Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929418800201728 |
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| author | Ayano, Shoma Li, Li Seki, Shogo Kitamura, Daichi |
| author_facet | Ayano, Shoma Li, Li Seki, Shogo Kitamura, Daichi |
| contents | Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08951 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization Ayano, Shoma Li, Li Seki, Shogo Kitamura, Daichi Sound Audio and Speech Processing Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use. |
| title | Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.08951 |