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Journal articles on the topic 'Music information processing'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Zhao, Tian, and Patricia K. Kuhl. "Music, speech, and temporal information processing." Journal of the Acoustical Society of America 144, no. 3 (2018): 1760. http://dx.doi.org/10.1121/1.5067789.

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2

Goto, Masataka, and Keiji Hirata. "Recent studies on music information processing." Acoustical Science and Technology 25, no. 6 (2004): 419–25. http://dx.doi.org/10.1250/ast.25.419.

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3

Tsuboi, Kuniharu. "Computer music and musical information processing." Journal of the Institute of Television Engineers of Japan 42, no. 1 (1988): 49–55. http://dx.doi.org/10.3169/itej1978.42.49.

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4

Katayose, Haruhiro. "The Dawn of Kansei Information Processing. Application of Kansei Information Processing. Music Performance." Journal of the Institute of Image Information and Television Engineers 52, no. 1 (1998): 53–55. http://dx.doi.org/10.3169/itej.52.53.

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5

Bugos, Jennifer, and Wendy Mostafa. "Musical Training Enhances Information Processing Speed." Bulletin of the Council for Research in Music Education, no. 187 (January 1, 2011): 7–18. http://dx.doi.org/10.2307/41162320.

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Abstract The purpose of this research is to examine the effects of music instruction on information processing speed. We examined music’s role on information processing speed in musicians (N = 14) and non-musicians (N = 16) using standardized neuropsychological measures, the Paced Auditory Serial Addition Task (PASAT) and the Trail Making Test (TMT). Results of a One Way ANOVA indicate significantly (p &lt; .05) enhanced performance by musicians compared to non-musicians on the PASAT and TMT (Part A and B). These results suggest that musical training has the capacity to enhance processing

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6

Atherton, Ryan P., Quin M. Chrobak, Frances H. Rauscher, et al. "Shared Processing of Language and Music." Experimental Psychology 65, no. 1 (2018): 40–48. http://dx.doi.org/10.1027/1618-3169/a000388.

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Abstract. The present study sought to explore whether musical information is processed by the phonological loop component of the working memory model of immediate memory. Original instantiations of this model primarily focused on the processing of linguistic information. However, the model was less clear about how acoustic information lacking phonological qualities is actively processed. Although previous research has generally supported shared processing of phonological and musical information, these studies were limited as a result of a number of methodological concerns (e.g., the use of sim

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7

FUKAYAMA, Satoru. "Music Information Processing for Visualization with Musical Notations." Journal of the Visualization Society of Japan 40, no. 158 (2020): 19–22. http://dx.doi.org/10.3154/jvs.40.158_19.

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8

Rammsayer, Thomas, and Eckart Altenmüller. "Temporal Information Processing in Musicians and Nonmusicians." Music Perception 24, no. 1 (2006): 37–48. http://dx.doi.org/10.1525/mp.2006.24.1.37.

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The present study was designed to examine the general notion that temporal information processing is more accurate in musicians than in nonmusicians. For this purpose, 36 academically trained musicians and 36 nonmusicians performed seven different auditory temporal tasks. Superior temporal acuity for musicians compared to nonmusicians was shown for auditory fusion, rhythm perception, and three temporal discrimination tasks. The two groups did not differ, however, in terms of their performance on two tasks of temporal generalization. Musicians’superior performance appeared to be limited to aspe

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9

Li, Yi. "Digital Development for Music Appreciation of Information Resources Using Big Data Environment." Mobile Information Systems 2022 (September 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/7873636.

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With the continuous development of information technology and the arrival of the era of big data, music appreciation has also entered the digital development. Big data essence is highlighted by comparison with traditional data management and processing technologies. Under different requirements, the required time processing range is different. Music appreciation is an essential and important part of music lessons, which can enrich people’s emotional experience, improve aesthetic ability, and cultivate noble sentiments. Data processing of music information resources will greatly facilitate the

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10

Moreno, Alberto. "ELEMENTS OF MUSIC BASED ON ARTIFICIAL INTELLIGENCE." Acta Informatica Malaysia 4, no. 2 (2020): 30–32. http://dx.doi.org/10.26480/aim.02.2020.30.32.

