The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
Abstract
The NeurIPS 2023 Machine Learning for Audio Workshop brings together machine learning (ML) experts from various audio domains. There are several valuable audio-driven ML tasks, from speech emotion recognition to audio event detection, but the community is sparse compared to other ML areas, e.g., computer vision or natural language processing. A major limitation with audio is the available data; with audio being a time-dependent modality, high-quality data collection is time-consuming and costly, making it challenging for academic groups to apply their often state-of-the-art strategies to a larger, more generalizable dataset. In this short white paper, to encourage researchers with limited access to large-datasets, the organizers first outline several open-source datasets that are available to the community, and for the duration of the workshop are making several propriety datasets available. Namely, three vocal datasets, Hume-Prosody, Hume-VocalBurst, an acted emotional speech dataset Modulate-Sonata, and an in-game streamer dataset Modulate-Stream. We outline the current baselines on these datasets but encourage researchers from across audio to utilize them outside of the initial baseline tasks.
Cite
@article{arxiv.2403.14048,
title = {The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data},
author = {Alice Baird and Rachel Manzelli and Panagiotis Tzirakis and Chris Gagne and Haoqi Li and Sadie Allen and Sander Dieleman and Brian Kulis and Shrikanth S. Narayanan and Alan Cowen},
journal= {arXiv preprint arXiv:2403.14048},
year = {2024}
}