Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques
The goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both rapid on-site and remote...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4797 |
| _version_ | 1797598397710991360 |
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| author | Roberto Benocci Andrea Afify Andrea Potenza H. Eduardo Roman Giovanni Zambon |
| author_facet | Roberto Benocci Andrea Afify Andrea Potenza H. Eduardo Roman Giovanni Zambon |
| author_sort | Roberto Benocci |
| collection | DOAJ |
| description | The goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both rapid on-site and remote surveys. The soundscape ranking index (SRI), introduced by us recently, can empirically account for the contribution of different sound sources by assigning a positive weight to natural sounds (biophony) and a negative weight to anthropogenic ones. The optimization of such weights was performed by training four machine learning algorithms (decision tree, DT; random forest, RF; adaptive boosting, AdaBoost; support vector machine, SVM) over a relatively small fraction of a labeled sound recording dataset. The sound recordings were taken at 16 sites distributed over an area of approximately 22 hectares at Parco Nord (Northern Park) of the city Milan (Italy). From the audio recordings, we extracted four different spectral features: two based on ecoacoustic indices and the other two based on mel-frequency cepstral coefficients (MFCCs). The labeling was focused on the identification of sounds belonging to biophonies and anthropophonies. This preliminary approach revealed that two classification models, DT and AdaBoost, trained by using 84 extracted features from each recording, are able to provide a set of weights characterized by a rather good classification performance (F1-score = 0.70, 0.71). The present results are in quantitative agreement with a self-consistent estimation of the mean SRI values at each site that was recently obtained by us using a different statistical approach. |
| first_indexed | 2024-03-11T03:20:38Z |
| format | Article |
| id | doaj.art-a17a8a14920b45229b132b52122a61f4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:20:38Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a17a8a14920b45229b132b52122a61f42023-11-18T03:12:42ZengMDPI AGSensors1424-82202023-05-012310479710.3390/s23104797Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning TechniquesRoberto Benocci0Andrea Afify1Andrea Potenza2H. Eduardo Roman3Giovanni Zambon4Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, ItalyDepartment of Physics, University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, ItalyDepartment of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, ItalyDepartment of Physics, University of Milano-Bicocca, Piazza della Scienza 3, 20126 Milano, ItalyDepartment of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, ItalyThe goal of estimating a soundscape index, aimed at evaluating the contribution of the environmental sound components, is to provide an accurate “acoustic quality” assessment of a complex habitat. Such an index can prove to be a powerful ecological tool associated with both rapid on-site and remote surveys. The soundscape ranking index (SRI), introduced by us recently, can empirically account for the contribution of different sound sources by assigning a positive weight to natural sounds (biophony) and a negative weight to anthropogenic ones. The optimization of such weights was performed by training four machine learning algorithms (decision tree, DT; random forest, RF; adaptive boosting, AdaBoost; support vector machine, SVM) over a relatively small fraction of a labeled sound recording dataset. The sound recordings were taken at 16 sites distributed over an area of approximately 22 hectares at Parco Nord (Northern Park) of the city Milan (Italy). From the audio recordings, we extracted four different spectral features: two based on ecoacoustic indices and the other two based on mel-frequency cepstral coefficients (MFCCs). The labeling was focused on the identification of sounds belonging to biophonies and anthropophonies. This preliminary approach revealed that two classification models, DT and AdaBoost, trained by using 84 extracted features from each recording, are able to provide a set of weights characterized by a rather good classification performance (F1-score = 0.70, 0.71). The present results are in quantitative agreement with a self-consistent estimation of the mean SRI values at each site that was recently obtained by us using a different statistical approach.https://www.mdpi.com/1424-8220/23/10/4797soundscapeecoacoustic indicessoundscape ranking index (SRI)urban parksmachine learning |
| spellingShingle | Roberto Benocci Andrea Afify Andrea Potenza H. Eduardo Roman Giovanni Zambon Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques Sensors soundscape ecoacoustic indices soundscape ranking index (SRI) urban parks machine learning |
| title | Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques |
| title_full | Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques |
| title_fullStr | Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques |
| title_full_unstemmed | Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques |
| title_short | Toward the Definition of a Soundscape Ranking Index (SRI) in an Urban Park Using Machine Learning Techniques |
| title_sort | toward the definition of a soundscape ranking index sri in an urban park using machine learning techniques |
| topic | soundscape ecoacoustic indices soundscape ranking index (SRI) urban parks machine learning |
| url | https://www.mdpi.com/1424-8220/23/10/4797 |
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