514 citations · 518 across the 3 of their papers we have counts for
5 papers
REAL-M: Towards Speech Separation on Real Mixtures
Cem Subakan, Mirco Ravanelli, Samuele Cornell +1
In recent years, deep learning based source separation has achieved impressive results. Most studies, however, still evaluate separation models on synthetic datasets, while the per…
SpeechBrain: A General-Purpose Speech Toolkit
Mirco Ravanelli, Titouan Parcollet, Peter Plantinga +18
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the research and development of neural speech processing technologies by being simple, fle…
Attention is All You Need in Speech Separation
Cem Subakan, Mirco Ravanelli, Samuele Cornell +2
Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parall…
Learning the Base Distribution in Implicit Generative Models
Cem Subakan, Oluwasanmi Koyejo, Paris Smaragdis
Popular generative model learning methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple dist…
Neural Network Alternatives to Convolutive Audio Models for Source Separation
Shrikant Venkataramani, Y. Cem Subakan, Paris Smaragdis
Convolutive Non-Negative Matrix Factorization model factorizes a given audio spectrogram using frequency templates with a temporal dimension. In this paper, we present a convolutio…