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Thomas Courtat

2 accepted papers

2023

Learning Interpretable Filters In Wav-UNet For Speech Enhancement

ICASSP 2023accepted

Due to their performances, deep neural networks have emerged as a major method in nearly all modern audio processing applications. Deep neural networks can be used to estimate some parameters or hyperparameters of a model, or in some cases the entire model in an end-to-end fashion. Although deep lea…

Cited by 0SourceScholar
2022

Phase Shifted Bedrosian Filterbank: An Interpretable Audio Front-End for Time-Domain Audio Source Separation

ICASSP 2022accepted

The use of a parameterized encoders or audio front-ends has shown promises in improving the interpretability of time domain single-channel source separation models such as Conv-TasNet. This type of filters also allows a potential reduction of the computational cost since larger encoder filters can b…

Cited by 0SourceScholar