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Thus, for the current status of research and practical music audio processing needs, this paper argues, the music element analysis technology is the key to this research field, and on this basis, proposes a new framework music processing – Music calculation system, the core objective is to study intelligently and automatically identifies various elements of music information and analyze the information used in constructing the music content, and intelligent retrieval method translated. To achieve the above core research objectives, the paper advocates will be closely integrated music theory an

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Achkar, Charbel El, and Talar Atechian. "MEI2JSON: a pre-processing music scores converter." International Journal of Intelligent Information and Database Systems 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijiids.2021.10040316.

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Achkar, Charbel El, and Talar Atéchian. "MEI2JSON: a pre-processing music scores converter." International Journal of Intelligent Information and Database Systems 15, no. 1 (2022): 57. http://dx.doi.org/10.1504/ijiids.2022.120130.

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13

Unyk, Anna M. "An information-processing analysis of expectancy in music cognition." Psychomusicology: A Journal of Research in Music Cognition 9, no. 2 (1990): 229–40. http://dx.doi.org/10.1037/h0094146.

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14

Rohrmeier, Martin A., and Stefan Koelsch. "Predictive information processing in music cognition. A critical review." International Journal of Psychophysiology 83, no. 2 (2012): 164–75. http://dx.doi.org/10.1016/j.ijpsycho.2011.12.010.

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15

Osaka, Naotoshi. "Electroacoustic Music Linked with Information Processing Research in Japan." Contemporary Music Review 37, no. 1-2 (2018): 67–85. http://dx.doi.org/10.1080/07494467.2018.1453337.

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16

Geake, John G. "An Information Processing Account of Audiational Abilities." Research Studies in Music Education 12, no. 1 (1999): 10–23. http://dx.doi.org/10.1177/1321103x9901200102.

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17

Fritz, Thomas, and Stefan Koelsch. "The role of semantic association and emotional contagion for the induction of emotion with music." Behavioral and Brain Sciences 31, no. 5 (2008): 579–80. http://dx.doi.org/10.1017/s0140525x08005347.

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AbstractWe suggest that semantic association may be a further mechanism by which music may elicit emotion. Furthermore, we note that emotional contagion is not always an immediate process requiring little prior information processing; rather, emotional contagion contributing to music processing may constitute a more complex decoding mechanism for information inherent in the music, which may be subject to a time course of activation.

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18

Zhang, Shenghuan, and Ye Cheng. "Masking and noise reduction processing of music signals in reverberant music." Journal of Intelligent Systems 31, no. 1 (2022): 420–27. http://dx.doi.org/10.1515/jisys-2022-0024.

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Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction meth

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19

Mao, Nan. "Analysis on the Application of Dependent Information System Optimization Algorithm in Music Education in Colleges and Universities." Mobile Information Systems 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/4102280.

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Although the influence of pop music is huge and the emergence of pop music talents is also increasing, its education has great problems. Moreover, both theoretical research and professional discipline construction are still very backward. In order to improve the efficiency of music teaching in colleges and universities, this paper applies the information system optimization algorithm to the intelligent analysis of music education in colleges and universities, selects the appropriate method for music information processing, and builds the music education system in colleges and universities on t

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20

Colby, Michael, Sarah J. Shaw, and Lauralee Shiere. "Sheet Music Cataloging and Processing: A Manual." Notes 42, no. 4 (1986): 779. http://dx.doi.org/10.2307/897789.

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21

Tsuboi, Kuniharu, and Mitsuru Ishizuka. "Describing method of music information toward its advanced computer processing." Systems and Computers in Japan 17, no. 7 (1986): 60–62. http://dx.doi.org/10.1002/scj.4690170707.

