| Paper: | AE-P5.12 | ||
| Session: | Applications to Music II | ||
| Time: | Friday, May 21, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Audio and Electroacoustics: Applications to Music | ||
| Title: | A DYNAMIC PROGRAMMING APPROACH TO AUDIO SEGMENTATION AND SPEECH/MUSIC DISCRIMINATION | ||
| Authors: | Michael Goodwin; Creative ATC | ||
| Jean Laroche; Creative ATC | |||
| Abstract: | We consider the problem of segmenting an audio signal into characteristic regions based on feature-set similarities. In the proposed approach, a feature-space representation of the signal is generated; sequences of these feature-space samples are then aggregated into clusters corresponding to distinct signal regions. The algorithm consists of using linear discriminant analysis (LDA) to condition the feature space and dynamic programming (DP) to identify data clusters. In this paper, we consider the design of the dynamic program cost functions; we are able to derive effective cost functions without relying on significant prior information about the structure of the expected data clusters. We demonstrate the application of the LDA-DP segmentation algorithm to speech / music discrimination; experimental results are given and discussed. | ||
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