Automatic Musical Pattern Feature Extraction Using Convolutional Neural Network

Music genre classification has been a challenging yet promising task in the field of music information retrieval (MIR). Due to the highly elu- sive characteristics of audio musical data, retrieving informative and reliable features from audio signals is crucial to the performance of any music genre classi- fication system. Previous work on audio music genre classification systems mainly concentrated on using timbral features, which limits the performance. To address this problem, 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 pro- vided. is adopted in image information retrieval tasks. Migrat- ing technologies from another research field brings new opportunities to break through the current bottleneck of music genre classification. The proposed musical pat- tern feature extractor has advantages in several aspects. It requires minimal prior knowledge to build up. Once obtained, the process of feature extraction is highly ef- ficient. These two advantages guarantee the scalability of our feature extractors. Moreover, our musical pattern features are complementary to other main-stream feature sets used in other classification systems. Our experiments show that musical data have very similar characteristics to image data so that the variation of musical patterns can be captured using CNN. We also show that the mu- sical pattern features are informative for genre classifica- tion tasks.