PROTAX-Sound: A probabilistic framework for automated animal sound identification.
Autonomous audio recording is stimulating new field in bioacoustics, with a great promise for conducting cost-effective species surveys. One major current challenge is the lack of reliable classifiers capable of multi-species identification. We present PROTAX-Sound, a statistical framework to perfor...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5581177?pdf=render |
| _version_ | 1818133601059864576 |
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| author | Ulisses Moliterno de Camargo Panu Somervuo Otso Ovaskainen |
| author_facet | Ulisses Moliterno de Camargo Panu Somervuo Otso Ovaskainen |
| author_sort | Ulisses Moliterno de Camargo |
| collection | DOAJ |
| description | Autonomous audio recording is stimulating new field in bioacoustics, with a great promise for conducting cost-effective species surveys. One major current challenge is the lack of reliable classifiers capable of multi-species identification. We present PROTAX-Sound, a statistical framework to perform probabilistic classification of animal sounds. PROTAX-Sound is based on a multinomial regression model, and it can utilize as predictors any kind of sound features or classifications produced by other existing algorithms. PROTAX-Sound combines audio and image processing techniques to scan environmental audio files. It identifies regions of interest (a segment of the audio file that contains a vocalization to be classified), extracts acoustic features from them and compares with samples in a reference database. The output of PROTAX-Sound is the probabilistic classification of each vocalization, including the possibility that it represents species not present in the reference database. We demonstrate the performance of PROTAX-Sound by classifying audio from a species-rich case study of tropical birds. The best performing classifier achieved 68% classification accuracy for 200 bird species. PROTAX-Sound improves the classification power of current techniques by combining information from multiple classifiers in a manner that yields calibrated classification probabilities. |
| first_indexed | 2024-12-11T08:55:19Z |
| format | Article |
| id | doaj.art-5f24bb0fa69341ed8ba9989cb57e1142 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-11T08:55:19Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-5f24bb0fa69341ed8ba9989cb57e11422022-12-22T01:13:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018404810.1371/journal.pone.0184048PROTAX-Sound: A probabilistic framework for automated animal sound identification.Ulisses Moliterno de CamargoPanu SomervuoOtso OvaskainenAutonomous audio recording is stimulating new field in bioacoustics, with a great promise for conducting cost-effective species surveys. One major current challenge is the lack of reliable classifiers capable of multi-species identification. We present PROTAX-Sound, a statistical framework to perform probabilistic classification of animal sounds. PROTAX-Sound is based on a multinomial regression model, and it can utilize as predictors any kind of sound features or classifications produced by other existing algorithms. PROTAX-Sound combines audio and image processing techniques to scan environmental audio files. It identifies regions of interest (a segment of the audio file that contains a vocalization to be classified), extracts acoustic features from them and compares with samples in a reference database. The output of PROTAX-Sound is the probabilistic classification of each vocalization, including the possibility that it represents species not present in the reference database. We demonstrate the performance of PROTAX-Sound by classifying audio from a species-rich case study of tropical birds. The best performing classifier achieved 68% classification accuracy for 200 bird species. PROTAX-Sound improves the classification power of current techniques by combining information from multiple classifiers in a manner that yields calibrated classification probabilities.http://europepmc.org/articles/PMC5581177?pdf=render |
| spellingShingle | Ulisses Moliterno de Camargo Panu Somervuo Otso Ovaskainen PROTAX-Sound: A probabilistic framework for automated animal sound identification. PLoS ONE |
| title | PROTAX-Sound: A probabilistic framework for automated animal sound identification. |
| title_full | PROTAX-Sound: A probabilistic framework for automated animal sound identification. |
| title_fullStr | PROTAX-Sound: A probabilistic framework for automated animal sound identification. |
| title_full_unstemmed | PROTAX-Sound: A probabilistic framework for automated animal sound identification. |
| title_short | PROTAX-Sound: A probabilistic framework for automated animal sound identification. |
| title_sort | protax sound a probabilistic framework for automated animal sound identification |
| url | http://europepmc.org/articles/PMC5581177?pdf=render |
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