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Found 11 papers, 3 papers with code
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.
Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping
Our system achieves a promising accuracy for streaming keyword spotting and keyword search on Common Voice audio using just 5 examples per keyword.
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.
Prediction of speech intelligibility with DNN-based performance measures
This paper presents a speech intelligibility model based on automatic speech recognition (ASR), combining phoneme probabilities from deep neural networks (DNN) and a performance measure that estimates the word error rate from these probabilities.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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).
EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices
People suffering from hearing impairment often have difficulties participating in conversations in so-called `cocktail party' scenarios with multiple people talking simultaneously.
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).
Ranked #2 on
Speech Enhancement
on WHAMR!
Speech Enhancement
Audio and Speech Processing
Sound
DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement
In contrast to single-frame approaches such as the Wiener gain, it has been shown that multi-frame approaches achieve a substantial noise reduction with hardly any speech distortion, provided that an accurate estimate of the correlation matrices and especially the speech interframe correlation (IFC) vector is available.
On the Relevance of Auditory-Based Gabor Features for Deep Learning in Automatic Speech Recognition
Previous studies support the idea of merging auditory-based Gabor features with deep learning architectures to achieve robust automatic speech recognition, however, the cause behind the gain of such combination is still unknown.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
