Abstract

Audio event detection is a widely studied field, with applications ranging from self-driving cars to healthcare. In-the-wild datasets such as Audioset have propelled research in this field. However, many efforts typically involve manual annotation and verification, which is expensive to perform at scale. Movies depict various real-life and fictional scenarios which makes them a rich resource for mining a wide range of audio events. In this work, we present a dataset of audio events called Subtitle-Aligned Movie Sounds (SAM-S). We use publicly available closed-caption transcripts to automatically mine over 110K audio events from 430 movies. We identify three dimensions to categorize audio events: sound, source, quality, and present the steps involved to produce a final taxonomy of 245 sounds. We discuss the choices involved in generating the taxonomy, and also highlight the human-centered nature of sounds in our …

Metadata

publication

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023

year

2023

publication date

2023/6/4

authors

Rajat Hebbar, Digbalay Bose, Krishna Somandepalli, Veena Vijai, Shrikanth Narayanan

link

https://ieeexplore.ieee.org/abstract/document/10094781/

resource_link

https://arxiv.org/pdf/2302.07315

conference

ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

pages

1-5

publisher

IEEE