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.

Authors: Kim, Hyoung-Gook; Sikora, Thomas
Affiliation: Communication Systems Group, Technical University of Berlin, Germany
AES Conference: 25th International Conference: Metadata for Audio (June 2004)
Paper Number: 5-2
Publication Date: June 1, 2004 Import into BibTeX
Subject: Metadata for Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=12814

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 25thPapers)   /25/aes25-000010.pdf

Start a discussion about this paper!