Automatic Soundscape Classification via Comparative Psychometrics and Machine Learning
Computational acoustical ecology is a relatively new field in which long-term environmental recordings are mined for meaningful data. Humans quite naturally and automatically associate environmental sounds with emotions and can easily identify the components of a soundscape. However, equipping a computer to accurately and automatically rate unknown environmental recordings along subjective psychoacoustic di-mensions, let alone report the environment (e.g., beach, barnyard, home kitchen, research lab, etc.) in which the environmental recordings were made with a high degree of accuracy is quite difficult. We present here a robust algorithm for automatic soundscape classification in which both psychometric data and computed audio features are compared and used to train a Naive Bayesian classifier. An algorithm for classifying the type of soundscape across different categories was developed. In a pilot test, automatic classification accuracy of 88% was achieved on 20 soundscapes, and the classifier was able to outperform human ratings in some tests. In a second test, classification accuracy of 95% was achieved on 30 soundscapes.
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