Abstract
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 dimensions, 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.
| Original language | English (US) |
|---|---|
| Title of host publication | 131st Audio Engineering Society Convention 2011 |
| Pages | 992-999 |
| Number of pages | 8 |
| Volume | 2 |
| State | Published - 2011 |
| Event | 131st Audio Engineering Society Convention 2011 - New York, NY, United States Duration: Oct 20 2011 → Oct 23 2011 |
Other
| Other | 131st Audio Engineering Society Convention 2011 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 10/20/11 → 10/23/11 |
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ASJC Scopus subject areas
- Modeling and Simulation
- Acoustics and Ultrasonics
Cite this
Automatic soundscape classification via comparative psychometrics and machine learning. / Rajagopal, Krithika; Minnick, Phil; Leider, Colby.
131st Audio Engineering Society Convention 2011. Vol. 2 2011. p. 992-999.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Automatic soundscape classification via comparative psychometrics and machine learning
AU - Rajagopal, Krithika
AU - Minnick, Phil
AU - Leider, Colby
PY - 2011
Y1 - 2011
N2 - 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 dimensions, 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.
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 dimensions, 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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M3 - Conference contribution
AN - SCOPUS:84866342101
SN - 9781618393968
VL - 2
SP - 992
EP - 999
BT - 131st Audio Engineering Society Convention 2011
ER -