We propose a dataset of audio samples called Emo-Soundscapes and two evaluation protocols for machine learning models. We curated 600 soundscape recordings in Freesound.org and mixed 613 audio clips from a combination of these. The Emo- Soundscapes dataset contains 1213 6-second Creative Commons licensed audio clips. We collected the ground truth annotations of perceived emotion in 1213 soundscape recordings using crowdsourcing listening experiment, where 1182 annotators from 74 different countries rank the audio clips according to the perceived valence/arousal. This dataset allows studying soundscape emotion recognition and how the mixing of various soundscape recordings influences their perceived emotion.

Download Link: Emo-Soundscapes

Papers & Posters:

  • J. Fan, M. Thorogood, and P. Pasquier, “Emo-Soundscapes- A Dataset for Soundscape Emotion Recognition,” Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 2017

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