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# auraloss

A collection of audio-focused loss functions in PyTorch.

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## Setup

``````pip install auraloss
``````

## Usage

``````import torch
import auraloss

mrstft = auraloss.freq.MultiResolutionSTFTLoss()

input = torch.rand(8,1,44100)
target = torch.rand(8,1,44100)

loss = mrstft(input, target)
``````

# Loss functions

We categorize the loss functions as either time-domain or frequency-domain approaches. Additionally, we include perceptual transforms.

Loss function Interface Reference
Time domain
Error-to-signal ratio (ESR) `auraloss.time.ESRLoss()` Wright & Välimäki, 2019
DC error (DC) `auraloss.time.DCLoss()` Wright & Välimäki, 2019
Log hyperbolic cosine (Log-cosh) `auraloss.time.LogCoshLoss()` Chen et al., 2019
Signal-to-noise ratio (SNR) `auraloss.time.SNRLoss()`
Scale-invariant signal-to-distortion
ratio (SI-SDR)
`auraloss.time.SISDRLoss()` Le Roux et al., 2018
Scale-dependent signal-to-distortion
ratio (SD-SDR)
`auraloss.time.SDSDRLoss()` Le Roux et al., 2018
Frequency domain
Aggregate STFT `auraloss.freq.STFTLoss()` Arik et al., 2018
Aggregate Mel-scaled STFT `auraloss.freq.MelSTFTLoss(sample_rate)`
Multi-resolution STFT `auraloss.freq.MultiResolutionSTFTLoss()` Yamamoto et al., 2019*
Random-resolution STFT `auraloss.freq.RandomResolutionSTFTLoss()` Steinmetz & Reiss, 2020
Sum and difference STFT loss `auraloss.freq.SumAndDifferenceSTFTLoss()` Steinmetz et al., 2020
Perceptual transforms
Sum and difference signal transform `auraloss.perceptual.SumAndDifference()`
FIR pre-emphasis filters `auraloss.perceptual.FIRFilter()` Wright & Välimäki, 2019

* Wang et al., 2019 also propose a multi-resolution spectral loss (that Engel et al., 2020 follow), but they do not include both the log magnitude (L1 distance) and spectral convergence terms, introduced in Arik et al., 2018, and then extended for the multi-resolution case in Yamamoto et al., 2019.

## Examples

Currently we include an example using a set of the loss functions to train a TCN for modeling an analog dynamic range compressor. For details please refer to the details in `examples/compressor`. We provide pre-trained models, evaluation scripts to compute the metrics in the paper, as well as scripts to retrain models.

There are some more advanced things you can do based upon the `STFTLoss` class. For example, you can compute both linear and log scaled STFT errors as in Engel et al., 2020. In this case we do not include the spectral convergence term.

``````stft_loss = auraloss.freq.STFTLoss(w_log_mag=1.0,
w_lin_mag=1.0,
w_sc=0.0, )
``````

There is also a Mel-scaled STFT loss, which has some special requirements. This loss requires you set the sample rate as well as specify the correct device.

``````sample_rate = 44100
melstft_loss = auraloss.freq.MelSTFTLoss(sample_rate, device="cuda")
``````

You can also build a multi-resolution Mel-scaled STFT loss with 64 bins easily. Make sure you pass the correct device where the tensors you are comparing will be.

``````mrmelstft_loss = auraloss.freq.MultiResolutionSTFTLoss(scale="mel",
n_bins=64,
sample_rate=sample_rate,
device="cuda")
``````

# Development

We currently have no tests, but those will also be coming soon, so use caution at the moment. Future loss functions to be included will target neural network based perceptual losses, which tend to be a bit more sophisticated than those we have included so far.

If you are interested in adding a loss function please make a pull request.

# Cite

If you use this code in your work please consider citing us.

``````@inproceedings{steinmetz2020auraloss,
title={auraloss: {A}udio focused loss functions in {PyTorch}},
author={Steinmetz, Christian J. and Reiss, Joshua D.},
booktitle={Digital Music Research Network One-day Workshop (DMRN+15)},
year={2020}}
``````

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