Presenting the Acoustic Sounds for Wellbeing Dataset and Baseline Classification Results
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The field of sound healing includes ancient practices coming from a broad range of cultures. Across such practices there is a variety of instrumentation utilised. Practitioners suggest the ability of sound to target both mental and even physical health issues, e.g., chronic-stress, or joint-pain. Instruments including the Tibetan singing bowl and vocal chanting, are methods which are still widely encouraged today. With the noise-floor of modern urban soundscapes continually increasing and known to impact wellbeing, methods to approve daily soundscapes are needed. With this in mind, this study presents the Acoustic Sounds for Wellbeing (ASW) dataset. The ASW dataset is a dataset gathered from YouTube including 88+ hrs of audio from 5-classes of acoustic instrumentation (Chimes, Chanting, Drumming, Gongs, and Singing Bowl). We additionally present initial baseline classification results on the dataset, finding that conventional Mel-Frequency Cepstra coefficient features achieve at best an unweighted average recalled of 57.4% for a 5-class support vector machine classification task.
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