7 citations · 22 across the 13 of their papers we have counts for
16 papers
Real-time Stereo Speech Enhancement with Spatial-Cue Preservation based on Dual-Path Structure
Masahito Togami, Jean-Marc Valin, Karim Helwani +3
We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source…
Semi-supervised Time Domain Target Speaker Extraction with Attention
Zhepei Wang, Ritwik Giri, Shrikant Venkataramani +5
In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on…
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets
Jean-Marc Valin, Ritwik Giri, Shrikant Venkataramani +2
In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech. Recently, deep learning-based speech enhancement approa…
End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation
Krishna Subramani, Jean-Marc Valin, Umut Isik +2
Neural vocoders have recently demonstrated high quality speech synthesis, but typically require a high computational complexity. LPCNet was proposed as a way to reduce the complexi…
Improved singing voice separation with chromagram-based pitch-aware remixing
Siyuan Yuan, Zhepei Wang, Umut Isik +4
Singing voice separation aims to separate music into vocals and accompaniment components. One of the major constraints for the task is the limited amount of training data with sepa…
Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet
Jean-Marc Valin, Umut Isik, Paris Smaragdis +1
Neural speech synthesis models can synthesize high quality speech but typically require a high computational complexity to do so. In previous work, we introduced LPCNet, which uses…
Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement
Ritwik Giri, Shrikant Venkataramani, Jean-Marc Valin +2
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size a…
Semi-Supervised Singing Voice Separation with Noisy Self-Training
Zhepei Wang, Ritwik Giri, Umut Isik +2
Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has bee…
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Jonah Casebeer, Vinjai Vale, Umut Isik +3
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output…
Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On PercepNet
Jean-Marc Valin, Srikanth Tenneti, Karim Helwani +2
Speech enhancement algorithms based on deep learning have greatly surpassed their traditional counterparts and are now being considered for the task of removing acoustic echo from…
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
Jean-Marc Valin, Umut Isik, Neerad Phansalkar +3
Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of…
Generating Music with a Self-Correcting Non-Chronological Autoregressive Model
Wayne Chi, Prachi Kumar, Suri Yaddanapudi +2
We describe a novel approach for generating music using a self-correcting, non-chronological, autoregressive model. We represent music as a sequence of edit events, each of which d…
PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
Umut Isik, Ritwik Giri, Neerad Phansalkar +3
Neural network applications generally benefit from larger-sized models, but for current speech enhancement models, larger scale networks often suffer from decreased robustness to t…
Efficient Trainable Front-Ends for Neural Speech Enhancement
Jonah Casebeer, Umut Isik, Shrikant Venkataramani +1
Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) f…
From Speech-to-Speech Translation to Automatic Dubbing
Marcello Federico, Robert Enyedi, Roberto Barra-Chicote +4
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of…
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Bahareh Tolooshams, Ritwik Giri, Andrew H. Song +2
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary ma…