Automatic Music Genre Classification Using a Hierarchical Clustering and a Language Model Approach

Automatic music genre classification has received a lot of attention from the Music Information Retrieval (MIR) community in the past years. Systems capable of discriminating music genres are essential for managing music databases. This paper presents a method for music genre classification based solely on the audio contents of the signal. The method relies on a language modeling approach and takes in account the temporal information of the music signals for genre classification. First, the music data is transformed into a sequence of symbols, and a model is derived for each genre by estimating n-grams from the training data. As a term o comparison, HMMs models for each musical genre were also implemented. Tests on different audio sets show that the proposed approach performs very well, and outperforms HMMs based methods.

[1]  W. Bruce Croft,et al.  A language modeling approach to information retrieval , 1998, SIGIR '98.

[2]  Hagen Soltau,et al.  Recognition of music types , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[3]  Beth Logan,et al.  A music similarity function based on signal analysis , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[4]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[5]  François Pachet,et al.  Improving Timbre Similarity : How high’s the sky ? , 2004 .

[6]  Daniel P. W. Ellis,et al.  A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures , 2004, Computer Music Journal.

[7]  Ming Li,et al.  A robust approach to sequence classification , 2005, 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05).

[8]  Stephen Cox,et al.  Finding An Optimal Segmentation for Audio Genre Classification , 2005, ISMIR.

[9]  Gerhard Widmer,et al.  Improvements of Audio-Based Music Similarity and Genre Classificaton , 2005, ISMIR.

[10]  Andreas Rauber,et al.  Evaluation of Feature Extractors and Psycho-Acoustic Transformations for Music Genre Classification , 2005, ISMIR.

[11]  Douglas Eck,et al.  Aggregate features and ADABOOST for music classification , 2006, Machine Learning.

[12]  Sheng Gao,et al.  Music Genres Classification using Text Categorization Method , 2006, 2006 IEEE Workshop on Multimedia Signal Processing.

[13]  Roberto Basili,et al.  Audio Feature Engineering for Automatic Music Genre Classification , 2007, RIAO.

[14]  François Pachet,et al.  The influence of polyphony on the dynamical modelling of musical timbre , 2007, Pattern Recognit. Lett..