Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals. Recently, conditional generative adversarial networks (cGANs) have shown promise in …
Recent advances in neural network (NN)-based speech enhancement schemes are shown to outperform most conventional techniques. However, the performance of such systems in adverse listening conditions such as negative signal-to-noise ratios and unseen …
Exemplar-based nonnegative models, where the noisy speech is decomposed as a sparse nonnegative linear combination of the speech and noise exemplars stored in a dictionary, have been successfully used for speech denoising. This paper extends this …
Exemplar-based techniques, where the noisy speech is decomposed as a linear combination of the speech and noise exemplars stored in a dictionary, have been successfully used for speech enhancement in noisy environments. This paper extends this …
Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the …
In this paper, we propose a single-channel speech enhancement system based on the noise robust exemplar matching (N-REM) framework using coupled dictionaries. N-REM approximates noisy speech segments as a sparse linear combination of speech and noise …
Deep neural network (DNN) based acoustic modelling has been successfully used for a variety of automatic speech recognition (ASR) tasks, thanks to its ability to learn higher-level information using multiple hidden layers. This paper investigates the …