Extract and Diffuse: Latent Integration for Improved Diffusion-based Speech and Vocal Enhancement

• URL: http://arxiv.org/abs/2409.09642v1
• Date: Sun, 15 Sep 2024 07:25:08 GMT
• Title: Extract and Diffuse: Latent Integration for Improved Diffusion-based Speech and Vocal Enhancement
• Authors: Yudong Yang, Zhan Liu, Wenyi Yu, Guangzhi Sun, Qiuqiang Kong, Chao Zhang,
• Abstract summary: Diffusion-based generative models have recently achieved remarkable results in speech and vocal enhancement. We propose Ex-Diff, a novel score-based diffusion model that integrates the latent representations produced by a discriminative model to improve speech and vocal enhancement.
• Score: 14.060387207656046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based generative models have recently achieved remarkable results in speech and vocal enhancement due to their ability to model complex speech data distributions. While these models generalize well to unseen acoustic environments, they may not achieve the same level of fidelity as the discriminative models specifically trained to enhance particular acoustic conditions. In this paper, we propose Ex-Diff, a novel score-based diffusion model that integrates the latent representations produced by a discriminative model to improve speech and vocal enhancement, which combines the strengths of both generative and discriminative models. Experimental results on the widely used MUSDB dataset show relative improvements of 3.7% in SI-SDR and 10.0% in SI-SIR compared to the baseline diffusion model for speech and vocal enhancement tasks, respectively. Additionally, case studies are provided to further illustrate and analyze the complementary nature of generative and discriminative models in this context.

Related papers

• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step. Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler [16.13996677489119]
  Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performance in mismatched conditions compared to state-of-the art discriminative models. We show that a proposed system substantially benefits from using multiple databases for training, and achieves superior performance compared to state-of-the-art discriminative models in both matched and mismatched conditions.
  arXiv  Detail & Related papers  (2023-12-05T11:40:38Z)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• DDTSE: Discriminative Diffusion Model for Target Speech Extraction [62.422291953387955]
  We introduce the Discriminative Diffusion model for Target Speech Extraction (DDTSE) We apply the same forward process as diffusion models and utilize the reconstruction loss similar to discriminative methods. We devise a two-stage training strategy to emulate the inference process during model training.
  arXiv  Detail & Related papers  (2023-09-25T04:58:38Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion. This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement. We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS [0.0]
  The diffusion model is capable of generating high-quality data through a probabilistic approach. It suffers from the drawback of slow generation speed due to the requirement of a large number of time steps. We propose a speech synthesis model with two discriminators: a diffusion discriminator for learning the distribution of the reverse process and a spectrogram discriminator for learning the distribution of the generated data.
  arXiv  Detail & Related papers  (2023-08-03T07:22:04Z)
• Diffusion Models: A Comprehensive Survey of Methods and Applications [10.557289965753437]
  Diffusion models are a class of deep generative models that have shown impressive results on various tasks with dense theoretical founding. Recent studies have shown great enthusiasm on improving the performance of diffusion model.
  arXiv  Detail & Related papers  (2022-09-02T02:59:10Z)
• Conditional Diffusion Probabilistic Model for Speech Enhancement [101.4893074984667]
  We propose a novel speech enhancement algorithm that incorporates characteristics of the observed noisy speech signal into the diffusion and reverse processes. In our experiments, we demonstrate strong performance of the proposed approach compared to representative generative models.
  arXiv  Detail & Related papers  (2022-02-10T18:58:01Z)
• A Study on Speech Enhancement Based on Diffusion Probabilistic Model [63.38586161802788]
  We propose a diffusion probabilistic model-based speech enhancement model (DiffuSE) model that aims to recover clean speech signals from noisy signals. The experimental results show that DiffuSE yields performance that is comparable to related audio generative models on the standardized Voice Bank corpus task.
  arXiv  Detail & Related papers  (2021-07-25T19:23:18Z)

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.