Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918168392368128 |
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| author | Southwell, Benjamin J Ho, Yin-Lee Gunawan, David |
| author_facet | Southwell, Benjamin J Ho, Yin-Lee Gunawan, David |
| contents | This paper introduces a novel approach for acoustic scene analysis by exploiting an ensemble of statistics extracted from a sub-band domain multi-hypothesis acoustic echo canceler (SDMH-AEC). A well-designed SDMH-AEC employs multiple adaptive filtering strategies with potentially complementary behaviours during convergence, perturbations, and steady-state conditions. By aggregating statistics across the sub-bands, we derive a feature vector that exhibits strong discriminative power for distinguishing different acoustic events and estimating acoustic parameters. The complementary nature of the SDMH-AEC filters provides a rich source of information that can be extracted at insignificant cost for acoustic scene analysis tasks. We demonstrate the effectiveness of the proposed approach experimentally with real data containing double-talk, echo path change and events where the full-duplex device is physically moved. The extracted features enable acoustic scene analysis using existing echo cancellation algorithms and techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_05652 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis Southwell, Benjamin J Ho, Yin-Lee Gunawan, David Audio and Speech Processing This paper introduces a novel approach for acoustic scene analysis by exploiting an ensemble of statistics extracted from a sub-band domain multi-hypothesis acoustic echo canceler (SDMH-AEC). A well-designed SDMH-AEC employs multiple adaptive filtering strategies with potentially complementary behaviours during convergence, perturbations, and steady-state conditions. By aggregating statistics across the sub-bands, we derive a feature vector that exhibits strong discriminative power for distinguishing different acoustic events and estimating acoustic parameters. The complementary nature of the SDMH-AEC filters provides a rich source of information that can be extracted at insignificant cost for acoustic scene analysis tasks. We demonstrate the effectiveness of the proposed approach experimentally with real data containing double-talk, echo path change and events where the full-duplex device is physically moved. The extracted features enable acoustic scene analysis using existing echo cancellation algorithms and techniques. |
| title | Sub-band Domain Multi-Hypothesis Acoustic Echo Canceler Based Acoustic Scene Analysis |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2501.05652 |