Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning
Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qiu, Zhibin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of electrical engineering & technology - [Singapore] : Springer Singapore, 2006, 18(2022), 3 vom: 31. Okt., Seite 2447-2456 |
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| Übergeordnetes Werk: |
volume:18 ; year:2022 ; number:3 ; day:31 ; month:10 ; pages:2447-2456 |
| Links: |
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| DOI / URN: |
10.1007/s42835-022-01284-z |
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| Katalog-ID: |
SPR050239120 |
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| 520 | |a Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. | ||
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| 700 | 1 | |a Liao, Caibo |4 aut | |
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| 700 | 1 | |a Kuang, Yanjun |4 aut | |
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10.1007/s42835-022-01284-z doi (DE-627)SPR050239120 (SPR)s42835-022-01284-z-e DE-627 ger DE-627 rakwb eng Qiu, Zhibin verfasserin (orcid)0000-0003-1962-7906 aut Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 Wang, Haixiang aut Liao, Caibo aut Lu, Zuwen aut Kuang, Yanjun aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2022), 3 vom: 31. Okt., Seite 2447-2456 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2022 number:3 day:31 month:10 pages:2447-2456 https://dx.doi.org/10.1007/s42835-022-01284-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 3 31 10 2447-2456 |
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10.1007/s42835-022-01284-z doi (DE-627)SPR050239120 (SPR)s42835-022-01284-z-e DE-627 ger DE-627 rakwb eng Qiu, Zhibin verfasserin (orcid)0000-0003-1962-7906 aut Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 Wang, Haixiang aut Liao, Caibo aut Lu, Zuwen aut Kuang, Yanjun aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2022), 3 vom: 31. Okt., Seite 2447-2456 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2022 number:3 day:31 month:10 pages:2447-2456 https://dx.doi.org/10.1007/s42835-022-01284-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 3 31 10 2447-2456 |
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10.1007/s42835-022-01284-z doi (DE-627)SPR050239120 (SPR)s42835-022-01284-z-e DE-627 ger DE-627 rakwb eng Qiu, Zhibin verfasserin (orcid)0000-0003-1962-7906 aut Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 Wang, Haixiang aut Liao, Caibo aut Lu, Zuwen aut Kuang, Yanjun aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2022), 3 vom: 31. Okt., Seite 2447-2456 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2022 number:3 day:31 month:10 pages:2447-2456 https://dx.doi.org/10.1007/s42835-022-01284-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 3 31 10 2447-2456 |
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10.1007/s42835-022-01284-z doi (DE-627)SPR050239120 (SPR)s42835-022-01284-z-e DE-627 ger DE-627 rakwb eng Qiu, Zhibin verfasserin (orcid)0000-0003-1962-7906 aut Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 Wang, Haixiang aut Liao, Caibo aut Lu, Zuwen aut Kuang, Yanjun aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2022), 3 vom: 31. Okt., Seite 2447-2456 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2022 number:3 day:31 month:10 pages:2447-2456 https://dx.doi.org/10.1007/s42835-022-01284-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 3 31 10 2447-2456 |
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10.1007/s42835-022-01284-z doi (DE-627)SPR050239120 (SPR)s42835-022-01284-z-e DE-627 ger DE-627 rakwb eng Qiu, Zhibin verfasserin (orcid)0000-0003-1962-7906 aut Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 Wang, Haixiang aut Liao, Caibo aut Lu, Zuwen aut Kuang, Yanjun aut Enthalten in Journal of electrical engineering & technology [Singapore] : Springer Singapore, 2006 18(2022), 3 vom: 31. Okt., Seite 2447-2456 (DE-627)519202015 (DE-600)2255142-6 2093-7423 nnns volume:18 year:2022 number:3 day:31 month:10 pages:2447-2456 https://dx.doi.org/10.1007/s42835-022-01284-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2022 3 31 10 2447-2456 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR050239120</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230429064831.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230429s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42835-022-01284-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR050239120</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42835-022-01284-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Qiu, Zhibin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1962-7906</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. 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Qiu, Zhibin |
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Qiu, Zhibin misc Transmission line misc Bird-related outage misc Birdsong recognition misc Transfer learning misc Mel spectrogram Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning |
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Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning Transmission line (dpeaa)DE-He213 Bird-related outage (dpeaa)DE-He213 Birdsong recognition (dpeaa)DE-He213 Transfer learning (dpeaa)DE-He213 Mel spectrogram (dpeaa)DE-He213 |
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sound recognition of harmful bird species related to power grid faults based on vggish transfer learning |
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Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning |
| abstract |
Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Bird activities threaten the safe operation of transmission lines and substations. In order to assist differentiated prevention of bird-related faults in power grid, this paper proposes a birdsong recognition method based on VGGish transfer learning. Firstly, according to the information of bird species related to historical power grid faults and the investigation results of bird species around transmission lines, 18 high-risk, 18 low-risk, and 2 harmless bird species were selected to establish a sample set with their song signals. Then, the birdsong signals were preprocessed by framing, windowing, noise reduction and clipping, thus to extract the spectrogram, which was mapped to a 64-order Mel filter banks to get the Mel spectrogram. Aiming at weak generalization ability of traditional birdsong recognition models due to insufficient number of samples, the VGGish transfer learning network pretrained by AudioSet was used as the birdsong feature extractor, and the Mel spectrograms of harmful bird species belong to the training set were taken as inputs to train the network parameters, thus to extract 128-dimensional VGGish deep features for bird recognition. This method was applied to classify 38 kinds of bird species related to power grid faults, and the recognition accuracy reaches 94.43%. The research results can provide references for power grid inspector to carry out intelligent recognition and ecological prevention of bird species. © The Author(s) under exclusive licence to The Korean Institute of Electrical Engineers 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Sound Recognition of Harmful Bird Species Related to Power Grid Faults Based on VGGish Transfer Learning |
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Wang, Haixiang Liao, Caibo Lu, Zuwen Kuang, Yanjun |
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Wang, Haixiang Liao, Caibo Lu, Zuwen Kuang, Yanjun |
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| score |
7.401457 |