91 citations · 232 across the 11 of their papers we have counts for
13 papers
Unsupervised Multi-channel Separation and Adaptation
Cong Han, Kevin Wilson, Scott Wisdom +1
A key challenge in machine learning is to generalize from training data to an application domain of interest. This work generalizes the recently-proposed mixture invariant training…
Distance-Based Sound Separation
Katharine Patterson, Kevin Wilson, Scott Wisdom +1
We propose the novel task of distance-based sound separation, where sounds are separated based only on their distance from a single microphone. In the context of assisted listening…
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
Soumi Maiti, Hakan Erdogan, Kevin Wilson +3
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling spe…
VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition
Quan Wang, Ignacio Lopez Moreno, Mert Saglam +8
We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speec…
Unsupervised Sound Separation Using Mixture Invariant Training
Scott Wisdom, Efthymios Tzinis, Hakan Erdogan +3
In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a…
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…
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…
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…
Exploring Tradeoffs in Models for Low-latency Speech Enhancement
Kevin Wilson, Michael Chinen, Jeremy Thorpe +5
We explore a variety of neural networks configurations for one- and two-channel spectrogram-mask-based speech enhancement. Our best model improves on previous state-of-the-art perf…
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
Quan Wang, Hannah Muckenhirn, Kevin Wilson +7
In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker. We ac…
AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies
Sourish Chaudhuri, Joseph Roth, Daniel P. W. Ellis +8
Speech activity detection (or endpointing) is an important processing step for applications such as speech recognition, language identification and speaker diarization. Both audio-…
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech
Brian Patton, Yannis Agiomyrgiannakis, Michael Terry +3
Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech. We demonstrate that we can model human raters' mean opin…
CNN Architectures for Large-Scale Audio Classification
Shawn Hershey, Sourish Chaudhuri, Daniel P. W. Ellis +10
Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of…