torch-log-wmse-audio-quality 0.1.0

logWMSE is an audio quality metric & loss function with support for digital silence target. Useful for training and evaluating audio source separation systems.

torch-log-wmse-audio-quality

This repository contains the torch implementation of an audio quality metric, logWMSE, originally suggested by Iver Jordal of Nomono. In addition to the original metric, this implementation can also be used as a loss function for training audio models.

logWMSE is a custom metric and loss function for audio signals that calculates the logarithm (log) of a frequency-weighted (W) Mean Squared Error (MSE). It is designed to address several shortcomings of common audio metrics, most importantly the lack of support for digital silence targets.

Installation

To use this repository, clone it and install the required dependencies:

git clone https://github.com/your-username/torch-log-wmse-audio-quality.git
cd torch-log-wmse-audio-quality
pip install -r requirements.txt

Usage Example

import torch
from torch_log_wmse_audio_quality import LogWMSE

# Tensor shapes
audio_length = 1.0
sample_rate = 44100
audio_channels = 2 # stereo
audio_stems = 3 # 3 audio stems
batch = 4 # batch size

# Instantiate logWMSELoss
log_wmse_loss = LogWMSE(audio_length=audio_length, sample_rate=sample_rate, return_as_loss=True)

# Generate random inputs (scale between -1 and 1)
audio_lengths_samples = int(audio_length * sample_rate)
unprocessed_audio = 2 * torch.rand(batch, audio_channels, audio_lengths_samples) - 1
processed_audio = unprocessed_audio.unsqueeze(2).expand(-1, -1, audio_stems, -1) * 0.1
target_audio = torch.zeros(batch, audio_channels, audio_stems, audio_lengths_samples)

log_wmse = log_wmse_loss(unprocessed_audio, processed_audio, target_audio)
print(log_wmse)  # Expected output: approx. -18.42

Motivation

• Supports digital silence targets not supported by other audio metrics. i.e. (SI-)SDR, SIR, SAR, ISR, VISQOL_audio, STOI, CDPAM, and VISQOL.
• Overcomes the small value range issue of MSE (i.e. between 1e-8 and 1e-3), making number formatting and sight-reading easier. Scaled similar to SI-SDR.
• Scale-invariant, aligns with the frequency sensitivity of human hearing.
• Invariant to the tiny errors of MSE that are inaudible to humans.
• Logarithmic, reflecting the logarithmic sensitivity of human hearing.
• Tailored specifically for audio signals.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or new features to suggest.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Acknowledgements

Thanks to Whitebalance for backing this project.