24 citations · 99 across the 27 of their papers we have counts for
9 papers · 1 filter
NICE-Beam: Neural Integrated Covariance Estimators for Time-Varying Beamformers
Jonah Casebeer, Jacob Donley, Daniel Wong +2
Estimating a time-varying spatial covariance matrix for a beamforming algorithm is a challenging task, especially for wearable devices, as the algorithm must compensate for time-va…
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…
Multichannel Speech Enhancement without Beamforming
Asutosh Pandey, Buye Xu, Anurag Kumar +3
Deep neural networks are often coupled with traditional spatial filters, such as MVDR beamformers for effectively exploiting spatial information. Even though single-stage end-to-en…
Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network
Ashutosh Pandey, Buye Xu, Anurag Kumar +3
Deep neural networks (DNNs) are very effective for multichannel speech enhancement with fixed array geometries. However, it is not trivial to use DNNs for ad-hoc arrays with unknow…
TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement
Ashutosh Pandey, Buye Xu, Anurag Kumar +3
In this work, we propose a new model called triple-path attentive recurrent network (TPARN) for multichannel speech enhancement in the time domain. TPARN extends a single-channel d…
Continual self-training with bootstrapped remixing for speech enhancement
Efthymios Tzinis, Yossi Adi, Vamsi K. Ithapu +2
We propose RemixIT, a simple and novel self-supervised training method for speech enhancement. The proposed method is based on a continuously self-training scheme that overcomes li…
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…
DPLM: A Deep Perceptual Spatial-Audio Localization Metric
Pranay Manocha, Anurag Kumar, Buye Xu +4
Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality. However, they are chal…