A novel NMF-HMM speech enhancement algorithm based on Poisson mixture model
| Autor: | Yang Xiang, Liming Shi, Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Mads Grasboll Christensen |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
business.industry Speech enhancement Pattern recognition Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing) Intelligibility (communication) Poisson distribution Mixture model Poisson mixture model (PMM) Non-negative matrix factorization symbols.namesake Minimum mean-square error (MMSE) Non-negative matrix factorization (NMF) Computer Science::Sound symbols Mixture distribution Artificial intelligence Hidden Markov model business Hidden Markov model (HMM) PESQ |
| Zdroj: | Xiang, Y, Shi, L, Lisby Højvang, J, Højfeldt Rasmussen, M & Christensen, M G 2021, A novel NMF-HMM speech enhancement algorithm based on Poisson mixture model . in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ., 9414620, IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, pp. 721-725, ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada, 06/06/2021 . https://doi.org/10.1109/ICASSP39728.2021.9414620 ICASSP |
| DOI: | 10.1109/ICASSP39728.2021.9414620 |
| Popis: | In this paper, we propose a novel non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM) based speech enhancement algorithm, which employs a Poisson mixture model (PMM). {Compared to} the previously proposed NMF-HMM method, the new algorithm, termed PMM-NMF-HMM, {uses} the Poisson mixture distribution for the state conditional likelihood function for a HMM rather than the single Poisson distribution. {This means that there are the more basis matrices that can be used to model the speech and noise signals, so more signal information can be captured by the resulting model. The proposed method is supervised and thus includes a training and an enhancement stage. It is shown that, in the training stage, the proposed method can be implemented efficiently using multiplicative update (MU) for the model parameters, much like the NMF-HMM algorithm. In the speech enhancement stage, which can be performed online, a novel PMM-NMF-HMM minimum mean-square error (MMSE) estimator is developed. The experimental results indicate that the PMM-NMF-HMM method can obtain higher short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) score than NMF-HMM. Additionally, the {method also outperforms other state-of-the-art NMF-based supervised speech enhancement algorithms. |
| Databáze: | OpenAIRE |
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