1 code implementation • 9 Oct 2023 • William Ravenscroft, Stefan Goetze, Thomas Hain
Convolution augmented transformers (conformers) have performed well for many speech processing tasks but have been under-researched for speech separation.
Ranked #3 on Speech Separation on WHAMR!
no code implementations • 14 Apr 2023 • William Ravenscroft, Stefan Goetze, Thomas Hain
In this work, the impact of applying these training signal length (TSL) limits is analysed for two speech separation models: SepFormer, a transformer model, and Conv-TasNet, a convolutional model.
no code implementations • 11 Jan 2023 • George Close, William Ravenscroft, Thomas Hain, Stefan Goetze
Recent work in the domain of speech enhancement has explored the use of self-supervised speech representations to aid in the training of neural speech enhancement models.
2 code implementations • 27 Oct 2022 • William Ravenscroft, Stefan Goetze, Thomas Hain
In this work deformable convolution is proposed as a solution to allow TCN models to have dynamic RFs that can adapt to various reverberation times for reverberant speech separation.
Ranked #12 on Speech Separation on WHAMR!
1 code implementation • 17 May 2022 • William Ravenscroft, Stefan Goetze, Thomas Hain
It is shown that this weighted multi-dilation temporal convolutional network (WD-TCN) consistently outperforms the TCN across various model configurations and using the WD-TCN model is a more parameter efficient method to improve the performance of the model than increasing the number of convolutional blocks.
Ranked #1 on Speech Dereverberation on WHAMR!
1 code implementation • 13 Apr 2022 • William Ravenscroft, Stefan Goetze, Thomas Hain
A feature of TCNs is that they have a receptive field (RF) dependent on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame.
Ranked #1 on Speech Dereverberation on WHAMR_ext