AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Avian Research Article
PDF (4.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet

Jiangjian Xiea,b,c,d,1, Zhulin Haoa,1, Chunhe Hua,c,d, Changchun Zhanga,c,d, Junguo Zhanga,b,c( )
School of Technology, Beijing Forestry University, Beijing, 100083, China
State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, 100083, China
Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation, Beijing, 100083, China
Research Center for Biodiversity Intelligent Monitoring, Beijing Forestry University, Beijing, 100083, China

1 The two authors contribute equally to this work.

Peer review under the responsibility of Editorial Office of Avian Research.

Show Author Information

Abstract

Bird vocalizations are pivotal for ecological monitoring, providing insights into biodiversity and ecosystem health. Traditional recognition methods often neglect phase information, resulting in incomplete feature representation. In this paper, we introduce a novel approach to bird vocalization recognition (BVR) that integrates both amplitude and phase information, leading to enhanced species identification. We propose MHAResNet, a deep learning (DL) model that employs residual blocks and a multi-head attention mechanism to capture salient features from logarithmic power (POW), Instantaneous Frequency (IF), and Group Delay (GD) extracted from bird vocalizations. Experiments on three bird vocalization datasets demonstrate our method’s superior performance, achieving accuracy rates of 94%, 98.9%, and 87.1% respectively. These results indicate that our approach provides a more effective representation of bird vocalizations, outperforming existing methods. This integration of phase information in BVR is innovative and significantly advances the field of automatic bird monitoring technology, offering valuable tools for ecological research and conservation efforts.

Keywords

Bird vocalization recognitionFeature fusionPhase informationResidual network

References

 

Dvořáková, D., Šipoš, J., Suchomel, J., 2023. Impact of agricultural landscape structure on the patterns of bird species diversity at a regional scale. Avian Res. 14, 100147.
Crossref Google Scholar
 

Eisele, J., Gerlach, A., Maeder, M., Marburg, S., 2024. Relevance of phase information for object classification in automotive ultrasonic sensing using convolutional neural networks. J. Acoust. Soc. Am. 155, 1060–1070.
Crossref Google Scholar
 

Feng, Y., Xiao, Y., Yan, Y., Max, L., 2018. Adaptation in Mandarin tone production with pitch-shifted auditory feedback: influence of tonal contrast requirements. Lang. Cogn. Neurosci. 33, 734–749.
Crossref Google Scholar
 

Florentin, J., Dutoit, T., Verlinden, O., 2020. Detection and identification of European woodpeckers with deep convolutional neural networks. Ecol. Inform. 55, 101023.
Crossref Google Scholar
 

Ge, R., Kakade, S.M., Kidambi, R., Netrapalli, P., 2019. The step decay schedule: a near optimal, geometrically decaying learning rate procedure for least squares. Adv. Neural Inf. Process. Syst. 32.
Google Scholar
 

Gupta, G., Kshirsagar, M., Zhong, M., Gholami, S., Ferres, J.L., 2021. Comparing recurrent convolutional neural networks for large scale bird species classification. Sci. Rep. 11, 17085.
Crossref Google Scholar
 

He, F., Liu, T., Tao, D., 2020. Why resnet works? residuals generalize. IEEE Transact. Neural Networks Learn. Syst. 31, 5349–5362.
Crossref Google Scholar
 
He, K., Zhang, X., Ren, S., Sun, J., 2016. In: Deep residual learning for image recognition. IEEE, pp. 770–778.
Crossref
 
Hidaka, S., Wakamiya, K., Kaburagi, T., 2022. An investigation of the effectiveness of phase for audio classification. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.
Crossref
 

Hu, S., Chu, Y., Tang, L., Zhou, G., Chen, A., Sun, Y., 2023a. A lightweight multi-sensory field-based dual-feature fusion residual network for bird song recognition. Appl. Soft Comput. 146, 110678.
Crossref Google Scholar
 

Hu, S., Chu, Y., Wen, Z., Zhou, G., Sun, Y., Chen, A., 2023b. Deep learning bird song recognition based on mff-scsenet. Ecol. Indic. 154, 110844.
Crossref Google Scholar
 

