Automatic Soundscape Classification via Comparative Psychometrics and Machine Learning

Rajagopal K, Minnick P, Leider C. Automatic Soundscape Classification via Comparative Psychometrics and Machine Learning. In: AES Convention 131. Audio Engineering Society; 2011.pp. 8581:1-. Available from: https://aes.org/publications/elibrary-page/?id=16106

@inproceedings{Rajagopal2011_16106,
  author = {Rajagopal, Krithika and Minnick, Phil and Leider, Colby},
  title = {{Automatic Soundscape Classification via Comparative Psychometrics and Machine Learning}},
  booktitle = {AES Convention 131},
  pages = {8581:1-},
  year = {2011},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=16106}
}

TY  - CPAPER
TI  - Automatic Soundscape Classification via Comparative Psychometrics and Machine Learning
AU  - Rajagopal, Krithika
AU  - Minnick, Phil
AU  - Leider, Colby
T2  - AES Convention 131
SP  - 1
M1  - Paper 8581
PY  - 2011
DA  - 2011/10/06
UR  - https://aes.org/publications/elibrary-page/?id=16106
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -