Feature extraction for musical instrument recognition with application to music segmentation.

Wan-chi Lee; C.-C. Jay Kuo

doi:10.1117/12.634225

24 October 2005 Feature extraction for musical instrument recognition with application to music segmentation

Wan-chi Lee, C.-C. Jay Kuo

Proceedings Volume 6015, Multimedia Systems and Applications VIII; 601518 (2005) https://doi.org/10.1117/12.634225
Event: Optics East 2005, 2005, Boston, MA, United States

Abstract

The problem of musical instrument recognition is investigated and its application to music segmentation is examined in this research. We propose a new framework for extracting features, in which audio frames are not placed uniformly as done traditionally. The equal spacing method inevitably leads to the consequence that some frames may contain a transition between notes and/or two notes may be included in one frame. Then, a music frame may consist of sounds from multiple instruments. Onset detection is integrated with frame location selection in this work to mitigate this phenomenon. This new framing scheme, called onsetaware framing scheme, provides comparable or better performance as compared with traditional methods. A new histogram-based feature is also presented and used with other common features in the musical instrument classification task. Feature reduction is adopted to reduce the dimensionality of the feature space. We conduct experiments on data sets of a different size with both synthesized and real musical signals. Finally, a simple segmentation method based on the musical instrument classification is proposed and demonstrated by an example.

Citation Download Citation

Wan-chi Lee and C.-C. Jay Kuo "Feature extraction for musical instrument recognition with application to music segmentation", Proc. SPIE 6015, Multimedia Systems and Applications VIII, 601518 (24 October 2005); https://doi.org/10.1117/12.634225

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KEYWORDS

Feature extraction

Databases

Feature selection

Image segmentation

Filtering (signal processing)

Detection and tracking algorithms

Signal detection

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