86 citations · 278 across the 36 of their papers we have counts for
8 papers · 1 filter
Performance optimizations on deep noise suppression models
Jerry Chee, Sebastian Braun, Vishak Gopal +1
We study the role of magnitude structured pruning as an architecture search to speed up the inference time of a deep noise suppression (DNS) model. While deep learning approaches h…
Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement
Xavier Gitiaux, Aditya Khant, Ebrahim Beyrami +3
Noise suppression models running in production environments are commonly trained on publicly available datasets. However, this approach leads to regressions due to the lack of trai…
MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
Chandan K. A. Reddy, Vishak Gopa, Harishchandra Dubey +3
With the recent growth of remote work, online meetings often encounter challenging audio contexts such as background noise, music, and echo. Accurate real-time detection of music e…
AECMOS: A speech quality assessment metric for echo impairment
Marju Purin, Sten Sootla, Mateja Sponza +2
Traditionally, the quality of acoustic echo cancellers is evaluated using intrusive speech quality assessment measures such as ERLE \cite{g168} and PESQ \cite{p862}, or by carrying…
DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors
Chandan K A Reddy, Vishak Gopal, Ross Cutler
Human subjective evaluation is the gold standard to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. We…
Speech Quality Assessment in Crowdsourcing: Comparison Category Rating Method
Babak Naderi, Sebastian Möller, Ross Cutler
Traditionally, Quality of Experience (QoE) for a communication system is evaluated through a subjective test. The most common test method for speech QoE is the Absolute Category Ra…