Our purpose is to evaluate the MPEG-7 Audio Spectrum Projection (ASP) features for general sound recognition performance vs. well established MFCC. The recognition tasks of interest are speaker recognition, sound classification, and segmentation of audio using sound/speaker identification. For the sound classification we use three approaches: the direct approach, the hierarchical approach without hints, and the hierarchical approach with hints. For audio segmentation the MPEG-7 ASP features and MFCCs are used to train hidden Markov models (HMM) for individual speakers and sounds. The trained sound/speaker models are then used to segment conversational speech involving a given subset of people in panel discussion television programs. Results show that MFCC approach yields sound/speaker recognition rate superior to MPEG-7 implementations.
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