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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.09350 |
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| _version_ | 1866915386042089472 |
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| author | Middelberg, Wiebke Lee, Jung-Suk Sereshki, Saeed Bagheri Aroudi, Ali Tourbabin, Vladimir Wong, Daniel D. E. |
| author_facet | Middelberg, Wiebke Lee, Jung-Suk Sereshki, Saeed Bagheri Aroudi, Ali Tourbabin, Vladimir Wong, Daniel D. E. |
| contents | Enhancing the user's own-voice for head-worn microphone arrays is an important task in noisy environments to allow for easier speech communication and user-device interaction. However, a rarely addressed challenge is the change of the microphones' transfer functions when one or more of the microphones gets occluded by skin, clothes or hair. The underlying problem for beamforming-based speech enhancement is the (potentially rapidly) changing transfer functions of both the own-voice and the noise component that have to be accounted for to achieve optimal performance. In this paper, we address the problem of an occluded microphone in a head-worn microphone array. We investigate three alternative mitigation approaches by means of (i) conventional adaptive beamforming, (ii) switching between a-priori estimates of the beamformer coefficients for the occluded and unoccluded state, and (iii) a hybrid approach using a switching-adaptive beamformer. In an evaluation with real-world recordings and simulated occlusion, we demonstrate the advantages of the different approaches in terms of noise reduction, own-voice distortion and robustness against voice activity detection errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Microphone Occlusion Mitigation for Own-Voice Enhancement in Head-Worn Microphone Arrays Using Switching-Adaptive Beamforming Middelberg, Wiebke Lee, Jung-Suk Sereshki, Saeed Bagheri Aroudi, Ali Tourbabin, Vladimir Wong, Daniel D. E. Audio and Speech Processing Enhancing the user's own-voice for head-worn microphone arrays is an important task in noisy environments to allow for easier speech communication and user-device interaction. However, a rarely addressed challenge is the change of the microphones' transfer functions when one or more of the microphones gets occluded by skin, clothes or hair. The underlying problem for beamforming-based speech enhancement is the (potentially rapidly) changing transfer functions of both the own-voice and the noise component that have to be accounted for to achieve optimal performance. In this paper, we address the problem of an occluded microphone in a head-worn microphone array. We investigate three alternative mitigation approaches by means of (i) conventional adaptive beamforming, (ii) switching between a-priori estimates of the beamformer coefficients for the occluded and unoccluded state, and (iii) a hybrid approach using a switching-adaptive beamformer. In an evaluation with real-world recordings and simulated occlusion, we demonstrate the advantages of the different approaches in terms of noise reduction, own-voice distortion and robustness against voice activity detection errors. |
| title | Microphone Occlusion Mitigation for Own-Voice Enhancement in Head-Worn Microphone Arrays Using Switching-Adaptive Beamforming |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.09350 |