16 citations · 25 across the 4 of their papers we have counts for
6 papers
Speaker independence of neural vocoders and their effect on parametric resynthesis speech enhancement
Soumi Maiti, Michael I Mandel
Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better q…
Onssen: an open-source speech separation and enhancement library
Zhaoheng Ni, Michael I Mandel
Speech separation is an essential task for multi-talker speech recognition. Recently many deep learning approaches are proposed and have been constantly refreshing the state-of-the…
Mask-dependent Phase Estimation for Monaural Speaker Separation
Zhaoheng Ni, Michael I Mandel
Speaker separation refers to isolating speech of interest in a multi-talker environment. Most methods apply real-valued Time-Frequency (T-F) masks to the mixture Short-Time Fourier…
Parametric Resynthesis with neural vocoders
Soumi Maiti, Michael I Mandel
Noise suppression systems generally produce output speech with compromised quality. We propose to utilize the high quality speech generation capability of neural vocoders for noise…
Speech denoising by parametric resynthesis
Soumi Maiti, Michael I Mandel
This work proposes the use of clean speech vocoder parameters as the target for a neural network performing speech enhancement. These parameters have been designed for text-to-spee…
Autotagging music with conditional restricted Boltzmann machines
Michael Mandel, Razvan Pascanu, Hugo Larochelle +1
This paper describes two applications of conditional restricted Boltzmann machines (CRBMs) to the task of autotagging music. The first consists of training a CRBM to predict tags t…