February 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/1268303.

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At present, there are many chess styles in piano education, but there is a lack of comprehensive, scientific, and guiding teaching mode. It highlights many educational problems and cannot meet the development requirements of piano education at this stage. However, the piano scoring system can partially replace teachers’ guidance to piano players. This paper extracts the signal characteristics of playing music, establishes the piano performance scoring model using Big Data and BP neural network technology, and selects famous works to test the effect of the scoring system. The results show that

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Ashraf, Mohsin, Fazeel Abid, Ikram Ud Din, et al. "A Hybrid CNN and RNN Variant Model for Music Classification." Applied Sciences 13, no. 3 (2023): 1476. http://dx.doi.org/10.3390/app13031476.

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Music genre classification has a significant role in information retrieval for the organization of growing collections of music. It is challenging to classify music with reliable accuracy. Many methods have utilized handcrafted features to identify unique patterns but are still unable to determine the original music characteristics. Comparatively, music classification using deep learning models has been shown to be dynamic and effective. Among the many neural networks, the combination of a convolutional neural network (CNN) and variants of a recurrent neural network (RNN) has not been signific

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Liu, Hongyu. "Using artificial intelligence to analyze and classify music emotion." Journal of Computational Methods in Sciences and Engineering 24, no. 4-5 (2024): 2611–28. http://dx.doi.org/10.3233/jcm-247488.

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With the rapid development of music digitization and online streaming services, automatic analysis and classification of music content has become an urgent need. This research focuses on music sentiment analysis, which is the identification and classification of emotions expressed by music through algorithms. The study defines and classifies possible emotions in music. Then, advanced artificial intelligence techniques, including traditional machine learning and deep learning methods, were employed to perform sentiment analysis on music fragments. In the process of creating and validating the m

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Liu, Xiahan. "An Improved Particle Swarm Optimization-Powered Adaptive Classification and Migration Visualization for Music Style." Complexity 2021 (April 13, 2021): 1–10. http://dx.doi.org/10.1155/2021/5515095.

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Based on the adaptive particle swarm algorithm and error backpropagation neural network, this paper proposes methods for different styles of music classification and migration visualization. This method has the advantages of simple structure, mature algorithm, and accurate optimization. It can find better network weights and thresholds so that particles can jump out of the local optimal solutions previously searched and search in a larger space. The global search uses the gradient method to accelerate the optimization and control the real-time generation effect of the music style transfer, the

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29

Wei, Xiang, and Shuping Sun. "Optimization of Piano Performance Teaching Mode Using Network Big Data Analysis Technology." International Journal of Information and Communication Technology Education 20, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijicte.341266.

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To effectively avoid subjective bias in manual evaluation. This article proposes a MIDI piano teaching performance evaluation method based on bidirectional LSTM. This method utilizes a three-layer bidirectional LSTM neural network mechanism to make it easier for the model to capture useful information. In addition, the Spark clustering training model is constructed using the deeplearning4j deep learning framework, and the model parameters are adjusted through the UI dependency relationships provided by deeplearning4j to improve work efficiency. The experimental results verified the superiority

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Gong, Tianzhuo. "Deep Belief Network-Based Multifeature Fusion Music Classification Algorithm and Simulation." Complexity 2021 (January 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/8861896.

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In this paper, the multifeature fusion music classification algorithm and its simulation results are studied by deep confidence networks, the multifeature fusion music database is established and preprocessed, and then features are extracted. The simulation is carried out using multifeature fusion music data. The multifeature fusion music preprocessing includes endpoint detection, framing, windowing, and pre-emphasis. In this paper, we extracted the rhythm features, sound quality features, and spectral features, including energy, cross-zero rate, fundamental frequency, harmonic noise ratio, an

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31

Xu, Ning, and Yuanyuan Zhao. "Online Education and Wireless Network Coordination of Electronic Music Creation and Performance under Artificial Intelligence." Wireless Communications and Mobile Computing 2021 (November 18, 2021): 1–9. http://dx.doi.org/10.1155/2021/5999152.

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This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy C -means clustering algorithms show a downward trend. However, with

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32

Engum, Trond, Thomas Henriksen, and Carl Haakon Waadeland. "Improvising Inside a House of Cards: New performance and music-making through a collective networked instrument." Organised Sound 26, no. 3 (2021): 378–89. http://dx.doi.org/10.1017/s1355771821000467.

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This article presents experiences and reflections related to performing improvised, live processed electroacoustic music within a context of networked music performance. The musical interaction is performed through a new collective networked instrument, and we report how the ensemble ‘Magnify the Sound’, consisting of two of the authors of this article, meets the instrument in different networked performance situations, and how this is related to the affordance of the instrument. In our performances the network is inherent to our artistic practice, and we experience a phenomenological and soma

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Chen, Yunfei. "Construction and Application of Music Style Intelligent Learning System Based on Situational Awareness." Mathematical Problems in Engineering 2022 (September 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/2689233.

