Real-time Speech Enhancement on Raw Signals with Deep State-space Modeling

• URL: http://arxiv.org/abs/2409.03377v2
• Date: Sat, 7 Sep 2024 23:13:20 GMT
• Title: Real-time Speech Enhancement on Raw Signals with Deep State-space Modeling
• Authors: Yan Ru Pei, Ritik Shrivastava, FNU Sidharth,
• Abstract summary: We present aTENNuate, a simple deep state-space autoencoder configured for efficient online raw speech enhancement. We benchmark aTENNuate on the VoiceBank + DEMAND and the Microsoft DNS1 synthetic test sets. The network outperforms previous real-time denoising models in terms of PESQ score, parameter count, MACs, and latency.
• Score: 1.0650780147044159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present aTENNuate, a simple deep state-space autoencoder configured for efficient online raw speech enhancement in an end-to-end fashion. The network's performance is primarily evaluated on raw speech denoising, with additional assessments on tasks such as super-resolution and de-quantization. We benchmark aTENNuate on the VoiceBank + DEMAND and the Microsoft DNS1 synthetic test sets. The network outperforms previous real-time denoising models in terms of PESQ score, parameter count, MACs, and latency. Even as a raw waveform processing model, the model maintains high fidelity to the clean signal with minimal audible artifacts. In addition, the model remains performant even when the noisy input is compressed down to 4000Hz and 4 bits, suggesting general speech enhancement capabilities in low-resource environments. Code is available at github.com/Brainchip-Inc/aTENNuate

Related papers

• CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning [13.632515996508172]
  CleanUMamba is a time-domain neural network architecture designed for causal audio denoising directly applied to raw waveforms. CleanUMamba achieves a PESQ score of 2.42 and STOI of 95.1% with only 442K parameters and 468M MACs, matching or outperforming larger models in real-time performance.
  arXiv  Detail & Related papers  (2024-10-14T20:18:03Z)
• sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks [51.516451451719654]
  Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient. This paper introduces a novel SNN-based Voice Activity Detection model, referred to as sVAD. It provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms.
  arXiv  Detail & Related papers  (2024-03-09T02:55:44Z)
• Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition [13.53738829631595]
  We propose a novel method to extract the denoising capabilities, that can be applied to any encoder-decoder architecture. We train our pre-processor on the Noisy Speech Database (NSD) to reconstruct denoised spectrograms from noisy inputs. We show that the Cleancoder is able to filter noise from speech and that it improves the total Word Error Rate (WER) of the downstream model in noisy conditions.
  arXiv  Detail & Related papers  (2023-09-05T11:34:21Z)
• Self-Supervised Learning for Speech Enhancement through Synthesis [5.924928860260821]
  We propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation.
  arXiv  Detail & Related papers  (2022-11-04T16:06:56Z)
• FastLTS: Non-Autoregressive End-to-End Unconstrained Lip-to-Speech Synthesis [77.06890315052563]
  We propose FastLTS, a non-autoregressive end-to-end model which can directly synthesize high-quality speech audios from unconstrained talking videos with low latency. Experiments show that our model achieves $19.76times$ speedup for audio generation compared with the current autoregressive model on input sequences of 3 seconds.
  arXiv  Detail & Related papers  (2022-07-08T10:10:39Z)
• Neural Vocoder is All You Need for Speech Super-resolution [56.84715616516612]
  Speech super-resolution (SR) is a task to increase speech sampling rate by generating high-frequency components. Existing speech SR methods are trained in constrained experimental settings, such as a fixed upsampling ratio. We propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
  arXiv  Detail & Related papers  (2022-03-28T17:51:00Z)
• Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder [30.318947721658862]
  We propose to include noise information in the training phase by using a noise-aware encoder trained on noisy-clean speech pairs. We show that our proposed noise-aware VAE outperforms the standard VAE in terms of overall distortion without increasing the number of model parameters.
  arXiv  Detail & Related papers  (2021-02-17T11:40:42Z)
• CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application [63.2243126704342]
  This study presents a deep learning-based speech signal-processing mobile application known as CITISEN. The CITISEN provides three functions: speech enhancement (SE), model adaptation (MA), and background noise conversion (BNC) Compared with the noisy speech signals, the enhanced speech signals achieved about 6% and 33% of improvements.
  arXiv  Detail & Related papers  (2020-08-21T02:04:12Z)
• Real Time Speech Enhancement in the Waveform Domain [99.02180506016721]
  We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is capable of removing various kinds of background noise including stationary and non-stationary noises.
  arXiv  Detail & Related papers  (2020-06-23T09:19:13Z)
• Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances [53.063441357826484]
  Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions. Speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances.
  arXiv  Detail & Related papers  (2020-02-14T13:34:33Z)
• RawNet: Fast End-to-End Neural Vocoder [4.507860128918788]
  RawNet is a complete end-to-end neural vocoder based on the auto-encoder structure for speaker-dependent and -independent speech synthesis. It automatically learns to extract features and recover audio using neural networks, which include a coder network to capture a higher representation of the input audio and an autoregressive voder network to restore the audio in a sample-by-sample manner.
  arXiv  Detail & Related papers  (2019-04-10T10:25:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.