17 citations · 65 across the 38 of their papers we have counts for
10 papers · 1 filter
Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks
Darius Petermann, Gordon Wichern, Aswin Shanmugam Subramanian +2
Emulating the human ability to solve the cocktail party problem, i.e., focus on a source of interest in a complex acoustic scene, is a long standing goal of audio source separation…
Reverberation as Supervision for Speech Separation
Rohith Aralikatti, Christoph Boeddeker, Gordon Wichern +2
This paper proposes reverberation as supervision (RAS), a novel unsupervised loss function for single-channel reverberant speech separation. Prior methods for unsupervised separati…
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.…
Cold Diffusion for Speech Enhancement
Hao Yen, François G. Germain, Gordon Wichern +1
Diffusion models have recently shown promising results for difficult enhancement tasks such as the conditional and unconditional restoration of natural images and audio signals. In…
SANE-TTS: Stable And Natural End-to-End Multilingual Text-to-Speech
Hyunjae Cho, Wonbin Jung, Junhyeok Lee +1
In this paper, we present SANE-TTS, a stable and natural end-to-end multilingual TTS model. By the difficulty of obtaining multilingual corpus for given speaker, training multiling…
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…
Locate This, Not That: Class-Conditioned Sound Event DOA Estimation
Olga Slizovskaia, Gordon Wichern, Zhong-Qiu Wang +1
Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant. In this paper, we propos…
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)…