14 citations · 21 across the 3 of their papers we have counts for
6 papers
Improving Sound Event Detection In Domestic Environments Using Sound Separation
Nicolas Turpault, Scott Wisdom, Hakan Erdogan +5
Performing sound event detection on real-world recordings often implies dealing with overlapping target sound events and non-target sounds, also referred to as interference or nois…
Universal Sound Separation
Ilya Kavalerov, Scott Wisdom, Hakan Erdogan +4
Recent deep learning approaches have achieved impressive performance on speech enhancement and separation tasks. However, these approaches have not been investigated for separating…
Transfer Learning From Sound Representations For Anger Detection in Speech
Mohamed Ezzeldin A. ElShaer, Scott Wisdom, Taniya Mishra
In this work, we train fully convolutional networks to detect anger in speech. Since training these deep architectures requires large amounts of data and the size of emotion datase…
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Scott Wisdom, John R. Hershey, Kevin Wilson +4
In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement system…
SDR - half-baked or well done?
Jonathan Le Roux, Scott Wisdom, Hakan Erdogan +1
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was develop…
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
Scott Wisdom, Thomas Powers, James Pitton +1
In this paper, we propose a novel recurrent neural network architecture for speech separation. This architecture is constructed by unfolding the iterations of a sequential iterativ…