Abstract
India is known for its culture of traditional folk music. Folk music represents the identity of different Indian regions and festival celebrations by people in those regions. Many popular Bollywood songs are based on folk music. There is a wide scope to apply artificial intelligence in this domain. However, to the best of our knowledge, there has been no availability of any audio dataset which represents Indian folk songs from various regions. In this work, we introduce a novel audio dataset of 307 folk songs from five major regions of India, namely Assamese, Marathi, Kashmiri, Kannada and Uttarakhandi. A pre-trained ResNet-based model has been used on mel-spectrogram-based audio representations for classifying a given folk song into one of these five regions. Results indicate that mel-spectrogram-based representation provides better performance in comparison with traditional spectrogram and short-term spectral feature-based representations.
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Authors acknowledge the insights provided by several people during discussions on this folk music research. We also thank to reviewers for their comments to improve the paper.
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Patel, A., Shah, A., Gor, K., Mankad, S.H. (2021). IFSC: A Database for Indian Folk Songs Classification. In: Biswas, A., Wennekes, E., Hong, TP., Wieczorkowska, A. (eds) Advances in Speech and Music Technology. Advances in Intelligent Systems and Computing, vol 1320. Springer, Singapore. https://doi.org/10.1007/978-981-33-6881-1_15
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DOI: https://doi.org/10.1007/978-981-33-6881-1_15
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