  The field of sound healing includes ancient practices coming from a broad
range of cultures. Across such practices there is a variety of acoustic
instrumentation utilised. Practitioners suggest that sound has the ability 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 still widely used today. With the noise-floor of modern urban soundscapes
continually increasing and known to impact wellbeing, methods to improve this
are needed. With that 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 (Gongs,
Drumming, Singing Bowls, and Chanting). 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
paradigm.
