Bird Call Identification Using Ensemble Empirical Mode Decomposition
Birds are iconic species of the environment. Bird monitoring can be achieved by collecting recordings of the calls of wild birds and later identifying the species. A new approach suggested in this study involves the application of ensemble empirical mode decomposition (EEMD) to analyze the time-freq...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/vib/5292138 |
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| author | Jingxuan Liu Hailan Chen |
| author_facet | Jingxuan Liu Hailan Chen |
| author_sort | Jingxuan Liu |
| collection | DOAJ |
| description | Birds are iconic species of the environment. Bird monitoring can be achieved by collecting recordings of the calls of wild birds and later identifying the species. A new approach suggested in this study involves the application of ensemble empirical mode decomposition (EEMD) to analyze the time-frequency characteristics of bird calls, along with assessing the variance ratio and correlation coefficient of intrinsic mode functions (IMFs) for recognizing bird vocalizations. A significant aspect of the approach lies in the ability to break down bird call signals characterized by nonstationary and nonlinear features into a limited set of IMFs. In contrast to conventional approaches that are easily influenced by noise disruption and tend to encounter mode aliasing issues, the suggested framework proficiently segregates the signal by the following steps. First, the vocalization signals of bird vocalizations are preprocessed and endpoint detection is used to separate the vocalization signals of each call. Then, through the decomposition of EEMD, multiple intrinsic mode components are acquired, and the ratio of the variance of each IMF to the variance of the original signal is calculated along with the correlation coefficient between each IMF and the original signal. These ratios, in conjunction with the correlation coefficients are used as the call features. Finally, applying a support vector machine classification and recognition algorithm enables a comparative analysis of various calls. The findings of the experiment reveal that the approach introduced in this paper demonstrates superior accuracy in identification compared to conventional methods, offering valuable insights for the identification of bird species, providing certain reference significance for bird species recognition. |
| format | Article |
| id | doaj-art-44b4f573bb0d43b99444805d6fd95d56 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-44b4f573bb0d43b99444805d6fd95d562025-08-20T03:53:13ZengWileyShock and Vibration1875-92032025-01-01202510.1155/vib/5292138Bird Call Identification Using Ensemble Empirical Mode DecompositionJingxuan Liu0Hailan Chen1School of ScienceSchool of ScienceBirds are iconic species of the environment. Bird monitoring can be achieved by collecting recordings of the calls of wild birds and later identifying the species. A new approach suggested in this study involves the application of ensemble empirical mode decomposition (EEMD) to analyze the time-frequency characteristics of bird calls, along with assessing the variance ratio and correlation coefficient of intrinsic mode functions (IMFs) for recognizing bird vocalizations. A significant aspect of the approach lies in the ability to break down bird call signals characterized by nonstationary and nonlinear features into a limited set of IMFs. In contrast to conventional approaches that are easily influenced by noise disruption and tend to encounter mode aliasing issues, the suggested framework proficiently segregates the signal by the following steps. First, the vocalization signals of bird vocalizations are preprocessed and endpoint detection is used to separate the vocalization signals of each call. Then, through the decomposition of EEMD, multiple intrinsic mode components are acquired, and the ratio of the variance of each IMF to the variance of the original signal is calculated along with the correlation coefficient between each IMF and the original signal. These ratios, in conjunction with the correlation coefficients are used as the call features. Finally, applying a support vector machine classification and recognition algorithm enables a comparative analysis of various calls. The findings of the experiment reveal that the approach introduced in this paper demonstrates superior accuracy in identification compared to conventional methods, offering valuable insights for the identification of bird species, providing certain reference significance for bird species recognition.http://dx.doi.org/10.1155/vib/5292138 |
| spellingShingle | Jingxuan Liu Hailan Chen Bird Call Identification Using Ensemble Empirical Mode Decomposition Shock and Vibration |
| title | Bird Call Identification Using Ensemble Empirical Mode Decomposition |
| title_full | Bird Call Identification Using Ensemble Empirical Mode Decomposition |
| title_fullStr | Bird Call Identification Using Ensemble Empirical Mode Decomposition |
| title_full_unstemmed | Bird Call Identification Using Ensemble Empirical Mode Decomposition |
| title_short | Bird Call Identification Using Ensemble Empirical Mode Decomposition |
| title_sort | bird call identification using ensemble empirical mode decomposition |
| url | http://dx.doi.org/10.1155/vib/5292138 |
| work_keys_str_mv | AT jingxuanliu birdcallidentificationusingensembleempiricalmodedecomposition AT hailanchen birdcallidentificationusingensembleempiricalmodedecomposition |