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DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech

Jaekwon Im, Juhan Nam

TL;DR

DiffRENT addresses the challenge of transforming speech to match a reference recording environment across microphone type/placement, room acoustics, and noise. It introduces a diffusion-based framework with a recording environment encoder, a content enhancer, and a diffusion decoder to generate target mel-spectrograms conditioned on both content and environment embeddings. The approach demonstrates strong generalization to unseen environments and speakers and yields improvements in both objective metrics and subjective listening tests, compared with acoustic matching and speech enhancement baselines. The work highlights the potential of diffusion models for holistic acoustic transfer and points to future work on vocoder quality and faster inference.

Abstract

Properly setting up recording conditions, including microphone type and placement, room acoustics, and ambient noise, is essential to obtaining the desired acoustic characteristics of speech. In this paper, we propose Diff-R-EN-T, a Diffusion model for Recording ENvironment Transfer which transforms the input speech to have the recording conditions of a reference speech while preserving the speech content. Our model comprises the content enhancer, the recording environment encoder, and the diffusion decoder which generates the target mel-spectrogram by utilizing both enhancer and encoder as input conditions. We evaluate DiffRENT in the speech enhancement and acoustic matching scenarios. The results show that DiffRENT generalizes well to unseen environments and new speakers. Also, the proposed model achieves superior performances in objective and subjective evaluation. Sound examples of our proposed model are available online.

DIFFRENT: A Diffusion Model for Recording Environment Transfer of Speech

TL;DR

DiffRENT addresses the challenge of transforming speech to match a reference recording environment across microphone type/placement, room acoustics, and noise. It introduces a diffusion-based framework with a recording environment encoder, a content enhancer, and a diffusion decoder to generate target mel-spectrograms conditioned on both content and environment embeddings. The approach demonstrates strong generalization to unseen environments and speakers and yields improvements in both objective metrics and subjective listening tests, compared with acoustic matching and speech enhancement baselines. The work highlights the potential of diffusion models for holistic acoustic transfer and points to future work on vocoder quality and faster inference.

Abstract

Properly setting up recording conditions, including microphone type and placement, room acoustics, and ambient noise, is essential to obtaining the desired acoustic characteristics of speech. In this paper, we propose Diff-R-EN-T, a Diffusion model for Recording ENvironment Transfer which transforms the input speech to have the recording conditions of a reference speech while preserving the speech content. Our model comprises the content enhancer, the recording environment encoder, and the diffusion decoder which generates the target mel-spectrogram by utilizing both enhancer and encoder as input conditions. We evaluate DiffRENT in the speech enhancement and acoustic matching scenarios. The results show that DiffRENT generalizes well to unseen environments and new speakers. Also, the proposed model achieves superior performances in objective and subjective evaluation. Sound examples of our proposed model are available online.
Paper Structure (19 sections, 6 equations, 2 figures, 2 tables)

This paper contains 19 sections, 6 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. DIFFRENT
  3. Recording Environment Encoder
  4. Content Enhancer
  5. Diffusion Decoder
  6. Diffusion/Forward Process
  7. Reverse Process
  8. Optimizing Diffusion Decoder
  9. Experiments
  10. Dataset and Preprocessing
  11. Implementation Details
  12. Evaluation
  13. Objective Evaluation
  14. Subjective Evaluation
  15. Results
  16. ...and 4 more sections

Figures (2)

  • Figure 1: The overall architecture of DiffRENT.
  • Figure 2: t-SNE scatter plots of the recording environment embedding $z_r$. The different colors of the points represent different recording environments. (a) The acoustic environment embedding from acousticmatching. (b) The baseline encoder in DiffRENT (c) The recording environment encoder in DiffRENT