32 citations · 122 across the 33 of their papers we have counts for
7 papers · 1 filter
LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders
Rodrigo Mira, Buye Xu, Jacob Donley +4
Audio-visual speech enhancement aims to extract clean speech from a noisy environment by leveraging not only the audio itself but also the target speaker's lip movements. This appr…
Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement
Kuan-Lin Chen, Daniel D. E. Wong, Ke Tan +3
Most speech enhancement (SE) models learn a point estimate and do not make use of uncertainty estimation in the learning process. In this paper, we show that modeling heteroscedast…
Towards Improved Room Impulse Response Estimation for Speech Recognition
Anton Ratnarajah, Ishwarya Ananthabhotla, Vamsi Krishna Ithapu +3
We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (AS…
SAQAM: Spatial Audio Quality Assessment Metric
Pranay Manocha, Anurag Kumar, Buye Xu +4
Audio quality assessment is critical for assessing the perceptual realism of sounds. However, the time and expense of obtaining ''gold standard'' human judgments limit the availabi…
Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks
Alexander Richard, Peter Dodds, Vamsi Krishna Ithapu
Impulse response estimation in high noise and in-the-wild settings, with minimal control of the underlying data distributions, is a challenging problem. We propose a novel framewor…
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
Efthymios Tzinis, Yossi Adi, Vamsi Krishna Ithapu +3
We present RemixIT, a simple yet effective self-supervised method for training speech enhancement without the need of a single isolated in-domain speech nor a noise waveform. Our a…