Abstract
The music industry has made remarkable progress over the last few decades from vinyls to digital audio. The field of Music Information Retrieval (MIR) has received attention from researchers across the globe because of its diverse applications, one of them being Automatic Music Transcription (AMT). A music piece generally consists of an array of instruments played simultaneously and prior to transcription it is essential to identify the active regions of the instruments. Identifying instruments in isolation prior to identifying their active regions in a piece is essential. SMIL (Segregate Musical Instrument by Listening) is a system aimed towards identification of isolated instruments from stereophonic audio. Mel Scale Cepstral Coefficient (MFCC) based features coupled with a Multi Layer Perceptron (MLP) based classifier has been used to characterize 2716 clips from 7 different instruments and an average accuracy of 98.38% has been obtained.
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Acknowledgment
The authors are thankful to Mr. Pradip Ghosh for lending a helping hand in course of data collection and preparation of the manuscript.
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Mukherjee, H., Obaidullah, S.M., Phadikar, S., Roy, K. (2017). SMIL - A Musical Instrument Identification System. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_11
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DOI: https://doi.org/10.1007/978-981-10-6427-2_11
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