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22

MORI, Kazuma. "Psychology and Cognitive Neuroscience Experiments Integrated with Music Information Processing." Journal of Japan Society of Kansei Engineering 23, no. 2 (2025): 68–71. https://doi.org/10.5057/kansei.23.2_68.

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23

Edworthy, Judy. "Interval and Contour in Melody Processing." Music Perception 2, no. 3 (1985): 375–88. http://dx.doi.org/10.2307/40285305.

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Musician subjects were required to detect interval and contour changes in transposed versions of standard melodies of 3, 5, 7, 9,11,13, and 15 notes. Subjects were significantly better at detecting contour alterations for melodies of up to 11 notes but significantly better at detecting interval alterations in the 15-note melodies. Serial position effects for 5-, 7-, and 9-note melodies showed contour to be immediately precise after transposition, whereas the ability to detect interval alterations improved as the melodies progressed. These results suggest that, on transposition, contour informa

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24

Terhardt, Ernst. "Music Perception and Sensory Information Acquisition: Relationships and Low-Level Analogies." Music Perception 8, no. 3 (1991): 217–39. http://dx.doi.org/10.2307/40285500.

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Information processing is characterized by conditional decisions on hierarchically organized levels. In biological systems, this principle is manifest in the phenomena of contourization and categorization, which are more or less synonymous. Primary contourization—such as in the visual system—is regarded as the first step of abstraction. Its auditory equivalent is formation of spectral pitches. Hierarchical processing is characterized by the principles of immediate processing, open end, recursion, distributed knowledge, forward processing, autonomy, and viewback. In that concept, perceptual phe

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25

Geake, John G. "Why Mozart? Information Processing Abilities of Gifted Young Musicians." Research Studies in Music Education 7, no. 1 (1996): 28–45. http://dx.doi.org/10.1177/1321103x9600700103.

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26

Ognjenovic, Predrag. "Processing of Aesthetic Information." Empirical Studies of the Arts 9, no. 1 (1991): 1–9. http://dx.doi.org/10.2190/kc25-jwtn-nrx4-c7a1.

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27

Menkin, A. V. "Development of a Music Recommender System Based on Content Metadata Processing." Vestnik NSU. Series: Information Technologies 17, no. 3 (2019): 43–60. http://dx.doi.org/10.25205/1818-7900-2019-17-3-43-60.

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Music recommender systems (MRS) help users of music streaming services to find interesting music in the music catalogs. The sparsity problem is an essential problem of MRS research. It refers to the fact that user usually rates only a tiny part of items. As a result, MRS often has not enough data to make a recommendation. To solve the sparsity problem, in this paper, a new approach that uses related items’ ratings is proposed. Hybrid MRS based on this approach is described. It uses tracks, albums, artists, genres normalized ratings along with information about relations between items of differ

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28

Goolsby, Thomas W. "Profiles of Processing: Eye Movements during Sightreading." Music Perception 12, no. 1 (1994): 97–123. http://dx.doi.org/10.2307/40285757.

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Temporal and sequential components of the eye movement used by a skilled and a less-skilled sightreader were used to construct six profiles of processing. Each subject read three melodies of varying levels of concentration of visual detail. The profiles indicates the order, duration, and location of each fixation while the subjects sightread the melodies. Results indicate that music readers do not fixate on note stems or the bar lines that connect eighth notes when sightreading. The less-skilled music reader progressed through the melody virtually note-by-note using long fixations, whereas the

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29

Carpentier, Sarah M., Andrea R. McCulloch, Tanya M. Brown, et al. "Complexity Matching: Brain Signals Mirror Environment Information Patterns during Music Listening and Reward." Journal of Cognitive Neuroscience 32, no. 4 (2020): 734–45. http://dx.doi.org/10.1162/jocn_a_01508.

