# A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement

arXiv: https://arxiv.org/abs/2108.11877 · HTML: https://mlsanity.com/paper/2108.11877/

## Key facts

- Cited by 2 papers in the map (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## 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, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Interspeech 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2101.01902/) (2021)
- [Single-Channel Multi-Speaker Separation using Deep Clustering](https://mlsanity.com/paper/1607.02173/) (2016)
- [Differentiable Consistency Constraints for Improved Deep Speech Enhancement](https://mlsanity.com/paper/1811.08521/) (2018)
- [Glance and Gaze: A Collaborative Learning Framework for Single-channel Speech Enhancement](https://mlsanity.com/paper/2106.11789/) (2021)
- [Data augmentation and loss normalization for deep noise suppression](https://mlsanity.com/paper/2008.06412/) (2020)
- [DCCRN+: Channel-wise Subband DCCRN with SNR Estimation for Speech Enhancement](https://mlsanity.com/paper/2106.08672/) (2021)
- [Real-time Denoising and Dereverberation with Tiny Recurrent U-Net](https://mlsanity.com/paper/2102.03207/) (2021)
- [Non-intrusive speech quality assessment using neural networks](https://mlsanity.com/paper/1903.06908/) (2019)
- [Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality](https://mlsanity.com/paper/1905.01898/) (2019)
- [Monaural Speech Enhancement with Complex Convolutional Block Attention Module and Joint Time Frequency Losses](https://mlsanity.com/paper/2102.01993/) (2021)
- [Complex Spectral Mapping With Attention Based Convolution Recurrent Neural Network for Speech Enhancement](https://mlsanity.com/paper/2104.05267/) (2021)

## Built on by (most cited first, 2 of 2)

- [HGCN: Harmonic gated compensation network for speech enhancement](https://mlsanity.com/paper/2201.12755/) (2022)
- [Multiple Confidence Gates For Joint Training Of SE And ASR](https://mlsanity.com/paper/2204.00226/) (2022)

## Abstract (arXiv)

Deep learning technology has been widely applied to speech enhancement. While testing the effectiveness of various network structures, researchers are also exploring the improvement of the loss function used in network training. Although the existing methods have considered the auditory characteristics of speech or the reasonable expression of signal-to-noise ratio, the correlation with the auditory evaluation score and the applicability of the calculation for gradient optimization still need to be improved. In this paper, a signal-to-noise ratio loss function based on auditory power compression is proposed. The experimental results show that the overall correlation between the proposed function and the indexes of objective speech intelligibility, which is better than other loss functions. For the same speech enhancement model, the training effect of this method is also better than other comparison methods.