65 citations · 92 across the 8 of their papers we have counts for
11 papers
SCA: Streaming Cross-attention Alignment for Echo Cancellation
Yang Liu, Yangyang Shi, Yun Li +3
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art…
ICASSP 2022 Deep Noise Suppression Challenge
Harishchandra Dubey, Vishak Gopal, Ross Cutler +8
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. This is the 4th DNS chal…
Interspeech 2021 Deep Noise Suppression Challenge
Chandan K A Reddy, Harishchandra Dubey, Kazuhito Koishida +7
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We recently organized a…
Interactive Speech and Noise Modeling for Speech Enhancement
Chengyu Zheng, Xiulian Peng, Yuan Zhang +2
Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In thi…
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Jayant Gupchup, Ashkan Aazami, Yaran Fan +23
Large software systems tune hundreds of 'constants' to optimize their runtime performance. These values are commonly derived through intuition, lab tests, or A/B tests. A 'one-size…
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results
Kusha Sridhar, Ross Cutler, Ando Saabas +6
The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhance…
ICASSP 2021 Deep Noise Suppression Challenge
Chandan K A Reddy, Harishchandra Dubey, Vishak Gopal +5
The Deep Noise Suppression (DNS) challenge is designed to foster innovation in the area of noise suppression to achieve superior perceptual speech quality. We recently organized a…
DNN No-Reference PSTN Speech Quality Prediction
Gabriel Mittag, Ross Cutler, Yasaman Hosseinkashi +4
Classic public switched telephone networks (PSTN) are often a black box for VoIP network providers, as they have no access to performance indicators, such as delay or packet loss.…
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
Chandan K. A. Reddy, Vishak Gopal, Ross Cutler +10
The INTERSPEECH 2020 Deep Noise Suppression (DNS) Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement aimed to maximize the subje…
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra Dubey +10
The INTERSPEECH 2020 Deep Noise Suppression Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement aimed to maximize the subjective…
A scalable noisy speech dataset and online subjective test framework
Chandan K. A. Reddy, Ebrahim Beyrami, Jamie Pool +3
Background noise is a major source of quality impairments in Voice over Internet Protocol (VoIP) and Public Switched Telephone Network (PSTN) calls. Recent work shows the efficacy…