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A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0

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Title A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0
 
Creator Dash, Sukanta Kumar
Solanki, S S
Chakraborty, Soubhik
 
Subject Classifier learning
Feature descriptors
Instrument recognition
Multimodal communication
Music information retrieval
 
Description 26-37
Over the last two decades, the application of machine technology has shifted from industrial to residential use. Further,
advances in hardware and software sectors have led machine technology to its utmost application, the human-machine
interaction, a multimodal communication. Multimodal communication refers to the integration of various modalities of
information like speech, image, music, gesture, and facial expressions. Music is the non-verbal type of communication that
humans often use to express their minds. Thus, Music Information Retrieval (MIR) has become a booming field of research
and has gained a lot of interest from the academic community, music industry, and vast multimedia users. The problem in
MIR is accessing and retrieving a specific type of music as demanded from the extensive music data. The most inherent
problem in MIR is music classification. The essential MIR tasks are artist identification, genre classification, mood
classification, music annotation, and instrument recognition. Among these, instrument recognition is a vital sub-task in MIR
for various reasons, including retrieval of music information, sound source separation, and automatic music transcription. In
recent past years, many researchers have reported different machine learning techniques for musical instrument recognition
and proved some of them to be good ones. This article provides a systematic, comprehensive review of the advanced
machine learning techniques used for musical instrument recognition. We have stressed on different audio feature
descriptors of common choices of classifier learning used for musical instrument recognition. This review article emphasizes
on the recent developments in music classification techniques and discusses a few associated future research problems.
 
Date 2023-01-16T10:52:28Z
2023-01-16T10:52:28Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61207
https://doi.org/10.56042/jsir.v82i1.70251
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]