Jiang, D., Hu, Y., Dai, L., Peng, J., 2021. Facial expression recognition based on attention mechanism. Sci. Program. 2021, 2021.
Crossref Google Scholar
 

Kahl, S., Wood, C.M., Eibl, M., Klinck, H., 2021. Birdnet: a deep learning solution for avian diversity monitoring. Ecol. Inform. 61, 101236.
Crossref Google Scholar
 

Kasten, E.P., Gage, S.H., Fox, J., Joo, W., 2012. The remote environmental assessment laboratory*s acoustic library: an archive for studying soundscape ecology. Ecol. Inform. 12, 50–67.
Crossref Google Scholar
 
Kinsler, L.E., Frey, A.R., Coppens, A.B., Sanders, J.V., 1999. Fundamentals of Acoustics, fourth ed. Wiley, New York.
 
Koh, C.Y., Chang, J.Y., Tai, C.L., Huang, D.Y., Hsieh, H.H., Liu, Y.W., 2019. In: Bird sound classification using convolutional neural networks. Conference and Labs of the Evaluation Forum. https://ceur-ws.org/Vol-2380/paper_68.pdf (Accessed 20 July 2024).
 

Lin, X., Liu, J., Kang, X., 2016. Audio recapture detection with convolutional neural networks. IEEE Transact. Multimed. 18, 1480–1487.
Crossref Google Scholar
 

Lu, J., Zhang, Y., Lv, D., Xie, S., Fu, Y., Lv, D., et al., 2023. Improved broad learning system for birdsong recognition. Appl. Sci. 13, 11009.
Crossref Google Scholar
 

Ma, K., 2016. Biodiversity monitoring relies on the integration of human observation and automatic collection of data with advanced equipment and facilities. Biodivers. Sci. 24, 1201.
Crossref Google Scholar
 

Manna, A., Upasani, N., Jadhav, S., Mane, R., Chaudhari, R., Chatre, V., 2023. Bird image classification using convolutional neural network transfer learning architectures. Int. J. Adv. Comput. Sci. Appl. 14.
Crossref Google Scholar
 

Nemeth, E., Pieretti, N., Zollinger, S.A., Geberzahn, N., Partecke, J., Miranda, A.C., et al., 2013. Bird song and anthropogenic noise: vocal constraints may explain why birds sing higher-frequency songs in cities. Proc. R. Soc. B 280, 20122798.
Crossref Google Scholar
 

Noumida, A., Rajan, R., 2022. Multi-label bird species classification from audio recordings using attention framework. Appl. Acoust. 197, 108901.
Crossref Google Scholar
 

de Oliveira, A.G., Ventura, T.M., Ganchev, T.D., Silva, L.N., Marques, M.I., Schuchmann, K.L., 2020. Speeding up training of automated bird recognizers by data reduction of audio features. PeerJ 8, e8407.
Crossref Google Scholar
 

Pahuja, R., Kumar, A., 2021. Sound-spectrogram based automatic bird species recognition using mlp classifier. Appl. Acoust. 180, 108077.
Crossref Google Scholar
 

Ptacek, L., Machlica, L., Linhart, P., Jaska, P., Muller, L., 2016. Automatic recognition of bird individuals on an open set using as-is recordings. Bioacoustics 25, 55–73.
Crossref Google Scholar
 

Ruff, J., Lesmeister, D., Duchac, L., Padmaraju, B., Sullivan, C., 2019. Automated identification of avian vocalizations with deep convolutional neural networks. Remote Sens. Ecol. Conserv. 6, 79–92.
Crossref Google Scholar
 

Schwab, E., Pogrebnoj, S., Freund, M., Flossmann, F., Vogl, S., Frommolt, K.H., 2023. Automated bat call classification using deep convolutional neural networks. Bioacoustics 32, 1–16.
Crossref Google Scholar
 

Sedlácek, O., Vokurková, J., Ferenc, M., Djomo, E., Albrecht, T., Hořák, D., 2015. A comparison of point counts with a new acoustic sampling method: a case study of a bird community from the montane forests of mount Cameroon. Ostrich 86, 213–220.
Crossref Google Scholar
 