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Understanding how the human brain integrates information from the environment with intrinsic brain signals to produce individual perspectives is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain, and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment

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30

Zhao, T. Christina, and Patricia K. Kuhl. "Musical intervention enhances infants’ neural processing of temporal structure in music and speech." Proceedings of the National Academy of Sciences 113, no. 19 (2016): 5212–17. http://dx.doi.org/10.1073/pnas.1603984113.

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Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants’ neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention tar

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31

Huron, David. "Music Information Processing Using the Humdrum Toolkit: Concepts, Examples, and Lessons." Computer Music Journal 26, no. 2 (2002): 11–26. http://dx.doi.org/10.1162/014892602760137158.

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32

Li, Pengfei. "Design of Meyer’s Theory-based High Quality Piano Multi-media System." International Journal of Emerging Technologies in Learning (iJET) 12, no. 01 (2017): 95. http://dx.doi.org/10.3991/ijet.v12i01.6486.

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Based on the current situation of the development of multi-media technology and its application in colleges and universities, this paper selected high-quality multi-media hardware devices suitable for the piano curriculum, followed Meyer’s principles for multi-media instruction design, and designed a multi-media through the visualization of multi-media information, promotion of necessary cognitive processing, reduction of external cognitive processing, stimulation of generative cognitive processing and other information processing methods, to improve the information presentation ways and deliv

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33

Sanfilippo, Dario. "Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing." Computer Music Journal 45, no. 1 (2021): 24–38. http://dx.doi.org/10.1162/comj_a_00592.

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Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated stream

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34

Siddiquee, Md Mahfuzur Rahman, Md Saifur Rahman, Shahnewaz Ul Islam Chowdhury, and Rashedur M. Rahman. "Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation." International Journal of Software Innovation 4, no. 2 (2016): 71–87. http://dx.doi.org/10.4018/ijsi.2016040105.

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In this research, the authors propose an intelligent system that can recommend songs to user according to his choice. They predict the next song a user might prefer to listen based on their previous listening patterns, currently played songs and similar music based on music data. To calculate music similarity the authors used a Matlab toolbox that considers audio signals. They used association rule mining to find users' listening patterns and predict the next song the user might prefer. As they propose a music discovery service as well, the authors use the information of music listening patter

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35

Tian, Jinhao, Zuchao Li, Jiajia Li, and Ping Wang. "N-gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15364–72. http://dx.doi.org/10.1609/aaai.v38i14.29461.

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The first step to apply deep learning techniques for symbolic music understanding is to transform musical pieces (mainly in MIDI format) into sequences of predefined tokens like note pitch, note velocity, and chords. Subsequently, the sequences are fed into a neural sequence model to accomplish specific tasks. Music sequences exhibit strong correlations between adjacent elements, making them prime candidates for N-gram techniques from Natural Language Processing (NLP). Consider classical piano music: specific melodies might recur throughout a piece, with subtle variations each time. In this pa

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36

Hutka, Stefanie, Sarah M. Carpentier, Gavin M. Bidelman, Sylvain Moreno, and Anthony R. McIntosh. "Musicianship and Tone Language Experience Are Associated with Differential Changes in Brain Signal Variability." Journal of Cognitive Neuroscience 28, no. 12 (2016): 2044–58. http://dx.doi.org/10.1162/jocn_a_01021.

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Musicianship has been associated with auditory processing benefits. It is unclear, however, whether pitch processing experience in nonmusical contexts, namely, speaking a tone language, has comparable associations with auditory processing. Studies comparing the auditory processing of musicians and tone language speakers have shown varying degrees of between-group similarity with regard to perceptual processing benefits and, particularly, nonlinguistic pitch processing. To test whether the auditory abilities honed by musicianship or speaking a tone language differentially impact the neural netw

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Gupta, Ashish, Braj Bhushan, and Laxmidhar Behera. "Neural response to sad autobiographical recall and sad music listening post recall reveals distinct brain activation in alpha and gamma bands." PLOS ONE 18, no. 1 (2023): e0279814. http://dx.doi.org/10.1371/journal.pone.0279814.

