A Study of Transfer Learning in Music Source Separation

10/23/2020
by   Andreas Bugler, et al.
0

Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data. While some music domains have enough data suitable for training a separation system, such as rock and pop genres, many musical domains do not, such as classical music, choral music, and non-Western music traditions. It is well known that transferring learning from related domains can result in a performance boost for deep learning systems, but it is not always clear how best to do pretraining. In this work we investigate the effectiveness of data augmentation during pretraining, the impact on performance as a result of pretraining and downstream datasets having similar content domains, and also explore how much of a model must be retrained on the final target task, once pretrained.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

09/18/2019

Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity

Music source separation performance has greatly improved in recent years...
08/06/2020

Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation

Blind music source separation has been a popular and active subject of r...
10/13/2021

Singer separation for karaoke content generation

Due to the rapid development of deep learning, we can now successfully s...
01/15/2019

Spectrogram Feature Losses for Music Source Separation

In this paper we study deep learning-based music source separation, and ...
06/16/2021

A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation

This paper describes a hands-on comparison on using state-of-the-art mus...
02/17/2020

Addressing the confounds of accompaniments in singer identification

Identifying singers is an important task with many applications. However...
10/25/2021

Unsupervised Source Separation By Steering Pretrained Music Models

We showcase an unsupervised method that repurposes deep models trained f...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.