{"ID":2897692,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.05399","arxiv_id":"2507.05399","title":"Sample Rate Offset Compensated Acoustic Echo Cancellation For Multi-Device Scenarios","abstract":"Acoustic echo cancellation (AEC) in multi-device scenarios is a challenging problem due to sample rate offset (SRO) between devices. The SRO hinders the convergence of the AEC filter, diminishing its performance. To address this , we approach the multi-device AEC scenario as a multi-channel AEC problem involving a multi-channel Kalman filter, SRO estimation, and resampling of far-end signals. Experiments in a two-device scenario show that our system mitigates the divergence of the multi-channel Kalman filter in the presence of SRO for both correlated and uncorrelated playback signals during echo-only and double-talk. Additionally, for devices with correlated playback signals, an independent single-channel AEC filter is crucial to ensure fast convergence of SRO estimation.","short_abstract":"Acoustic echo cancellation (AEC) in multi-device scenarios is a challenging problem due to sample rate offset (SRO) between devices. The SRO hinders the convergence of the AEC filter, diminishing its performance. To address this , we approach the multi-device AEC scenario as a multi-channel AEC problem involving a mult...","url_abs":"https://arxiv.org/abs/2507.05399","url_pdf":"https://arxiv.org/pdf/2507.05399v1","authors":"[\"Srikanth Korse\",\"Oliver Thiergart\",\"Emanuel A. P. Habets\"]","published":"2025-07-07T18:33:09Z","proceeding":"eess.AS","tasks":"[\"eess.AS\"]","methods":"[]","has_code":false}