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., 2017. In: Grad-cam: visual explanations from deep networks via gradient-based localization. IEEE, pp. 618–626.
Crossref
 

Sinha, H., Awasthi, V., Ajmera, P., 2020. Audio classification using braided convolutional neural networks. IET Signal Process. 14, 448–454.
Crossref Google Scholar
 

Tanzi, L., Audisio, A., Cirrincione, G., Aprato, A., Vezzetti, E., 2022. Vision transformer for femur fracture classification. Injury 53, 2625–2634.
Crossref Google Scholar
 

Wheeldon, A., Mossman, H.L., Sullivan, M.J., Mathenge, J., de Kort, S.R., 2019. Comparison of acoustic and traditional point count methods to assess bird diversity and composition in the Aberdare National Park, Kenya. Afr. J. Ecol. 57, 168–176.
Crossref Google Scholar
 

Xiao, H., Liu, D., Avolio, A.P., Chen, K., Li, D., Hu, B., et al., 2022a. Estimation of cardiac stroke volume from radial pulse waveform by artificial neural network. Comput. Methods Progr. Biomed. 218, 106738.
Crossref Google Scholar
 

Xiao, H., Liu, D., Chen, K., Zhu, M., 2022b. Amresnet: an automatic recognition model of bird sounds in real environment. Appl. Acoust. 201, 109121.
Crossref Google Scholar
 
Xiao, H., Ran, Z., Mabu, S., Li, Y., Li, L., 2022c. Saunet++: an Automatic Segmentation Model of Covid-19 Lesion from Ct Slices, 39. Visual Comput, pp. 2291–2304.
Crossref
 

Xie, J., Hu, K., Zhu, M., Yu, J., Zhu, Q., 2019. Investigation of different cnn-based models for improved bird sound classification. IEEE Access 7, 175353–175361.
Crossref Google Scholar
 

Xie, J., Zhong, Y., Zhang, J., Liu, S., Ding, C., Triantafyllopoulos, A., 2023. A review of automatic recognition technology for bird vocalizations in the deep learning era. Ecol. Inform. 73, 101927.
Crossref Google Scholar
 

Xie, J., Zhu, M., 2023. Acoustic classification of bird species using an early fusion of deep features. Birds 4, 138–147.
Crossref Google Scholar
 

Xie, S., Lu, J., Liu, J., Zhang, Y., Lv, D., Chen, X., et al., 2022. Multi-view features fusion for birdsong classification. Ecol. Inform. 72, 101893.
Crossref Google Scholar
 

Xu, Y., Li, L., Gao, H., 2020. Sentiment classification with adversarial learning and attention mechanism. Comput. Intell. 37, 774–798.
Crossref Google Scholar
 

Yan, N., Chen, A., Zhou, G., Zhang, Z., Liu, X., Wang, J., et al., 2021. Birdsong classification based on multi-feature fusion. Multimed. Tool. Appl. 80, 36529–36547.
Crossref Google Scholar
 

Yang, F., Jiang, Y., Xu, Y., 2022. Design of bird sound recognition model based on lightweight. IEEE Access 10, 85189–85198.
Crossref Google Scholar
 

Zollinger, S.A., Podos, J., Nemeth, E., Goller, F., Brumm, H., 2012. On the relationship between, and measurement of, amplitude and frequency in birdsong. Anim. Behav. 84, e1–e9.
Crossref Google Scholar
Avian Research
Volume 16 Issue 1,
March 2025
DOI: 10.1016/j.avrs.2025.100229
Cite this article:
Xie J, Hao Z, Hu C, et al. Beyond amplitude: Phase integration in bird vocalization recognition with MHAResNet. Avian Research, 2025, 16(1). https://doi.org/10.1016/j.avrs.2025.100229

122

Views

5

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 26 September 2024
Revised: 22 January 2025
Accepted: 14 February 2025
Published: 15 February 2025
© 2025 The Authors.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Return