7 accepted papers
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…
Speech separation algorithms are often used to separate the target speech from other interfering sources. However, purely neural network based speech separation systems often cause nonlinear distortion that is harmful for automatic speech recognition (ASR) systems. The conventional mask-based minimu…
Hand-crafted spatial features (e.g., inter-channel phase difference, IPD) play a fundamental role in recent deep learning based multi-channel speech separation (MCSS) methods. However, these manually designed spatial features are hard to incorporate into the end-to-end optimized MCSS framework. In t…
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicate…
The recent exploration of deep learning for supervised speech separation has significantly accelerated the progress on the multi-talker speech separation problem. Multi-channel extension has attracted much research attention due to the benefit of spatial information in far-field acoustic environment…