Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vijayalakshmi, P.S.; Kavitha, K.; Poorana Senthilkumar, S.; Karthik, S.; Rajesh Kanna, R.; Muthusamy, A. Title A name given to the resource Audio Recognition of Animals Using Optimized Deep Learning Techniques for the Conservation of Wildlife Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 17th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2025;pp.345-351 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/CICN67655.2025.11368282" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/CICN67655.2025.11368282</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034686805?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034686805?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Classification; Convolutional neural networks; Feature extraction; LSTM; Mel frequency cepstral coefficient; Training Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158733-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online