Bird Species Prediction Based on Voice Using CRNN

Abstract

Bird species recognition through sound is a crucial tool for biodiversity monitoring, enabling non-invasive, scalable insights into avian populations and their habitats. This project aims to develop a machine learning-based bird sound recognition system that identifies bird species from audio. The system processes audio inputs to extract relevant acoustic features, such as spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs), leveraging tools like LibROSA. A Convolutional Recurrent Neural Network (CRNN) hybrid model is trained on these features, utilizing its ability to capture spatial patterns within audio data, for accurate bird species prediction. The proposed system also integrates a web interface, allowing users to upload recordings and view prediction results in real-time. This interface facilitates broader accessibility, making it useful for both research and citizen science initiatives.

Introduction

1. Project Overview:
The project focuses on building an AI-powered system for automatic bird species identification using their vocalizations. It addresses the limitations of traditional birdwatching (visual/manual sound recognition) by offering a scalable, non-invasive, and efficient solution for ecological research, conservation, and citizen science.

2. System Architecture:

• The system uses Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms to extract audio features.

• A Convolutional Recurrent Neural Network (CRNN) model is employed, combining:

  • CNNs for extracting spatial (frequency-based) patterns

  • RNNs for capturing temporal (time-dependent) sequences

• The model is integrated into a web interface that allows users to upload bird sounds and receive predictions with confidence scores.

3. Key Features:

• Audio Preprocessing: Noise reduction and filtering to isolate bird calls.

• Feature Extraction: MFCCs and spectrograms capture both frequency and temporal characteristics.

• Model Training: CRNN model trained to classify bird species with high accuracy.

• Postprocessing & Prediction: Confidence thresholds and optional ensemble methods enhance accuracy. Results are accessible through a user-friendly UI.

4. Real-World Relevance:
The system aids biodiversity monitoring and offers a practical bridge between acoustic research and ecological needs. It supports large-scale deployment, data management (via databases and audio storage), and is built for robustness and scalability.

Literature Review Highlights:

A survey of 20 related works reveals several approaches and challenges in bird sound recognition:

• Techniques explored include CNNs, SVMs, AlexNet, TDNN, HMMs, and self-supervised learning.

• Hybrid models (e.g., CNN + RNN or CNN + SVM) tend to improve accuracy.

• Few-shot learning, audio denoising, and mobile deployment are emerging areas of interest.

• Accuracy across models ranges from 70% to over 97%, depending on dataset size, quality, and complexity.

• Limitations include poor performance with overlapping calls, high computational cost, and reliance on clean audio.

Conceptual Framework:

The proposed system is structured in several stages:

1. Audio Preprocessing: Cleans raw audio by reducing noise.

2. Feature Extraction: Uses MFCCs and spectrograms to convert audio into analyzable formats.

3. Model Training: CRNN model processes spatial and temporal aspects for accurate species classification.

4. Classification & Postprocessing: Outputs predictions with confidence scores and applies filtering to improve reliability.

5. Output & User Interface: Delivers results via a web platform, accessible for non-experts.

6. Additional Considerations: Enables periodic retraining and potential multi-species recognition.

Research Gaps Identified:

1. Limited Species Diversity: Many datasets contain only 4–5 bird species, hindering generalizability.

2. Real-Time Constraints: Deep models like CNNs are resource-intensive and not suited for low-power devices.

3. Noise and Overlap Sensitivity: Environmental sounds and overlapping calls reduce accuracy.

4. Underutilized Hybrid Models: CRNN and similar architectures are not widely adopted despite their advantages.

5. High-Quality Audio Dependency: Most models perform poorly with noisy or low-quality recordings.

6. Few-Shot Learning Deficiency: Rare species lack labeled data; few-shot/self-supervised methods need development.

7. Field Deployment Gaps: Most studies are lab-based; real-world validation and practical deployment are limited.

Conclusion

Bird sound recognition technology holds immense potential for advancing ecological research and conservation efforts by providing efficient, scalable tools for species identification and monitoring. This review highlights various methodologies, such as convolutional neural networks (CNNs), support vector machines (SVMs), and feature extraction techniques like Mel Frequency Cepstral Coefficients (MFCCs) and spectrogram analysis, which have shown promising results in bird species classification. However, despite the progress, several research gaps remain, including limitations in species diversity, sensitivity to background noise, and dependency on high-quality audio data. Current models often struggle with real-time classification, generalization to noisy, natural environments, and deployment on low-power devices, posing challenges for practical field applications. Moreover, there is a need for more extensive datasets and hybrid model architectures (such as CRNNs) that can capture both spatial and temporal features in bird calls. Addressing these limitations could lead to significant advancements in model robustness, making automated bird sound recognition more accurate and accessible in real-world conditions. In conclusion, future research should focus on creating noise-resilient, lightweight, and generalizable models, as well as exploring innovative approaches like few-shot learning for rare species. Bridging these gaps will enhance the practicality and effectiveness of bird sound recognition systems, empowering ecologists, researchers, and citizen scientists to contribute to biodiversity monitoring and conservation on a broader scale.

References

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Copyright

Copyright © 2025 Ashish Kumar Pandey, Sanyam Kudale, Rohit Jadhav, Swaraj Giri, S. K. Wagh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.