24 citations · 51 across the 7 of their papers we have counts for
5 papers · 1 filter
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References
Pranay Manocha, Buye Xu, Anurag Kumar
The perceptual task of speech quality assessment (SQA) is a challenging task for machines to do. Objective SQA methods that rely on the availability of the corresponding clean refe…
Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems
Yangyang Xia, Buye Xu, Anurag Kumar
Supervised speech enhancement relies on parallel databases of degraded speech signals and their clean reference signals during training. This setting prohibits the use of real-worl…
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Ori Kabeli, Yossi Adi, Zhenyu Tang +2
Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produ…
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
Ke Tan, Buye Xu, Anurag Kumar +2
Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaura…
Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data
Haytham M. Fayek, Anurag Kumar
Recognizing sounds is a key aspect of computational audio scene analysis and machine perception. In this paper, we advocate that sound recognition is inherently a multi-modal audio…