4 accepted papers
This paper describes our submission to the fourth Acoustic Echo Cancellation (AEC) Challenge, which is part of ICASSP 2023 Signal Processing Grand Challenge. The proposed system is developed based on our earlier system submitted to the ICASSP 2022 AEC challenge with significant latency and network s…
Deep learning based wideband (16kHz) acoustic echo cancellation (AEC) approaches have surpassed traditional methods. This work proposes a deep hierarchical fusion (DHF) network with intra-network and inter-network fusion to further improve the wideband AEC performance. Meanwhile, this work extends t…
Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fullband (48kHz) speech enhancement system is proposed in this paper. Compared to the existing full-band systems that util…
Deep learning-based wideband (16kHz) speech enhancement approaches have surpassed traditional methods. This work further extends the existing wideband systems to enable full-band (48kHz) speech enhancement while simultaneously ensuring automatic speech recognition compatibility and optionally, perso…