30 citations · 65 across the 16 of their papers we have counts for
7 papers · 1 filter
Adapting Speech Separation to Real-World Meetings Using Mixture Invariant Training
Aswin Sivaraman, Scott Wisdom, Hakan Erdogan +1
The recently-proposed mixture invariant training (MixIT) is an unsupervised method for training single-channel sound separation models in the sense that it does not require ground-…
Improving Bird Classification with Unsupervised Sound Separation
Tom Denton, Scott Wisdom, John R. Hershey
This paper addresses the problem of species classification in bird song recordings. The massive amount of available field recordings of birds presents an opportunity to use machine…
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
Yuma Koizumi, Shigeki Karita, Scott Wisdom +4
Single-channel speech enhancement (SE) is an important task in speech processing. A widely used framework combines an analysis/synthesis filterbank with a mask prediction network,…
Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention
Efthymios Tzinis, Scott Wisdom, Tal Remez +1
We introduce a state-of-the-art audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking…
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation
Scott Wisdom, Aren Jansen, Ron J. Weiss +2
Supervised neural network training has led to significant progress on single-channel sound separation. This approach relies on ground truth isolated sources, which precludes scalin…
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…