Evaluation of Feature Extractors and Psycho-Acoustic Transformations for Music Genre Classification

We present a study on the importance of psycho-acoustic transformations for effective audio feature calculation. From the results, both crucial and problematic parts of the algorithm for Rhythm Patterns feature extraction are identified. We furthermore introduce two new feature representations in this context: Statistical Spectrum Descriptors and Rhythm Histogram features. Evaluation on both the individual and combined feature sets is accomplished through a music genre classification task, involving 3 reference audio collections. Results are compared to published measures on the same data sets. Experiments confirmed that in all settings the inclusion of psycho-acoustic transformations provides significant improvement of classification accuracy.

• Type: Conference
• Year: 2005
• Where: ISMIR
• Authors: Thomas Lidy, Andreas Rauber
• Keywords: Effective Audio Feature, Information Retrieval, ISMIR 2005, Patterns Feature Extraction, Psycho-acoustic Transformations