Music genre classification using RNN-LSTM approach
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Date
2021-02
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
In the present world of technology, computers and computational techniques play a very keen role in the life of each individual. Everyone is dependent on technology in some or another way, whether it be personal or professional dependability. In the era of Artificial Intelligence and Machine Learning, complex real-world problems are being solved by these evolving techniques. Now a days, the difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches. In this work, a self constructed dataset that consists of 6 classes with each class representing a sub-genre namely Abhang, Bhajan, Kajari, Qawwali, Tappa, and Thumri, that fall under the Indian semi-classical genre hierarchy. In this research work the above mentioned classes of music has been classified. The RNN-LSTM (Long-Short Term Memory) deep learning technique has been used here for classification. We have used STFT (Short-Term Fourier Transform) and DCT (Discrete Cosine Transform) to pre-process the music dataset. This research can be helpful in developing an automatic music recommendation module of online music applications, for increasing the browsing functionality of the music platform.