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Contextual representation recommendation directly uses contextual prefiltering technology when processing user contextual data, which is not the integration of context and model in the true sense. To this end, this paper proposes a context-aware recommendation model based on probability matrix factorization. We design a music genre style recognition and generation network. In this network, all the sub-networks of music genres share the explanation layer, which can greatly reduce the learning of model parameters and improve the learning efficiency. Each music genre sub-network analyzes music of

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34

Wang, Shu, Chonghuan Xu, Austin Shijun Ding, and Zhongyun Tang. "A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network." Electronics 10, no. 15 (2021): 1769. http://dx.doi.org/10.3390/electronics10151769.

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Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a rep

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Li, Lei. "Improved Feature Pyramid Convolutional Neural Network for Effective Recognition of Music Scores." Computational Intelligence and Neuroscience 2022 (May 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/6071114.

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Music written by composers and performed by multidimensional instruments is an art form that reflects real-life emotions. Historically, people disseminated music primarily through sheet music recording and oral transmission. Among them, recording music in sheet music form was a great musical invention. It became the carrier of music communication and inheritance, as well as a record of humanity's magnificent music culture. The advent of digital technology solves the problem of difficult musical score storage and distribution. However, there are many drawbacks to using data in image format, and

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Yang, Dongxu, and Weiya Zhang. "Construction of Piano Performance Curriculum System Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (August 23, 2022): 1–8. http://dx.doi.org/10.1155/2022/1556606.

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Comprehensively promoting quality education and the all-round development of human beings is the focus of current educational work. When carrying out quality education at university, it is important to start from all aspects such as ideology and morality, physical and mental health, professional learning and personality cultivation, and to give full play to their potential and enhance their creativity. Music teaching is an important element of quality education and using it as an entry point can prevent it from being too abstract. However, music education is still a weak aspect of higher educa

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Alexandraki, Chrisoula, and Demosthenes Akoumianakis. "Exploring New Perspectives in Network Music Performance: The DIAMOUSES Framework." Computer Music Journal 34, no. 2 (2010): 66–83. http://dx.doi.org/10.1162/comj.2010.34.2.66.

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38

Romero-Arenas, Raymundo, Alfonso Gómez-Espinosa, and Benjamín Valdés-Aguirre. "Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network." Applied Sciences 12, no. 15 (2022): 7405. http://dx.doi.org/10.3390/app12157405.

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Singing Voice Detection (SVD) is a classification task that determines whether there is a singing voice in a given audio segment. While current systems produce high-quality results on this task, the reported experiments are usually limited to popular music. A Long-Term Recurrent Convolutional Network (LRCN) was adapted to detect vocals in a new dataset of electronic music to evaluate its performance in a different music genre and compare its results against those in other state-of-the-art experiments in pop music to prove its effectiveness across a different genre. Experiments on two datasets

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Mersy, Gabriel. "Efficient Robust Music Genre Classification with Depthwise Separable Convolutions and Source Separation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15972–73. http://dx.doi.org/10.1609/aaai.v35i18.17982.

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Given recent advances in deep music source separation, a feature representation method is proposed that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). A depthwise separable convolutional neural network is trained on a challenging electronic dance music (EDM) data set and its performance is compared to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data se

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Mersy, Gabriel, and Jin Hong Kuan. "Source Separation and Depthwise Separable Convolutions for Computer Audition (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15847–48. http://dx.doi.org/10.1609/aaai.v35i18.17920.

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Given recent advances in deep music source separation, we propose a feature representation method that combines source separation with a state-of-the-art representation learning technique that is suitably repurposed for computer audition (i.e. machine listening). We train a depthwise separable convolutional neural network on a challenging electronic dance music (EDM) data set and compare its performance to convolutional neural networks operating on both source separated and standard spectrograms. It is shown that source separation improves classification performance in a limited-data setting c

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Chen, Xize, Xiaoyu Qu, Yufeng Qian, and Yiyao Zhang. "Music Recognition Using Blockchain Technology and Deep Learning." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/7025338.

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The purposes are to recognize and classify different music characteristics and strengthen the copyright protection system for original digital music in the big data era. Deep learning (DL) and blockchain technology are applied and researched herein. Based on CNN (Convolutional Neural Network), a music recognition method combined with hashing learning is proposed. The error generated when outputting the binary hash code is considered, and the semantic similarity of the hash code is ensured. Besides, the application of blockchain technology in the current intellectual property protection in orig

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Chen, Jing. "Construction of Music Intelligent Creation Model Based on Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (July 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/2854066.

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The application of machine learning technology to intelligent music creation has become a very important field in music creation. The main current research on music intelligent creation methods uses fixed coding steps in audio data, which lead to weak feature expression ability. Based on convolutional neural network theory, this paper proposes a deep music intelligent creation method. The model uses a convolutional recurrent neural network to generate an effective hash code, first preprocesses the music signal to obtain a Mel spectrogram, and then inputs it into a pretrained CNN to extract fro

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Zhang, Mingjie. "Designing a National Music System for a Smart Concert Hall Using Neural Network and Wireless Internet of Things." Scientific Programming 2022 (June 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/3365974.

