TY - GEN
T1 - Using wavelets and Gaussian Mixture Models for audio classification
AU - Chuan, Ching Hua
AU - Vasana, Susan
AU - Asaithambi, Asai
PY - 2012/12/1
Y1 - 2012/12/1
N2 - In this paper, we present an audio classification system using wavelets for extracting low-level acoustic features. We perform multiple-level decomposition using Discrete Wavelet Transform to extract acoustic features at different scales and time from audio recordings. The extracted features are then translated into a compact vector representation. Gaussian Mixture Models with Expectation Maximization algorithm are then used to build models for sound classes. Specifically, three types of audio classification tasks are designed to evaluate the system, including speech/music classification, male/female speech classification, and music genre (classical, pop, jazz, and electronic) classification. By evaluating the system through 5-fold cross validation, the experimental result shows the promising capability of wavelets for speech and music analyses.
AB - In this paper, we present an audio classification system using wavelets for extracting low-level acoustic features. We perform multiple-level decomposition using Discrete Wavelet Transform to extract acoustic features at different scales and time from audio recordings. The extracted features are then translated into a compact vector representation. Gaussian Mixture Models with Expectation Maximization algorithm are then used to build models for sound classes. Specifically, three types of audio classification tasks are designed to evaluate the system, including speech/music classification, male/female speech classification, and music genre (classical, pop, jazz, and electronic) classification. By evaluating the system through 5-fold cross validation, the experimental result shows the promising capability of wavelets for speech and music analyses.
KW - Audio classification
KW - Gaussian Mixture Models
KW - Wavelets
UR - http://www.scopus.com/inward/record.url?scp=84874231378&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874231378&partnerID=8YFLogxK
U2 - 10.1109/ISM.2012.86
DO - 10.1109/ISM.2012.86
M3 - Conference contribution
AN - SCOPUS:84874231378
SN - 9780769548753
T3 - Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012
SP - 421
EP - 426
BT - Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012
T2 - 14th IEEE International Symposium on Multimedia, ISM 2012
Y2 - 10 December 2012 through 12 December 2012
ER -