The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio
  Benchmarks and Novel Data
Alice Baird Rachel Manzelli Panagiotis Tzirakis Chris Gagne Haoqi Li Sadie Allen Sander Dieleman Brian Kulis Shrikanth S. Narayanan Alan Cowen
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.