# MDDM: A Multi-view Discriminative Enhanced Diffusion-based Model for Speech Enhancement

arXiv: https://arxiv.org/abs/2505.13029 · HTML: https://mlsanity.com/paper/2505.13029/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 19 May 2025.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 21 of 21)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2212.11851/) (2022)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration](https://mlsanity.com/paper/2204.05841/) (2022)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement](https://mlsanity.com/paper/2106.08672/) (2021)
- [A variance modeling framework based on variational autoencoders for speech enhancement](https://mlsanity.com/paper/1902.01605/) (2019)
- [High Fidelity Speech Enhancement with Band-split RNN](https://mlsanity.com/paper/2212.00406/) (2022)
- [Noise-aware Speech Enhancement using Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.08029/) (2023)
- [Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement](https://mlsanity.com/paper/2004.04001/) (2020)
- [Variance-Preserving-Based Interpolation Diffusion Models for Speech Enhancement](https://mlsanity.com/paper/2306.08527/) (2023)
- [Pre-training Feature Guided Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2406.07646/) (2024)
- [DBNet: A Dual-branch Network Architecture Processing on Spectrum and Waveform for Single-channel Speech Enhancement](https://mlsanity.com/paper/2105.02436/) (2021)

## Abstract (arXiv)

With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusion-based Model. Specifically, we take the features of three domains (time, frequency and noise) as inputs of a discriminative prediction network, generating the preliminary spectrogram. Then, the discriminative output can be converted to clean speech by several inference sampling steps. Due to the intersection of the distributions between discriminative output and clean target, the smaller sampling steps can achieve the competitive performance compared to other diffusion-based methods. Experiments conducted on a public dataset and a realworld dataset validate the effectiveness of MDDM, either on subjective or objective metric.