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Audio spectrum projection based on several basis decomposition algorithms applied to general sound recognition and audio segmentation

Our challenge is to analyze/classify video sound track content for indexing purposes. To this end we compare the performance of MPEG-7 Audio Spectrum Projection (ASP) features based on basis decomposition vs. Mel-scale Frequency Cepstrum Coefficients (MFCC). For basis decomposition in the feature extraction we have three choices: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Non-negative Matrix Factorization (NMF). Audio features are computed from these reduced vectors and are fed into hidden Markov model (HMM) classifier. Experimental results show that the MFCC features yield better performance compared to MPEG-7 ASP in the sound recognition, and audio segmentation.