The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data

Deepmind London, Sander Dieleman, Alice Baird, Panagiotis Tzirakis, Alan Cowen, Rachel Manzelli, Chris Gagne, Haoqi Li, Sadie Allen, Shrikanth Narayanan

arXiv · PDF · Open in the Atlas

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• HumeAI/competitions

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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 largedatasets, 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.

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