Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers

11/25/2022
by   Khaled Koutini, et al.
0

The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale datasets are capable of capturing a diverse set of features, and learning a representation that can generalize onto unseen tasks and datasets that are from the same domain. Hence, these models can be used as powerful feature extractors, in combination with shallower models as classifiers, for smaller tasks and datasets where the amount of training data is insufficient for learning an end-to-end model from scratch. During the past years, Convolutional Neural Networks (CNNs) have largely been the method of choice for audio processing. However, recently attention-based transformer models have demonstrated great potential in supervised settings, outperforming CNNs. In this work, we investigate the use of audio transformers trained on large-scale datasets to learn general-purpose representations. We study how the different setups in these audio transformers affect the quality of their embeddings. We experiment with the models' time resolution, extracted embedding level, and receptive fields in order to see how they affect performance on a variety of tasks and datasets, following the HEAR 2021 NeurIPS challenge evaluation setup. Our results show that representations extracted by audio transformers outperform CNN representations. Furthermore, we will show that transformers trained on Audioset can be extremely effective representation extractors for a wide range of downstream tasks.

READ FULL TEXT

page 13

page 14

page 15

page 24

research
10/11/2021

Efficient Training of Audio Transformers with Patchout

The great success of transformer-based models in natural language proces...
research
10/24/2020

ReadOnce Transformers: Reusable Representations of Text for Transformers

While large-scale language models are extremely effective when directly ...
research
03/03/2023

Low-Complexity Audio Embedding Extractors

Solving tasks such as speaker recognition, music classification, or sema...
research
08/14/2023

Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers

We propose a shift towards end-to-end learning in bird sound monitoring ...
research
05/01/2021

Audio Transformers:Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions

Over the past two decades, CNN architectures have produced compelling mo...
research
03/01/2022

Tricks and Plugins to GBM on Images and Sequences

Convolutional neural networks (CNNs) and transformers, which are compose...

Please sign up or login with your details

Forgot password? Click here to reset