Article Title：Instrument recognition in polyphonic music based on automatic taxonomies
Abstract：
We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it,does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances,. we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53%.
Keywords: hierarchical taxonomy; instrument recognition; machine learning; pairwise classification; pairwise feature selection; polyphonic music; probabilistic distances; support vector machines
DOI: 10.1109/TSA.2005.860351
Source：IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Welcome to correct the error, please contact email: humanisticspider@gmail.com
