Name
Title | ||
|---|---|---|
Automatic detection of malicious sound using segmental two-dimensional mel-frequency cepstral coefficients and histograms of oriented gradients |
Abstract | ||
|---|---|---|
This paper addresses the problem of recognizing malicious sounds, such as sexual scream or moan, to detect and block the objectionable multimedia contents. The malicious sounds show the distinct characteristics that have large temporal variations and fast spectral transitions. Therefore, extracting appropriate features to properly represent these characteristics is important in achieving a better performance. In this paper, we employ segment-based two-dimensional Mel-frequency cepstral coefficients and histograms of gradient directions as a feature set to characterize both the temporal variations and spectral transitions within a long-range segment of the target signal. Gaussian mixture model (GMM) is adopted to statistically represent the malicious and non-malicious sounds, and the test sounds are classified by a maximum a posterior probability (MAP) method. Evaluation of the proposed feature extraction method on a database of several hundred malicious and non-malicious sound clips yielded precision of 91.31% and recall of 94.27%. This result suggests that this approach could be used as an alternative to the image-based methods. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1873951.1874104 | ACM Multimedia 2001 |
Keywords | Field | DocType |
spectral transition,malicious sound,automatic detection,gaussian mixture model,oriented gradient,large temporal variation,image-based method,non-malicious sound,proposed feature extraction method,non-malicious sound clip,appropriate feature,segmental two-dimensional mel-frequency cepstral,temporal variation,feature extraction,mel frequency cepstral coefficient | Mel-frequency cepstrum,Histogram,Pattern recognition,Computer science,Feature extraction,Speech recognition,Posterior probability,Feature set,Artificial intelligence,Mixture model | Conference |
Citations | PageRank | References |
2 | 0.47 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Myung Jong Kim | 1 | 31 | 6.30 |
| Younggwan Kim | 2 | 17 | 6.11 |
| Jae-Deok Lim | 3 | 5 | 3.49 |
| Hoi-Rin Kim | 4 | 102 | 20.64 |