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Although apparently paradoxical, sad music has been effective in coping with sad life experiences. The underpinning brain neural correlates of this are not well explored. We performed Electroencephalography (EEG) source-level analysis for the brain during a sad autobiographical recall (SAR) and upon exposure to sad music. We specifically investigated the Cingulate cortex complex and Parahippocampus (PHC) regions, areas prominently involved in emotion and memory processing. Results show enhanced alpha band lag phase-synchronization in the brain during sad music listening, especially within and

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38

Maidhof, Clemens, and Stefan Koelsch. "Effects of Selective Attention on Syntax Processing in Music and Language." Journal of Cognitive Neuroscience 23, no. 9 (2011): 2252–67. http://dx.doi.org/10.1162/jocn.2010.21542.

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The present study investigated the effects of auditory selective attention on the processing of syntactic information in music and speech using event-related potentials. Spoken sentences or musical chord sequences were either presented in isolation, or simultaneously. When presented simultaneously, participants had to focus their attention either on speech, or on music. Final words of sentences and final harmonies of chord sequences were syntactically either correct or incorrect. Irregular chords elicited an early right anterior negativity (ERAN), whose amplitude was decreased when music was s

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39

Berz, William L. "Working Memory in Music: A Theoretical Model." Music Perception 12, no. 3 (1995): 353–64. http://dx.doi.org/10.2307/40286188.

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Many psychologists have accepted a dual memory system with separate short-and long-term storage components. More recently, the concept of working memory, where short-term memory is composed of both storage and processing segments, has been considered. Baddeley (1990) proposes a model for working memory that includes a central executive controller along with two slave systems: the phonological loop and the visuospatial sketch pad. The model allows for both storage and manipulation of information. However, this model does not seem to account adequately for musical memory (Clarke, 1993). Through

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40

Koelsch, Stefan, Tobias Grossmann, Thomas C. Gunter, Anja Hahne, Erich Schröger, and Angela D. Friederici. "Children Processing Music: Electric Brain Responses Reveal Musical Competence and Gender Differences." Journal of Cognitive Neuroscience 15, no. 5 (2003): 683–93. http://dx.doi.org/10.1162/jocn.2003.15.5.683.

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Numerous studies investigated physiological correlates of the processing of musical information in adults. How these correlates develop during childhood is poorly understood. In the present study, we measured event-related electric brain potentials elicited in 5and 9-year-old children while they listened to (major–minor tonal) music. Stimuli were chord sequences, infrequently containing harmonically inappropriate chords. Our results demonstrate that the degree of (in) appropriateness of the chords modified the brain responses in both groups according to music-theoretical principles. This sugge

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41

Swinney, David, and Tracy Love. "The Processing of Discontinuous Dependencies in Language and Music." Music Perception 16, no. 1 (1998): 63–78. http://dx.doi.org/10.2307/40285778.

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This article examines the nature and time course of the processing of discontinuous dependency relationships in language and draws suggestive parallels to similar issues in music perception. The on-line language comprehension data presented demonstrate that discontinuous structural dependencies cause reactivation of the misordered or "stranded" sentential material at its underlying canonical position in the sentence during ongoing comprehension. Further, this process is demonstrated to be driven by structural knowledge, independent of pragmatic information, aided by prosodic cues, and dependen

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42

Gedik, Ali C., and Barış Bozkurt. "Pitch-frequency histogram-based music information retrieval for Turkish music." Signal Processing 90, no. 4 (2010): 1049–63. http://dx.doi.org/10.1016/j.sigpro.2009.06.017.

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43

Schwabe, Markus, Omar Elaiashy, and Fernando Puente León. "Incorporation of phase information for improved time-dependent instrument recognition." tm - Technisches Messen 87, s1 (2020): s62—s67. http://dx.doi.org/10.1515/teme-2020-0031.

