Audiovisual Transformer Architectures for Large-Scale Classification and Synchronization of Weakly Labeled Audio Events

TL;DR

This paper introduces modified audiovisual transformer models that leverage both audio and visual data for environmental sound classification, achieving state-of-the-art results and demonstrating potential for multimodal synchronization.

Contribution

It presents novel transformer architectures adapted for audiovisual data, improving classification accuracy and enabling synchronization in weakly labeled, large-scale datasets.

Findings

Achieved state-of-the-art classification performance.

Visual information significantly improves audio event recognition.

Demonstrated potential for multimodal synchronization.

Abstract

We tackle the task of environmental event classification by drawing inspiration from the transformer neural network architecture used in machine translation. We modify this attention-based feedforward structure in such a way that allows the resulting model to use audio as well as video to compute sound event predictions. We perform extensive experiments with these adapted transformers on an audiovisual data set, obtained by appending relevant visual information to an existing large-scale weakly labeled audio collection. The employed multi-label data contains clip-level annotation indicating the presence or absence of 17 classes of environmental sounds, and does not include temporal information. We show that the proposed modified transformers strongly improve upon previously introduced models and in fact achieve state-of-the-art results. We also make a compelling case for devoting more attention to research in multimodal audiovisual classification by proving the usefulness of visual information for the task at hand,namely audio event recognition. In addition, we visualize internal attention patterns of the audiovisual transformers and in doing so demonstrate their potential for performing multimodal synchronization.