ArticleScientific reports2021
Comparing recurrent convolutional neural networks for large scale bird species classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-Powered Vocalization Analysis in Poultry: Systematic Review of Health, Behavior, and Welfare Monitoring.Sensors (Basel, Switzerland) · 2025Pooled it
- Automated detection of stereotyped animal sounds using data augmentation and transfer learning.Scientific reports · 2026Article
- Noise-Resilient Bioacoustics Feature Extraction Methods and Their Implications on Audio Classification Performance: Systematic Review.JMIR biomedical engineering · 2025Review
- Automating timeScientific reports · 2025Article
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Multi-modal Language models in bioacoustics with zero-shot transfer: a case study.Scientific reports · 2025Article
- A novel approach to Indian bird species identification: employing visual-acoustic fusion techniques for improved classification accuracy.Frontiers in artificial intelligence · 2025Article
- Pre-trained Convolutional Neural Networks Identify Parkinson's Disease from Spectrogram Images of Voice Samples.Research square · 2024Article
- Reshaping Bioacoustics Event Detection: Leveraging Few-Shot Learning (FSL) with Transductive Inference and Data Augmentation.Bioengineering (Basel, Switzerland) · 2024Article
- MosquitoSong+: A noise-robust deep learning model for mosquito classification from wingbeat sounds.PloS one · 2024Article
- Deep transfer learning-based bird species classification using mel spectrogram images.PloS one · 2024Article
- A machine learning method to process voice samples for identification of Parkinson's disease.Scientific reports · 2023Article
- A Novel Bird Sound Recognition Method Based on Multifeature Fusion and a Transformer Encoder.Sensors (Basel, Switzerland) · 2023Article
- An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features.Animals : an open access journal from MDPI · 2022Article
- Birdsong classification based on ensemble multi-scale convolutional neural network.Scientific reports · 2022Article
- Convolutional Neural Networks for the Identification of African Lions from Individual Vocalizations.Journal of imaging · 2022Article
- Review
- The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics.PloS one · 2022Article
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5 authors.
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Abstract
We present a deep learning approach towards the large-scale prediction and analysis of bird acoustics from 100 different bird species. We use spectrograms constructed on bird audio recordings from the Cornell Bird Challenge (CBC)2020 dataset, which includes recordings of multiple and potentially overlapping bird vocalizations with background noise. Our experiments show that a hybrid modeling approach that involves a Convolutional Neural Network (CNN) for learning the representation for a slice of the spectrogram, and a Recurrent Neural Network (RNN) for the temporal component to combine across time-points leads to the most accurate model on this dataset. We show results on a spectrum of models ranging from stand-alone CNNs to hybrid models of various types obtained by combining CNNs with other CNNs or RNNs of the following types: Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and Legendre Memory Units (LMU). The best performing model achieves an average accuracy of 67% over the 100 different bird species, with the highest accuracy of 90% for the bird species, Red crossbill. We further analyze the learned representations visually and find them to be intuitive, where we find that related bird species are clustered close together. We present a novel way to empirically interpret the representations learned by the LMU-based hybrid model which shows how memory channel patterns change over time with the changes seen in the spectrograms.
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