# Perceptual Loss Function for Neural Modelling of Audio Systems

arXiv: https://arxiv.org/abs/1911.08922 · HTML: https://mlsanity.com/paper/1911.08922/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching](https://mlsanity.com/paper/2407.03648/) (2024)
- [A Diffusion-Based Generative Equalizer for Music Restoration](https://mlsanity.com/paper/2403.18636/) (2024)
- [Automatic music mixing with deep learning and out-of-domain data](https://mlsanity.com/paper/2208.11428/) (2022)
- [Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet](https://mlsanity.com/paper/2402.17701/) (2024)
- [Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations](https://mlsanity.com/paper/2205.01897/) (2022)
- [Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models](https://mlsanity.com/paper/2403.16331/) (2024)
- [The Interpretation Gap in Text-to-Music Generation Models](https://mlsanity.com/paper/2407.10328/) (2024)
- [Resampling Filter Design for Multirate Neural Audio Effect Processing](https://mlsanity.com/paper/2501.18470/) (2025)
- [Anti-aliasing of neural distortion effects via model fine tuning](https://mlsanity.com/paper/2505.11375/) (2025)
- [Gradient-based Optimisation of Modulation Effects](https://mlsanity.com/paper/2601.04867/) (2026)

## Abstract (arXiv)

This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing. In our previous work, the error-to-signal ratio loss function was used during network training, with a first-order highpass pre-emphasis filter applied to both the target signal and neural network output. This work considers more perceptually relevant pre-emphasis filters, which include lowpass filtering at high frequencies. We conducted listening tests to determine whether they offer an improvement to the quality of a neural network model of a guitar tube amplifier. Listening test results indicate that the use of an A-weighting pre-emphasis filter offers the best improvement among the tested filters. The proposed perceptual loss function improves the sound quality of neural network models in audio processing without affecting the computational cost.