<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The bioacoustic analyses of animal sounds result in an enormous amount of digitized acoustic data, and we need effective automatic processing to extract the information content of the recordings. Our research focuses on the song of Collared Flycatcher <italic>(Ficedula albicollis)</italic> and we are interested in the evolution of acoustic signals. During the last 20 years, we obtained hundreds of hours of recordings of bird songs collected in natural environment, and there is a permanent need for the automatic process of recordings. In this study, we chose an open-source, deep-learning image detection system to (1) find the species-specific songs of the Collared Flycatcher on the recordings and (2) to detect the small, discrete elements so-called syllables within the song. For these tasks, we first transformed the acoustic data into spectrogram images, then we trained two deep-learning models separately on our manually segmented database. The resulted models detect the songs with an intersection of union higher than 0.8 and the syllables higher than 0.7. This technique anticipates an order of magnitude less human effort in the acoustic processing than the manual method used before. Thanks to the new technique, we are able to address new biological questions that need large amount of acoustic data.</p></abstract>

The bioacoustic analyses of animal sounds result in an enormous amount of digitized acoustic data, and we need effective automatic processing to extract the information content of the recordings. Our research focuses on the song of Collared Flycatcher (Ficedula albicollis) and we are interested in the evolution of acoustic signals. During the last 20 years, we obtained hundreds of hours of recordings of bird songs collected in natural environment, and there is a permanent need for the automatic process of recordings. In this study, we chose an open-source, deep-learning image detection system to (1) find the species-specific songs of the Collared Flycatcher on the recordings and (2) to detect the small, discrete elements so-called syllables within the song. For these tasks, we first transformed the acoustic data into spectrogram images, then we trained two deep-learning models separately on our manually segmented database. The resulted models detect the songs with an intersection of union higher than 0.8 and the syllables higher than 0.7. This technique anticipates an order of magnitude less human effort in the acoustic processing than the manual method used before. Thanks to the new technique, we are able to address new biological questions that need large amount of acoustic data.

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher <italic>(Ficedula albicollis)</italic>

Behavioural Ecology Group, Department of Systematic Zoology and Ecology

MTA-ELTE, Theoretical Biology and Evolutionary Ecology Research Group, Department of Plant Systematics, Ecology and Theoretical Biology

Evolutionary Ecology Group, Centre for Ecological Research, Institute of Ecology and Botany

Ornis Hungarica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher 

The bioacoustic analyses of animal sounds result in an enormous amount of digitized acoustic data, and we need effective automatic processing to extract the...

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

Online veröffentlicht: 16. Dez. 2019

Seitenbereich: 59 - 66

Eingereicht: 12. Sept. 2019

Akzeptiert: 21. Okt. 2019

DOI: https://doi.org/10.2478/orhu-2019-0015

Schlüsselwörter
bird song, deep-learning, object detection, Collared Flycatcher, automatic segmentation

© 2019 Sándor Zsebők et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Automatic bird song and syllable segmentation with an open-source deep-learning object detection method – a case study in the Collared Flycatcher (Ficedula albicollis)

Online veröffentlicht: 16. Dez. 2019

Seitenbereich: 59 - 66

Eingereicht: 12. Sept. 2019

Akzeptiert: 21. Okt. 2019

DOI: https://doi.org/10.2478/orhu-2019-0015

Schlüsselwörterbird song, deep-learning, object detection, Collared Flycatcher, automatic segmentation

© 2019 Sándor Zsebők et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
bird song, deep-learning, object detection, Collared Flycatcher, automatic segmentation