Audio musical genre classification using convolutional neural networks and pitch and tempo transformations

Abstract

Musical genre classification is a potential yet challenging task in the field of music information retrieval. As an important first step of any genre classification system,music feature extraction is a critical process that will drastically affect the final performance. In this thesis, we will try to address two important questions of the feature extraction stage: 1) is there any potential alternative techniques for musical feature extraction when traditional audio feature sets seem to meet their performance bottlenecks? 2) is the widely used MFCC feature purely a timbral feature set so that it is invariant to changes in musical key and tempo in the songs? To answer the first question, we propose a novel approach to extract musical pattern features in audio music using convolutional neural network (CNN), a model widely adopted in image information retrieval tasks. Our experiments show that CNN has strong capacity to capture informative features from the variations of musical patterns with minimal prior knowledge provided. To answer the second question, we investigate the invariance of MFCC to musical key and tempo, and show that MFCCs in fact encode both timbral and key information. We also show that musical genres, which should be independent of key, are in fact influenced by the fundamental keys of the instruments involved. As a result, genre classifiers based on the MFCC features will be influenced by the dominant keys of the genre, resulting in poor performance on songs in less common keys. We propose an approach to address this problem, which consists of augmenting classifier training and prediction with various key and tempo transformations of the songs. The resulting genre classifier is invariant to key, and thus more timbre-oriented, resulting in improved classification accuracy in our experiments.

Date of Award	4 Oct 2010
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Antoni Bert CHAN (Supervisor) & Hon Wai CHUN (Supervisor)