12 citations · 19 across the 4 of their papers we have counts for
13 papers
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…
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…
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…
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision
Yun-Ning Hung, Gordon Wichern, Jonathan Le Roux
Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain. In this work, we use musical scores, which are co…
AutoClip: Adaptive Gradient Clipping for Source Separation Networks
Prem Seetharaman, Gordon Wichern, Bryan Pardo +1
Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method fo…
Finding Strength in Weakness: Learning to Separate Sounds with Weak Supervision
Fatemeh Pishdadian, Gordon Wichern, Jonathan Le Roux
While there has been much recent progress using deep learning techniques to separate speech and music audio signals, these systems typically require large collections of isolated s…