Acoustic Bird Detection System Using Deep Learning

Abstract

Researchers conduct a study that unites audio and image recognition methods to identify bird species through a workflow which combines spectrograms with Convolutional Neural Networks (CNNs). The preservation of species diversity requires effective monitoring of bird populations because biodiversity keeps declining. The methodology employs bird vocal recordings that get transformed into spectrograms through spectral analysis to extract temporal along with frequency signal characteristics. The analysis includes images captured in bird habitats that enhance the audio record evaluation process. The system starts with gathering different data sets which contain audio recordings together with images of multiple bird species. The processing of audio data creates spectrograms through Short-Time Fourier Transform (STFT) and the image data gets preprocessed for uniform presentation. The designed CNN model uses dual-input architecture to process spectrograms alongside images simultaneously. The training combines transfer learning with pre-trained networks for two purposes: improved execution and decreased computational demands

Introduction

1. Introduction

Bird population decline is a major concern for ecologists and conservationists due to their crucial roles in pollination, seed dispersal, and environmental monitoring. Traditional bird identification methods, like expert field surveys, are time-consuming and labor-intensive. Recent advances in image and audio processing, combined with deep learning, now offer faster and more accurate identification methods.

2. Importance of Audio-Visual Data

• Audio Data: Bird vocalizations provide key information about species presence and behavior. These are transformed into spectrograms, which can be analyzed by machine learning models.

• Visual Data: Images and videos help identify birds by morphology, color patterns, and motion.

• Combining both audio and image data improves identification accuracy, as each modality contributes unique information.

3. Deep Learning for Bird Identification

• CNNs (Convolutional Neural Networks) are widely used to analyze both spectrograms and images.

• A dual-input system using CNNs for both modalities is proposed to enhance performance and support conservation efforts.

4. Literature Review Highlights

• Dhakne et al. (2022): Audio-only approach using CNN (AlexNet) reached 97% accuracy, demonstrating noise-resilience.

• Anekar et al. (2023): Combined spectrograms and image data using GoogLeNet, achieving 88.33% accuracy.

• Zhang et al. (2023): Used multiple acoustic features (MFCC, Chroma, etc.) with CNN + Transformer, achieving ~98% accuracy.

• Ansline Lidiya et al. (2024): CNNs with spectrograms handled noise and variability, reaching 92.4% F1-score.

• Swaminathan et al. (2024): Applied Wav2Vec transformer for multi-label bird classification, scoring 0.89 F1 on noisy datasets.

Trend: Evolving from basic CNN models to multi-modal, attention-based transformers, with improved accuracy, noise handling, and scalability.

5. Proposed System Overview

• Goal: Build an automated bird species detection platform using audio and image fusion via deep learning.

• Dataset: Includes audio and image data for six bird species (e.g., Common Myna, Indian Peacock) sourced from Kaggle and organized for real-world variability (lighting, weather, noise).

Dataset Diversity

• Audio: Recorded in varied environments and times.

• Images: Captured from multiple angles and lighting conditions, including occluded and action shots.

• Matched and labeled for multi-modal learning (audio + image).

6. Data Preprocessing

Audio:

• Resampled to 22,050 Hz

• Pre-emphasis, silence removal

• MFCCs extracted (40-dim features)

• Final format: (40,1) for CNN input

Image:

• Resized to 64×64 RGB

• Normalized (pixel values 0–1)

• Data augmentation (shearing, zooming, flipping)

• One-hot encoded class labels

7. Model Architecture

Image Model (CNN):

• 3 Conv2D layers (32 → 64 filters)

• MaxPooling + Dropout (0.5)

• Dense layer (256 units) → Softmax output

• Optimizer: SGD, Loss: Categorical Crossentropy, 100 epochs

Audio Model (1D CNN):

• Two Conv1D layers (64 → 128 filters)

• MaxPooling + Dropout (0.3 → 0.5)

• Dense (128 units) → Softmax

• Optimizer: Adam, 200 epochs

Fusion:

• Combines predictions using weighted average:

  FinalPrediction = argmax(α⋅AudioModel + β⋅ImageModel)

• Fusion enhances robustness and overall accuracy.

8. Mathematical Formulations

• MFCC Calculation:
  MFCC[n] = Σ? log(S[k]) × cos[n(k - 0.5)π/N]

• CNN Function:
  Y = f(WX + b)
  where Y = output, X = input, W = weights, b = bias, f = activation function

• Fusion Formula:
  Weighted combination of model outputs

9. Future Prospects

• Improved accuracy through:

  • Tuning hyperparameters

  • Adding more training data

  • Using cross-validation

• Real-world deployment via:

  • Mobile apps

  • Citizen science tools

  • Wildlife monitoring systems

Conclusion

A thorough system for automated bird species recognition employed sound files along with picture data resulted in successful development through a deep learning evaluation process. Multiple variations of convolutional neural networks were tested before creating a dual-path system that processes Mel spectrograms along with RGB images as inputs. A CNN-based pipeline for image classification operated on a multi-species bird image dataset allowing convolutional pooling dropout layers which produced high accuracy together with real-time model optimization and augmentation techniques. The audio classification model extracted features from MFCCs before processing them through a 1D CNN structure which trained segmented vocalizations in order to detect unique acoustic patterns in species characteristics. After optimization the models received separate treatment before the implementation of a decision-level weighted average ensemble method to strengthen predictions under various real-world scenarios. The system displayed robust performance since it achieved excellent results on different evaluation assessments which included accuracy and precision and recall and F1-score therefore strengthening its ability to recognize birds through multiple modalities. Inside the graphical user interface desktop system users accessed real-time classification services for image or audio file uploading. The system demonstrated extensive testing across clean and noisy inputs which verified its operational readiness under various outdoor conditions. By uniting visual and acoustic data together the system earned superior identification capabilities which overcome single-modal weaknesses to become more stable. Researchers and biodiversity monitors and ecologists find this framework to hold great potential for their respective fields of study. Further research endeavors should analyze the implementation of transformer-based models with temporal attention methods for processing extensive audio durations as well as bigger species-diverse datasets. The system can reach more field applications through mobile or embedded deployments using Raspberry Pi devices and edge platforms to generate significant contributions toward wildlife conservation and species tracking automation. The system creates foundational elements for future studies that unite three disciplines: ecology with bioacoustics along with artificial intelligence. The growing accessibility of bird vocalization along with image dataset libraries on Xeno-Canto and eBird enables continuous improvement of the model so it can identify hundreds of bird species within diverse habitats. The system can obtain additional benefits through the implementation of geographic information systems together with cloud-based databases which would enable the development of real-time biodiversity mapping and migration tracking features. The automated control system makes bird monitoring more efficient and it provides accessible and modern ecological research platforms for educators alongside conservation professionals and general citizens. Such smart systems show transformative potential to drive data-based conservation practices while supporting global ecological management because biodiversity monitoring has become an urgent priority during habitat destruction and climate change.

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Copyright

Copyright © 2025 Prof. Aarti Bhise, Danish Nagaonkar, Vrushabh Ghodke, Varun Toshniwal, Tushar Shinde. 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.