ItemConference Paper

Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife

Title
Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife
Creator
Vijayalakshmi, P.S.; Kavitha, K.; Poorana Senthilkumar, S.; Karthik, S.; Rajesh Kanna, R.; Muthusamy, A.
Description
The classification of animal sounds has emerged as a vital tool in contemporary research, offering numerous benefits for animal occurrence records, taxonomic research, and behavioral studies. However, the problem of accurately identifying animal species based on their vocalizations remains a significant challenge, particularly in real-world environments where background noise and variability in sound patterns can hinder classification accuracy. In this paper addressed this challenge by proposing a CNN-optimized approach for classifying animal sounds. In order to enhance the number of sound samples, utilized augmentation techniques to extract animal sounds from the Kaggle animal sounds dataset. The animal sounds totally 600 audio samples are used. To improve performance, this model was developed using feature extractions from the MFCC, ZCR, and Mel-Spectrogram. The seamless deployment of forest department workers is ensured by the interpretability of our model for real-world applications related to wildlife conservation and monitoring. The main goal is to successfully identify animals using auditory properties, such as tiger, leopard, elephant, and otter noises, based on their vocalizations. Additionally, The optimized CNN and LSTM for sound classification. The Optimized CNN outperformed all other models, achieving an outstanding 98.32 % training accuracy rate. 2025 IEEE.
Source
2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.345-351
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Classification; Convolutional neural networks; Feature extraction; LSTM; Mel frequency cepstral coefficient; Training
Coverage
Vijayalakshmi P.S., Dr. N. G. P. Arts and Science College, Department of Computer Applications, Tamil Nadu, Coimbatore, India; Kavitha K., Dr. N. G. P. Arts and Science College, Department of Computer Applications, Tamil Nadu, Coimbatore, India; Poorana Senthilkumar S., Dr. N. G. P. Arts and Science College, Department of Computer Applications, Tamil Nadu, Coimbatore, India; Karthik S., Kristu Jayanti Deemed to Be University, Department of Computer Science (PG), Bangalore, India; Rajesh Kanna R., CHRIST University, Department of Computer Science, Bangalore, India; Muthusamy A., Kongu Engineering College, Department of Computer Technology, Tamil Nadu, Erode, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158733-8;
Format
online
Language
English
Type
Conference paper

Citation

Vijayalakshmi, P.S.; Kavitha, K.; Poorana Senthilkumar, S.; Karthik, S.; Rajesh Kanna, R.; Muthusamy, A., “Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25789.

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