The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data
<|reference_start|>The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio Benchmarks and Novel Data: 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.<|reference_end|>
@article{baird2024the,
    title={The NeurIPS 2023 Machine Learning for Audio Workshop: Affective Audio
  Benchmarks and Novel Data},
    author={Alice Baird, Rachel Manzelli, Panagiotis Tzirakis, Chris Gagne, Haoqi
  Li, Sadie Allen, Sander Dieleman, Brian Kulis, Shrikanth S. Narayanan, Alan
  Cowen},
    journal={arXiv preprint arXiv:2403.14048},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14048},
    primaryClass={cs.SD cs.CL eess.AS}
}