327 research outputs found
Bidirectional truncated recurrent neural networks for efficient speech denoising
We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.P
High-dimensional sequence transduction
We investigate the problem of transforming an input sequence into a
high-dimensional output sequence in order to transcribe polyphonic audio music
into symbolic notation. We introduce a probabilistic model based on a recurrent
neural network that is able to learn realistic output distributions given the
input and we devise an efficient algorithm to search for the global mode of
that distribution. The resulting method produces musically plausible
transcriptions even under high levels of noise and drastically outperforms
previous state-of-the-art approaches on five datasets of synthesized sounds and
real recordings, approximately halving the test error rate
Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement
Phase information has a significant impact on speech perceptual quality and
intelligibility. However, existing speech enhancement methods encounter
limitations in explicit phase estimation due to the non-structural nature and
wrapping characteristics of the phase, leading to a bottleneck in enhanced
speech quality. To overcome the above issue, in this paper, we proposed
MP-SENet, a novel Speech Enhancement Network which explicitly enhances
Magnitude and Phase spectra in parallel. The proposed MP-SENet adopts a codec
architecture in which the encoder and decoder are bridged by time-frequency
Transformers along both time and frequency dimensions. The encoder aims to
encode time-frequency representations derived from the input distorted
magnitude and phase spectra. The decoder comprises dual-stream magnitude and
phase decoders, directly enhancing magnitude and wrapped phase spectra by
incorporating a magnitude estimation architecture and a phase parallel
estimation architecture, respectively. To train the MP-SENet model effectively,
we define multi-level loss functions, including mean square error and
perceptual metric loss of magnitude spectra, anti-wrapping loss of phase
spectra, as well as mean square error and consistency loss of short-time
complex spectra. Experimental results demonstrate that our proposed MP-SENet
excels in high-quality speech enhancement across multiple tasks, including
speech denoising, dereverberation, and bandwidth extension. Compared to
existing phase-aware speech enhancement methods, it successfully avoids the
bidirectional compensation effect between the magnitude and phase, leading to a
better harmonic restoration. Notably, for the speech denoising task, the
MP-SENet yields a state-of-the-art performance with a PESQ of 3.60 on the
public VoiceBank+DEMAND dataset.Comment: Submmited to IEEE Transactions on Audio, Speech and Language
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