Exploring Self-Attention Mechanisms for Speech Separation

1 code implementation • 6 Feb 2022 • Cem Subakan, Mirco Ravanelli, Samuele Cornell, Francois Grondin, Mirko Bronzi

In particular, we extend our previous findings on the SepFormer by providing results on more challenging noisy and noisy-reverberant datasets, such as LibriMix, WHAM!, and WHAMR!.

Denoising Speech Enhancement +1

Accounting for Variance in Machine Learning Benchmarks

no code implementations • 1 Mar 2021 • Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

Benchmarking BIG-bench Machine Learning +1

Attention is All You Need in Speech Separation

4 code implementations • 25 Oct 2020 • Cem Subakan, Mirco Ravanelli, Samuele Cornell, Mirko Bronzi, Jianyuan Zhong

Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi-head attention mechanism.

Speech Separation

Automatic Evaluation of Relation Extraction Systems on Large-scale

no code implementations • WS 2012 • Mirko Bronzi, Zhaochen Guo, Filipe Mesquita, Denilson Barbosa, Paolo Merialdo

Relation Relation Extraction