144 citations · 165 across the 14 of their papers we have counts for
18 papers
Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement
Xulin Fan, Juan Azcarreta, Ashutosh Pandey +5
Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational constraints significantly limit on…
Masked Autoencoders as Universal Speech Enhancer
Rajalaxmi Rajagopalan, Ritwik Giri, Zhiqiang Tang +1
Supervised speech enhancement methods have been very successful. However, in practical scenarios, there is a lack of clean speech, and self-supervised learning-based (SSL) speech e…
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…
A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
Zhepei Wang, Ritwik Giri, Devansh Shah +3
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement. This is achieved by i…
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…
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…
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…
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…
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…
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…
Relevance Subject Machine: A Novel Person Re-identification Framework
Igor Fedorov, Ritwik Giri, Bhaskar D. Rao +1
We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem. RSM falls under the category of Bayesian sparse recovery…
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
Igor Fedorov, Ritwik Giri, Bhaskar D. Rao +1
In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the abil…
A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem
Igor Fedorov, Alican Nalci, Ritwik Giri +3
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide variety of applications where an unknown, non-negative quantity must be recovered…
Type I and Type II Bayesian Methods for Sparse Signal Recovery using Scale Mixtures
Ritwik Giri, Bhaskar D. Rao
In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors curre…