104 citations · 200 across the 24 of their papers we have counts for
24 papers
Boosting Unknown-number Speaker Separation with Transformer Decoder-based Attractor
Younglo Lee, Shukjae Choi, Byeong-Yeol Kim +2
We propose a novel speech separation model designed to separate mixtures with an unknown number of speakers. The proposed model stacks 1) a dual-path processing block that can mode…
A Single Speech Enhancement Model Unifying Dereverberation, Denoising, Speaker Counting, Separation, and Extraction
Kohei Saijo, Wangyou Zhang, Zhong-Qiu Wang +3
We propose a multi-task universal speech enhancement (MUSE) model that can perform five speech enhancement (SE) tasks: dereverberation, denoising, speech separation (SS), target sp…
The Multimodal Information Based Speech Processing (MISP) 2023 Challenge: Audio-Visual Target Speaker Extraction
Shilong Wu, Chenxi Wang, Hang Chen +13
Previous Multimodal Information based Speech Processing (MISP) challenges mainly focused on audio-visual speech recognition (AVSR) with commendable success. However, the most advan…
Toward Universal Speech Enhancement for Diverse Input Conditions
Wangyou Zhang, Kohei Saijo, Zhong-Qiu Wang +2
The past decade has witnessed substantial growth of data-driven speech enhancement (SE) techniques thanks to deep learning. While existing approaches have shown impressive performa…
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
Samuele Cornell, Matthew Wiesner, Shinji Watanabe +8
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR (DA…
UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures
Zhong-Qiu Wang, Shinji Watanabe
In reverberant conditions with multiple concurrent speakers, each microphone acquires a mixture signal of multiple speakers at a different location. In over-determined conditions w…
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…
TF-GridNet: Making Time-Frequency Domain Models Great Again for Monaural Speaker Separation
Zhong-Qiu Wang, Samuele Cornell, Shukjae Choi +3
We propose TF-GridNet, a novel multi-path deep neural network (DNN) operating in the time-frequency (T-F) domain, for monaural talker-independent speaker separation in anechoic con…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
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…
Improving Frame-Online Neural Speech Enhancement with Overlapped-Frame Prediction
Zhong-Qiu Wang, Shinji Watanabe
Frame-online speech enhancement systems in the short-time Fourier transform (STFT) domain usually have an algorithmic latency equal to the window size due to the use of overlap-add…
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…
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE Submission to The L3DAS22 Challenge
Yen-Ju Lu, Samuele Cornell, Xuankai Chang +5
This paper describes our submission to the L3DAS22 Challenge Task 1, which consists of speech enhancement with 3D Ambisonic microphones. The core of our approach combines Deep Neur…
The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks
Darius Petermann, Gordon Wichern, Zhong-Qiu Wang +1
The cocktail party problem aims at isolating any source of interest within a complex acoustic scene, and has long inspired audio source separation research. Recent efforts have mai…
Leveraging Low-Distortion Target Estimates for Improved Speech Enhancement
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
A promising approach for multi-microphone speech separation involves two deep neural networks (DNN), where the predicted target speech from the first DNN is used to compute signal…
On The Compensation Between Magnitude and Phase in Speech Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
Deep neural network (DNN) based end-to-end optimization in the complex time-frequency (T-F) domain or time domain has shown considerable potential in monaural speech separation. Ma…
Convolutive Prediction for Reverberant Speech Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
We investigate the effectiveness of convolutive prediction, a novel formulation of linear prediction for speech dereverberation, for speaker separation in reverberant conditions. T…
Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation
Zhong-Qiu Wang, Gordon Wichern, Jonathan Le Roux
A promising approach for speech dereverberation is based on supervised learning, where a deep neural network (DNN) is trained to predict the direct sound from noisy-reverberant spe…
Localization Based Sequential Grouping for Continuous Speech Separation
Zhong-Qiu Wang, DeLiang Wang
This study investigates robust speaker localization for con-tinuous speech separation and speaker diarization, where we use speaker directions to group non-contiguous segments of t…
Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation
Zhong-Qiu Wang, Peidong Wang, DeLiang Wang
We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditio…
Multi-Microphone Complex Spectral Mapping for Speech Dereverberation
Zhong-Qiu Wang, DeLiang Wang
This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN)…
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
Zhong-Qiu Wang, Hakan Erdogan, Scott Wisdom +5
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation. Our neural networks…
Deep Learning Based Phase Reconstruction for Speaker Separation: A Trigonometric Perspective
Zhong-Qiu Wang, Ke Tan, DeLiang Wang
This study investigates phase reconstruction for deep learning based monaural talker-independent speaker separation in the short-time Fourier transform (STFT) domain. The key obser…
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Zhong-Qiu Wang, Jonathan Le Roux, DeLiang Wang +1
This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transf…