A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0
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.
Classifier learning, Feature descriptors, Instrument recognition, Multimodal communication, Music information retrieval
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