Acoustic monitoring techniques for bird species identification have evolved significantly with advancements in artificial intelligence and machine learning. Here are some key methods and tools used for this purpose:
## 1. **Deep Learning Techniques**
- **BirdNET**: This is a deep neural network capable of identifying 984 North American and European bird species by sound[3].
- **LSTM with Coordinate Attention**: A novel method using Long Short-Term Memory (LSTM) networks combined with coordinate attention to identify a large number of bird species based on their calls, achieving a mean average precision (mAP) of 77.43%[1].
## 2. **Bioacoustic Monitoring**
- Bioacoustic techniques involve recording and analyzing bird vocalizations to monitor populations. This method has been enhanced by new technologies that allow for more precise detection and classification of species[5].
- Automated click here or passive acoustic monitoring equipment can capture long-term data from fixed locations, providing valuable ecological insights when analyzed using machine learning techniques[7].
## 3. **Band-Limited Phase-Only Correlation (BLPOC) Function**
- This technique is used for acoustic individual identification in birds, offering a click here precise way to distinguish between individuals based on their unique vocal characteristics[2].
## 4. **Animal Sound Identifier (ASI) Software**
- Developed as part of the MATLAB software suite, ASI performs probabilistic classification of species occurrences from field recordings without needing pre-defined reference libraries[3].
These methods collectively enhance the efficiency and accuracy of bird species identification through acoustic means.
### Correct Names:
Some specific tools mentioned include:
- **BirdNET**: A deep neural network.
- **Animal Sound Identifier (ASI)**: A MATLAB software.
Some relevant studies focus on various bird species like *Cercomacra tyrannina*, *Hylorchilus sumichrasti*, *Pitangus sulphuratus*, *Psarocolius montezuma*, and *Amazona viridigenalis* in terms of individual identification using BLPOC functions[2].
Citations:
[1] https://pubmed.ncbi.nlm.nih.gov/36139299/
[2] https://www.mdpi.com/2076-3417/10/7/2382
[3] https://3rswildlife.info/detection-birds/
[4] https://www.researchgate.net/publication/261229851_Acoustic_monitoring_techniques_for_avian_detection_and_classification
[5] https://www.birdsnz.org.nz/wp-content/uploads/2021/12/Steer_2010.pdf
[6] https://www.researchgate.net/publication/275969986_Visual_and_acoustic_identification_of_bird_species
[7] https://birdsurveyguidelines.org/acoustic-survey-methods/
[8] https://cdnsciencepub.com/doi/10.1139/cjz-2023-0044