Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification

2018-02-27 · Mingwen Dong

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by training a simple convolutional neural network (CNN) to classify a short segment of the music signal. Then, the genre of a music is determined by splitting it into short segments and then combining CNN's predictions from all short segments. After training, this method achieves human-level (70%) accuracy and the filters learned in the CNN resemble the spectrotemporal receptive field (STRF) in the auditory system.

📄 PDF Abstract BibTeX arXiv:1802.09697

Code (2)

ds7711/music_genre_classification 공식 구현 tf

belqiliass/Music-Classifier

Tasks

Classification General Classification Genre classificationMusic Genre Classification

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