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Found 8 papers, 3 papers with code
Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios
Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids.
Binaural multichannel blind speaker separation with a causal low-latency and low-complexity approach
In this paper, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions.
Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids
The performance of an oracle binaural LCMV beamformer in non-low-latency configuration can be matched even by a unilateral configuration of the GCFSnet in terms of objective metrics.
tPLCnet: Real-time Deep Packet Loss Concealment in the Time Domain Using a Short Temporal Context
The model with the lowest complexity described in this paper reaches a robust PLC performance and consistent improvements over the zero-filling baseline for all metrics.
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators
Using weight pruning, we show that we are able to compress an already compact model's memory footprint by a factor of 42x from 3. 7MB to 87kB while only losing 0. 1 dB SDR in performance.
Reduction of Subjective Listening Effort for TV Broadcast Signals with Recurrent Neural Networks
Listening to the audio of TV broadcast signals can be challenging for hearing-impaired as well as normal-hearing listeners, especially when background sounds are prominent or too loud compared to the speech signal.
Acoustic echo cancellation with the dual-signal transformation LSTM network
This paper applies the dual-signal transformation LSTM network (DTLN) to the task of real-time acoustic echo cancellation (AEC).
Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression
This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge).
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Speech Enhancement
on WHAMR!
Speech Enhancement
Audio and Speech Processing
Sound
