In our paper, we proposed MP-SENet: a TF-domain monaural SE model with parallel magnitude and phase spectra denoising.
A long-version MP-SENet was extended to the speech denoising, dereverberation, and bandwidth extension tasks.
Audio samples can be found at the demo website.
We provide our implementation as open source in this repository.

Abstract: 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 that explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet comprises a Transformer-embedded encoder-decoder architecture. The encoder aims to encode the input distorted magnitude and phase spectra into time-frequency representations, which are further fed into time-frequency Transformers for alternatively capturing time and frequency dependencies. The decoder comprises a magnitude mask decoder and a phase decoder, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude masking architecture and a phase parallel estimation architecture, respectively. Multi-level loss functions explicitly defined on the magnitude spectra, wrapped phase spectra, and short-time complex spectra are adopted to jointly train the MP-SENet model. A metric discriminator is further employed to compensate for the incomplete correlation between these losses and human auditory perception. Experimental results demonstrate that our proposed MP-SENet achieves state-of-the-art performance across multiple speech enhancement tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it further mitigates the compensation effect between the magnitude and phase by explicit phase estimation, elevating the perceptual quality of enhanced speech. Remarkably, for the speech denoising task, the proposed MP-SENet yields a PESQ of 3.60 on the VoiceBank+DEMAND dataset and 3.62 on the DNS challenge dataset.

1. Python >= 3.6.
2. Clone this repository.
3. Install python requirements. Please refer requirements.txt.
4. Download and extract the VoiceBank+DEMAND dataset. Resample all wav files to 16kHz, and move the clean and noisy wavs to VoiceBank+DEMAND/wavs_clean and VoiceBank+DEMAND/wavs_noisy, respectively. You can also directly download the downsampled 16kHz dataset here.

CUDA_VISIBLE_DEVICES=0,1 python train.py --config config.json

Checkpoints and copy of the configuration file are saved in the cp_mpsenet directory by default.
You can change the path by adding --checkpoint_path option.

python inference.py --checkpoint_file [generator checkpoint file path]

You can also use the pretrained best checkpoint files we provide in the best_ckpt directory.
Generated wav files are saved in generated_files by default. You can change the path by adding --output_dir option.
Here is an example:

python inference.py --checkpoint_file best_ckpt/g_best_vb --output_dir generated_files/MP-SENet_VB

We referred to HiFiGAN, NSPP and CMGAN to implement this.

@inproceedings{lu2023mp,
  title={{MP-SENet}: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra},
  author={Lu, Ye-Xin and Ai, Yang and Ling, Zhen-Hua},
  booktitle={Proc. Interspeech},
  pages={3834--3838},
  year={2023}
}

@article{lu2023explicit,
  title={Explicit estimation of magnitude and phase spectra in parallel for high-quality speech enhancement},
  author={Lu, Ye-Xin and Ai, Yang and Ling, Zhen-Hua},
  journal={arXiv preprint arXiv:2308.08926},
  year={2023}
}