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With the fast advancement of science and technology, numerous music systems have emerged. However, there are few folk music in the music industry, for example, pop music, which leads to the inability of folk music lovers to find their favorite music. For this problem, this paper develops a national system of smart concert hall based on a neural network and wireless Internet of things (IoT). It establishes the architecture model of the wireless IoT system of the concert hall by describing the neural network, neuron model, and BP neural network model in detail. Besides, this paper develops the s

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Pan, Jian, Shaode Yu, Zi Zhang, Zhen Hu, and Mingliang Wei. "The Generation of Piano Music Using Deep Learning Aided by Robotic Technology." Computational Intelligence and Neuroscience 2022 (October 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/8336616.

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In order to improve the accuracy and precision of music generation assisted by robotics, this study analyzes the application of deep learning in piano music generation. Firstly, based on the basic concepts of robotics and deep learning, the advantages of long short-term memory (LSTM) networks are introduced and applied to the piano music generation. Meanwhile, based on LSTM, dropout coefficients are used for optimization. Secondly, various parameters of the algorithm are determined, including the effects of the number of iterations and neurons in the hidden layer on the effect of piano music g

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Liu, Zhiwei, Ting Bian, and Minglai Yang. "Locally Activated Gated Neural Network for Automatic Music Genre Classification." Applied Sciences 13, no. 8 (2023): 5010. http://dx.doi.org/10.3390/app13085010.

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Automatic music genre classification is a prevailing pattern recognition task, and many algorithms have been proposed for accurate classification. Considering that the genre of music is a very broad concept, even music within the same genre can have significant differences. The current methods have not paid attention to the characteristics of large intra-class differences. This paper presents a novel approach to address this issue, using a locally activated gated neural network (LGNet). By incorporating multiple locally activated multi-layer perceptrons and a gated routing network, LGNet adapt

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Xu, Fumei, and Yu Xia. "Music Art Teaching Quality Evaluation System Based on Convolutional Neural Network." Computational and Mathematical Methods in Medicine 2022 (June 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/8479940.

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With the rapid growth of music and art education in colleges and universities today, the development of associated teaching quality assessment (TQE) is still in its infancy. In truth, most modern music and art education has yet to build a rigorous and appropriate evaluation system based on actual classroom teaching quality. Simply adopting classroom TQE indicators and approaches from other disciplines would unavoidably lead to formalization of music TQE findings in some schools and institutions. It has no bearing on evaluation, feedback, or advancement. Therefore, this paper uses the superior

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Burtner, Matthew, Steven Kemper, and David Topper. "Network Socio-Synthesis and Emergence in NOMADS." Organised Sound 17, no. 1 (2012): 45–55. http://dx.doi.org/10.1017/s1355771811000501.

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NOMADS (Network-Operational Mobile Applied Digital System) is a network client–server-based system for participant interaction in music and multimedia performance contexts. NOMADS allows large groups of participants, including the audience, to form a mobile interactive computer ensemble distributed across a network. Participants become part of a synergistic interaction with other performers, contributing to the multimedia performance. The system enhances local performance spaces, and it can integrate audiences located in multiple performance venues. Individual user input from up to thousands o

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Huang, Shaohan. "Innovative Practice of Music Education in the Universities in the Context of 5G Network." Computational Intelligence and Neuroscience 2022 (June 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/3451422.

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Music education is among the most significant subjects covered in providing high-quality education in Chinese universities and colleges. Music education is critical to providing high-quality education to students. It contributes significantly to the development of students’ creative motivation, inventive capacity, and personality development. Music education provides excellent outcomes in music instruction and fosters students’ original thinking and comprehensive abilities, and therefore supports the overall development of high-quality education. With the development of the educational system,

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KAPUR, AJAY, GE WANG, PHILIP DAVIDSON, and PERRY R. COOK. "Interactive Network Performance: a dream worth dreaming?" Organised Sound 10, no. 3 (2005): 209–19. http://dx.doi.org/10.1017/s1355771805000956.

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This paper questions and examines the validity and future of interactive network performance. The history of research in the area is described as well as experiments with our own system. Our custom-built networked framework, known as GIGAPOPR, transfers high-quality audio, video and MIDI data over a network connection to enable live musical performances to occur in two or more distinct locations. One of our first sensor-augmented Indian instruments, The Electronic Dholak (EDholak) is a multi-player networked percussion controller that is modelled after the traditional Indian Dholak. The EDhola

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Zhang, LiLan. "Music Morphology Interaction under Artificial Intelligence in Wireless Network Environment." Computational Intelligence and Neuroscience 2022 (May 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/9002093.

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Music has become the main information carrier, and music and its emotional expression are accurately classified to obtain relevant information. However, how to classify music accurately is a problem that needs to be discussed. The concept and feature extraction strategy of the morphology of music are described. Moreover, the feature extraction and morphological classification elements of digital music are introduced. Next, music morphology is recognized and classified based on the neural network and relief algorithm. In the network, by randomly selecting different music types, the audio data i

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