Investigation of Acoustic and Visual Features for Frog Call Classification
Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xie, Jie [verfasserIn] Towsey, Michael [verfasserIn] Zhang, Jinglan [verfasserIn] Roe, Paul [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 92(2019), 1 vom: 26. Feb., Seite 23-36 |
|---|---|
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volume:92 ; year:2019 ; number:1 ; day:26 ; month:02 ; pages:23-36 |
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10.1007/s11265-019-1445-4 |
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| 520 | |a Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. | ||
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| 700 | 1 | |a Roe, Paul |e verfasserin |4 aut | |
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10.1007/s11265-019-1445-4 doi (DE-627)SPR018334326 (SPR)s11265-019-1445-4-e DE-627 ger DE-627 rakwb eng Xie, Jie verfasserin aut Investigation of Acoustic and Visual Features for Frog Call Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. Bioacoustics (dpeaa)DE-He213 Ecoacoustics (dpeaa)DE-He213 Acoustic feature extraction (dpeaa)DE-He213 Visual feature extraction (dpeaa)DE-He213 Frog call classification (dpeaa)DE-He213 Towsey, Michael verfasserin aut Zhang, Jinglan verfasserin aut Roe, Paul verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 92(2019), 1 vom: 26. Feb., Seite 23-36 (DE-627)SPR018308090 nnns volume:92 year:2019 number:1 day:26 month:02 pages:23-36 https://dx.doi.org/10.1007/s11265-019-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 92 2019 1 26 02 23-36 |
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10.1007/s11265-019-1445-4 doi (DE-627)SPR018334326 (SPR)s11265-019-1445-4-e DE-627 ger DE-627 rakwb eng Xie, Jie verfasserin aut Investigation of Acoustic and Visual Features for Frog Call Classification 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. Bioacoustics (dpeaa)DE-He213 Ecoacoustics (dpeaa)DE-He213 Acoustic feature extraction (dpeaa)DE-He213 Visual feature extraction (dpeaa)DE-He213 Frog call classification (dpeaa)DE-He213 Towsey, Michael verfasserin aut Zhang, Jinglan verfasserin aut Roe, Paul verfasserin aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 92(2019), 1 vom: 26. Feb., Seite 23-36 (DE-627)SPR018308090 nnns volume:92 year:2019 number:1 day:26 month:02 pages:23-36 https://dx.doi.org/10.1007/s11265-019-1445-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 92 2019 1 26 02 23-36 |
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Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. |
| abstractGer |
Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. |
| abstract_unstemmed |
Abstract Rapid decreases in frog populations have been spotted worldwide, which are regarded as one of the most critical threats to the global biodiversity. Recent advances in acoustic sensors provide a novel way to assess frog vocalizations and further optimize the global protection policy. Specifically, frog populations can be reflected by detecting frog species using collected recordings. Previous studies have explored various acoustic features for classifying frog calls. However, few studies investigate visual features for frog call classification, which have been successfully used in acoustic event detection, speech/speaker recognition. In this study, various acoustic and visual features are proposed for frog call classification: MPEG-7 audio descriptor, syllable duration, oscillation rate, entropy related features, linear prediction codings, Mel-frequency Cepstral coefficients, local binary patterns, and histogram of oriented gradients. After segmenting continuous frog calls into individual syllables, different constructed feature sets are evaluated with a k-nearest neighbor classifier and support vector machines. Comprehensive results on 16 frog species demonstrate the effectiveness of both acoustic and visual features for classifying frog calls. |
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Investigation of Acoustic and Visual Features for Frog Call Classification |
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https://dx.doi.org/10.1007/s11265-019-1445-4 |
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Towsey, Michael Zhang, Jinglan Roe, Paul |
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10.1007/s11265-019-1445-4 |
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2024-07-03T18:57:40.551Z |
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