Abstract
Sounds or music usually occurs in an unstructured environment where their frequency varies from time to time. These temporal variations are one of the major problems in the music information retrieval. Additionally, polyphonic music or polyphony is simultaneous combination of two or more tones or melodic line, where each line is an independent melody of an instrument. As a result, identifying various instruments from recordings of polyphonic music is difficult and inaccurate using conventional methods. In this paper, a framework is presented for predominant instrument recognition in real-world polyphonic music. The framework consists of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are used to pull out important features that are unchanging to local spectral and temporal variations. Similarly, RNNs are used as they quickly learn the long-term dependencies in the audio signals. The results obtained by the convolutional recurrent neural networks (CRNNs) showed an improved performance when compared to network built using only convolutional neural networks.
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Acknowledgements
It is our privilege to acknowledge with deep sense of gratitude and devotion for keen personal interest and invaluable guidance rendered by our Head of the Department, Dr. Suresh Pabboju, Professor, Department of Information Technology, Chaitanya Bharathi Institute of Technology.
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Kilambi, B.R., Parankusham, A.R., Tadepalli, S.K. (2021). Instrument Recognition in Polyphonic Music Using Convolutional Recurrent Neural Networks. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_38
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DOI: https://doi.org/10.1007/978-981-15-8443-5_38
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