@INPROCEEDINGS{0780Kim2004, AUTHOR = {Hyoung-Gook Kim and Juan José Burred and Thomas Sikora}, TITLE = {How efficient is MPEG-7 for General Sound Recognition?}, BOOKTITLE = {25th International AES Conference Metadata for Audio}, YEAR = {2004}, MONTH = jun, ADDRESS = {London, UK}, PDF = {http://elvera.nue.tu-berlin.de/files/0780Kim2004.pdf}, URL = {http://elvera.nue.tu-berlin.de/files/0780Kim2004.pdf}, ABSTRACT = {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 several basis decomposition algorithms vs. Mel-scale Frequency Cepstrum Coefficients (MFCC). For basis decomposition in the feature extraction we evaluate three approaches: 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 a continuous hidden Markov model (CHMM) classifier. Our conclusion is that established MFCC features yield better performance compared to MPEG-7 ASP in the general sound recognition under practical constraints.} }