Phase Aware Ear-Conditioned Learning for Multi-Channel Binaural Speaker Separation
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908589436698624 |
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| author | Jeremiah, Ruben Johnson Robert Goli, Peyman van de Par, Steven |
| author_facet | Jeremiah, Ruben Johnson Robert Goli, Peyman van de Par, Steven |
| contents | Separating competing speech in reverberant environments requires models that preserve spatial cues while maintaining separation efficiency. We present a Phase-aware Ear-conditioned speaker Separation network using eight microphones (PEASE-8) that consumes complex STFTs and directly introduces a raw-STFT input to the early decoder layer, bypassing the entire encoder pathway to improve reconstruction. The model is trained end-to-end with an SI-SDR-based objective against direct-path ear targets, jointly performing separation and dereverberation for two speakers in a fixed azimuth, eliminating the need for permutation invariant training. On spatialized two-speaker mixtures spanning anechoic, reverberant, and noisy conditions, PEASE-8 delivers strong separation and intelligibility. In reverberant environments, it achieves 12.37 dB SI-SDR, 0.87 STOI, and 1.86 PESQ at T60 = 0.6 s, while remaining competitive under anechoic conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phase Aware Ear-Conditioned Learning for Multi-Channel Binaural Speaker Separation Jeremiah, Ruben Johnson Robert Goli, Peyman van de Par, Steven Audio and Speech Processing Separating competing speech in reverberant environments requires models that preserve spatial cues while maintaining separation efficiency. We present a Phase-aware Ear-conditioned speaker Separation network using eight microphones (PEASE-8) that consumes complex STFTs and directly introduces a raw-STFT input to the early decoder layer, bypassing the entire encoder pathway to improve reconstruction. The model is trained end-to-end with an SI-SDR-based objective against direct-path ear targets, jointly performing separation and dereverberation for two speakers in a fixed azimuth, eliminating the need for permutation invariant training. On spatialized two-speaker mixtures spanning anechoic, reverberant, and noisy conditions, PEASE-8 delivers strong separation and intelligibility. In reverberant environments, it achieves 12.37 dB SI-SDR, 0.87 STOI, and 1.86 PESQ at T60 = 0.6 s, while remaining competitive under anechoic conditions. |
| title | Phase Aware Ear-Conditioned Learning for Multi-Channel Binaural Speaker Separation |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.11366 |