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Found 12 papers, 4 papers with code
Rethinking complex-valued deep neural networks for monaural speech enhancement
By comparing complex- and real-valued versions of fundamental building blocks in the recently developed gated convolutional recurrent network (GCRN), we show how different mechanisms for basic blocks affect the performance.
LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders
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.
Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement
During training, our approach augments a model learning complex spectral mapping with a temporary submodel to predict the covariance of the enhancement error at each time-frequency bin.
Spatially Selective Active Noise Control Systems
We simulated the proposed algorithm based on a microphone array on a pair of augmented eye-glasses and compared it with the existing methods in the literature.
SAQAM: Spatial Audio Quality Assessment Metric
Audio quality assessment is critical for assessing the perceptual realism of sounds.
RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing
RemixIT is based on a continuous self-training scheme in which a pre-trained teacher model on out-of-domain data infers estimated pseudo-target signals for in-domain mixtures.
Continual self-training with bootstrapped remixing for speech enhancement
Specifically, a separation teacher model is pre-trained on an out-of-domain dataset and is used to infer estimated target signals for a batch of in-domain mixtures.
NORESQA: A Framework for Speech Quality Assessment using Non-Matching References
We show that neural networks trained using our framework produce scores that correlate well with subjective mean opinion scores (MOS) and are also competitive to methods such as DNSMOS, which explicitly relies on MOS from humans for training networks.
Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems
Supervised speech enhancement relies on parallel databases of degraded speech signals and their clean reference signals during training.
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Our stateful implementation for online separation leads to a minor drop in performance compared to the offline model; 0. 8dB for monaural inputs and 0. 3dB for binaural inputs while reaching a real-time factor of 0. 65.
DPLM: A Deep Perceptual Spatial-Audio Localization Metric
Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality.
SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation
In addition, our approach effectively preserves the interaural cues, which improves the accuracy of sound localization.
Audio and Speech Processing Sound
