17 citations · 62 across the 37 of their papers we have counts for
5 papers · 2 filters
Optimal Condition Training for Target Source Separation
Efthymios Tzinis, Gordon Wichern, Paris Smaragdis +1
Recent research has shown remarkable performance in leveraging multiple extraneous conditional and non-mutually exclusive semantic concepts for sound source separation, allowing th…
Latent Iterative Refinement for Modular Source Separation
Dimitrios Bralios, Efthymios Tzinis, Gordon Wichern +2
Traditional source separation approaches train deep neural network models end-to-end with all the data available at once by minimizing the empirical risk on the whole training set.…
Heterogeneous Target Speech Separation
Efthymios Tzinis, Gordon Wichern, Aswin Subramanian +2
We introduce a new paradigm for single-channel target source separation where the sources of interest can be distinguished using non-mutually exclusive concepts (e.g., loudness, ge…
STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency
Zhong-Qiu Wang, Gordon Wichern, Shinji Watanabe +1
Deep learning based speech enhancement in the short-time Fourier transform (STFT) domain typically uses a large window length such as 32 ms. A larger window can lead to higher freq…
Extended Graph Temporal Classification for Multi-Speaker End-to-End ASR
Xuankai Chang, Niko Moritz, Takaaki Hori +2
Graph-based temporal classification (GTC), a generalized form of the connectionist temporal classification loss, was recently proposed to improve automatic speech recognition (ASR)…