Publication Date: 2023/05/19
Abstract: Music genre classification is one example of content-based analysis of music signals. Historically, human-engineered features were employed to automate this process, and in the 10-genre classification, 61% accuracy was attained. Even yet, it falls short of the 70% accuracy that humans are capable of in the identical activity. Here, we suggest a novel approach that combines understanding of the neurophysiology of the auditory system with research on human perception in the classification of musical genres. The technique involves training a straightforward convolutional neural network (CNN) to categorise a brief portion of the music input. The genre of the song is then identified by breaking it up into manageable chunks and combining CNN's predictions from each individual chunk. The filters learned in the CNN match the Spectro temporal receptual field (STRF) in humans, and after training, this approach reaches human-level (70%) accuracy.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.7950988
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23MAY473.pdf
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