Seeing Sound Dataset v1.0.1

<p>This is dataset contains the synthesized soundscapes and crowdsourced audio annotations that accompany the paper,</p> <blockquote> <p>M. Cartwright, A. Seals, J. Salamon, A. Williams, S. Mikloska, D. MacConnell, E. Law, J. Bello, and O. Nov. "Seeing sound: Investigating the effects of visualizations and complexity on crowdsourced audio annotations." In <em>Proceedings of the ACM on Human-Computer Interaction</em>, 1(2), 2017. https://doi.org/10.1145/3134664</p> </blockquote> <p>which investigates the effects of soundscape complexity and sound visualizations on the quality and speed of annotations of sound events (i.e. start time, end time, sound class, and proximity).</p> <p>In this dataset, we varied the soundscape complexity along two dimensions: maximum polyphony (3 levels) and Gini polyphony (2 levels). Maximum polyphony is the maximum number of sound events that occurred simultaneously in the soundscape. Gini polyphony is a measure of the concentration of sound events. For each of the 6 (3 x 2) combinations of complexity levels, we synthesized 10 soundscapes, each of which was 10 seconds long, for a total of 60 soundscapes. Each soundscape was annotated by 90 participants from Amazon's Mechanical Turk. Of these 90 participants, 30 were aided by waveform visualization, 30 were aided by a spectrogram visualization, and 30 did not have any visualization aid. For more details on how this data was collected, please refer to the paper.</p>