Abstract
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.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012 |
| Pages | 421-426 |
| Number of pages | 6 |
| DOIs | |
| State | Published - Dec 1 2012 |
| Externally published | Yes |
| Event | 14th IEEE International Symposium on Multimedia, ISM 2012 - Irvine, CA, United States Duration: Dec 10 2012 → Dec 12 2012 |
Other
| Other | 14th IEEE International Symposium on Multimedia, ISM 2012 |
|---|---|
| Country | United States |
| City | Irvine, CA |
| Period | 12/10/12 → 12/12/12 |
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Keywords
- Audio classification
- Gaussian Mixture Models
- Wavelets
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Software
Cite this
Using wavelets and Gaussian Mixture Models for audio classification. / Chuan, Ching-Hua; Vasana, Susan; Asaithambi, Asai.
Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012. 2012. p. 421-426 6424700.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
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
SN - 9780769548753
SP - 421
EP - 426
BT - Proceedings - 2012 IEEE International Symposium on Multimedia, ISM 2012
ER -