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Performance measurement in blind audio source separation
Open accessAbstract
In this paper, we discuss the evaluation of blind audio source separation (BASS) algorithms. Depending on the exact application, different distortions can be allowed between an estimated source and the wanted true source. We consider four different sets of such allowed distortions, from time-invariant gains to time-varying filters. In each case, we decompose the estimated source into a true source part plus error terms corresponding to interferences, additive noise, and algorithmic artifacts. Then, we derive a global performance measure using an energy ratio, plus a separate performance measure for each error term. These measures are computed and discussed on the results of several BASS problems with various difficulty levels
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Cited by
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Deep Learning for Audio Signal Processing
Purwins, H., Li, B., Virtanen, T., Schluter, J., Chang, S.-Y., & Sainath, T. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2908700 · Cited by 855
Kolbaek, M., Yu, D., Tan, Z.-H., & Jensen, J. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2726762 · Cited by 820
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Owens, A., & Efros, A. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_39 · Cited by 704
SpeechBrain: A General-Purpose Speech Toolkit
Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Dawalatabad, N., Heba, A., Zhong, J., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.04624 · Cited by 512
Metadata
- Authors
- Vincent, E., Gribonval, R., & Fevotte, C.
- Venue
- IEEE Transactions on Audio, Speech and Language Processing
- Published
- 2006-07
- Volume
- 14
- Issue
- 4
- Pages
- 1462-1469
- ISSN
- 1558-7916 (ISSN-L)
- OpenAlex
- W2127851351
- Cited by
- 2,889
- References
- 0
- FWCI
- 57.80
- Type
- journal article
- Language
- en
- Open access
- green
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