The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A robust audio classification and segmentation method
Abstract
In this paper, we present a robust algorithm for audio classification that is capable of segmenting and classifying an audio stream into speech, music, environment sound and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and non-speech discrimination. In this step, a novel algorithm based on KNN and LSP VQ is presented. The second step further divides non-speech class into music, environment sounds and silence with a rule based classification scheme. Some new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. Our experiments in the context of video structure parsing have shown the algorithms produce very satisfactory results.
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Cites
An Algorithm for Vector Quantizer Design
Linde, Y., Buzo, A., & Gray, R. (1980) · IEEE Transactions on Communications · doi:10.1109/tcom.1980.1094577 · Cited by 7,201
Cited by
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Content analysis for audio classification and segmentation
Lu, L., Zhang, H.-J., & Jiang, H. (2002) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2002.804546 · Cited by 508
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Metadata
- Authors
- Lu, L., Jiang, H., & Zhang, H.
- Venue
- Proceedings of the ninth ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2001-10
- Pages
- 203-211
- OpenAlex
- W2110215948
- Cited by
- 202
- References
- 1
- FWCI
- 15.81
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lu, L., Jiang, H., & Zhang, H. (2001). A robust audio classification and segmentation method. Proceedings of the ninth ACM international conference on Multimedia. https://doi.org/10.1145/500141.500173
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