The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Content analysis for audio classification and segmentation
Abstract
We present our study of audio content analysis for classification and segmentation, in which an audio stream is segmented according to audio type or speaker identity. We propose a robust approach that is capable of classifying and segmenting 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 nonspeech discrimination. In this step, a novel algorithm based on K-nearest-neighbor (KNN) and linear spectral pairs-vector quantization (LSP-VQ) is developed. The second step further divides nonspeech class into music, environment sounds, and silence with a rule-based classification scheme. A set of new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. We also develop an unsupervised speaker segmentation algorithm using a novel scheme based on quasi-GMM and LSP correlation analysis. Without a priori knowledge, this algorithm can support the open-set speaker, online speaker modeling and real time segmentation. Experimental results indicate that the proposed algorithms can produce very satisfactory results.
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Cites
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Content-based classification, search, and retrieval of audio
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A robust audio classification and segmentation method
Lu, L., Jiang, H., & Zhang, H. (2001) · Proceedings of the ninth ACM international conference on Multimedia · doi:10.1145/500141.500173 · Cited by 202
Self-citationA hidden Markov model framework for video segmentation using audio and image features
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Presence versus availability: the design and evaluation of a context-aware communication client
Fogarty, J., Lai, J., & Christensen, J. (2004) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2003.12.016 · Cited by 152
A deep learning framework for audio deepfake detection
Khochare, J., Joshi, C., Yenarkar, B., Suratkar, S., & Kazi, F. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06297-w · Cited by 135
Toward User-Driven Sound Recognizer Personalization with People Who Are d/Deaf or Hard of Hearing
Goodman, S. M., Liu, P., Jain, D., McDonnell, E. J., Froehlich, J. E., & Findlater, L. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3463501 · Cited by 33
Zhang, A., Pyon, R. E., Chen, K., & Lin, A. Y. (2023) · FACE · doi:10.1177/27325016231187985 · Cited by 16
Metadata
- Authors
- Lu, L., Zhang, H.-J., & Jiang, H.
- Venue
- IEEE Transactions on Speech and Audio Processing
- Published
- 2002-10
- Volume
- 10
- Issue
- 7
- Pages
- 504-516
- ISSN
- 1063-6676 (ISSN-L)
- OpenAlex
- W2131981197
- Cited by
- 508
- References
- 11
- FWCI
- 30.00
- Type
- journal article
- Language
- en
- Open access
- closed
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