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AbstractTime-dependent estimation of playing instruments in music recordings is an important preprocessing for several music signal processing algorithms. In this approach, instrument recognition is realized by neural networks with a two-dimensional input of short-time Fourier transform (STFT) magnitudes and a time-frequency representation based on phase information. The modified group delay (MODGD) function and the product spectrum (PS), which is based on MODGD, are analysed as phase representations. Training and evaluation processes are executed based on the MusicNet dataset. By the incorpor

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Schaefer, Rebecca S. "Mental Representations in Musical Processing and their Role in Action-Perception Loops." Empirical Musicology Review 9, no. 3-4 (2015): 161. http://dx.doi.org/10.18061/emr.v9i3-4.4291.

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Music is created in the listener as it is perceived and interpreted - its meaning derived from our unique sense of it; likely driving the range of interpersonal differences found in music processing. Person-specific mental representations of music are thought to unfold on multiple levels as we listen, spanning from an entire piece of music to regularities detected across notes. As we track incoming auditory information, predictions are generated at different levels for different musical aspects, leading to specific percepts and behavioral outputs, illustrating a tight coupling of cognition, pe

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45

Kızrak, Merve Ayyüce, and Bülent Bolat. "A musical information retrieval system for Classical Turkish Music makams." SIMULATION 93, no. 9 (2017): 749–57. http://dx.doi.org/10.1177/0037549717708615.

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Musical information retrieval (MIR) applications have become an interesting topic both for researchers and commercial applications. The majority of the current knowledge on MIR is based on Western music. However, traditional genres, such as Classical Turkish Music (CTM), have great structural differences compared with Western music. Then, the validity of the current knowledge on this subject must be checked on such genres. Through this work, a MIR application that simulates the human music processing system based on CTM is proposed. To achieve this goal, first mel-frequency cepstral coefficien

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46

Rege, Amit, and Ravi Sindal. "Audio classification for music information retrieval of Hindustani vocal music." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1481. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1481-1490.

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An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classifica

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47

Chen, Chen. "Design of Deep Learning Network Model for Personalized Music Emotional Recommendation." Security and Communication Networks 2022 (May 2, 2022): 1–8. http://dx.doi.org/10.1155/2022/4443277.

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Music is a way for people to express their inner thoughts, and it is an art form to express their feelings and send their emotions. In modern society, people tend to listen to music more and more as a way of leisure and entertainment, and different types of music hold different feelings of listeners and trigger different emotional resonances. In this study, we propose an algorithmic model based on the two-layer attention mechanism, which includes the processing of textual convolutional neural network for music name and music label text data and the processing of two-layer attention mechanism,

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48

Herulambang, Wiwiet, and Rifki Fahrial Zainal. "Realtime Portable Music's Genre Classificator with The Kohonen (SOM) Methods Using Raspberry PI." JEECS (Journal of Electrical Engineering and Computer Sciences) 3, no. 2 (2018): 439–44. http://dx.doi.org/10.54732/jeecs.v3i2.131.

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Music genre is one of the digital music data that is determined to classify music based on all the characterequations of each type. The characteristics in question are usually seen from the frequency of music, rhythmicstructure, instrumentation structure, and harmony content that the music has. Classification of music genres inrealtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because itis done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quiteconcisely used as a portable media and is quite

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Jensen, Robert C., and Sarah Hargus Ferguson. "Music Perception in Adult Users of Cochlear Implants: A Brief Review." Perspectives on Aural Rehabilitation and Its Instrumentation 22, no. 1 (2015): 4–11. http://dx.doi.org/10.1044/arii22.1.4.

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Although cochlear implants (CIs) can provide good speech understanding in quiet, in general, users of CIs have shown poor music perception performance, particularly with regard to pitch (and hence melody). This is primarily due to the limited ability of CI processing strategies and electric stimulation to provide place pitch and fine structure information from the original input signal to the auditory nervous system of the user. Approaches such as current focusing, current steering, enhanced amplitude modulation cues, and optic stimulation have been shown or theorized to assist in music percep

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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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Abstract:

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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