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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
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Authors
Ketki Dandgavale,Anjali Mundhe,Siddhi Choughule,Kanchan Warke
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Keywords
Fake audio detection, Acoustic features, Manipulated audio, CNN framework, MFCCs, CNN classification, User-friendly interface, data annotation, Accessibility
Abstract
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure. However, artificial adjustment of the parameters can have a relatively obvious influence on the results. It is almost impossible to manually set the best set of parameters. The pervasive presence of manipulated, fabricated, and counterfeit audio recordings has raised serious concerns regarding misinformation, identity theft, and privacy violations. In response to the growing threat of manipulated and counterfeit audio recordings, our project, "AI-Generated and Manipulated Audio Detection," aims to develop a fully automated end-to-end solution. This system addresses the limitations of existing fake audio detection methods, which often rely on manual parameter adjustments, by proposing an efficient and automated approach. Our approach leverages a convolution neural network (CNN) framework, utilizing speech waveforms and acoustic features such as MFCCs to extract high-level representations while considering prosody differences between genuine and fake speech. The project begins with comprehensive data collection, assembling a diverse dataset of audio samples, and annotating them to distinguish between authentic and manipulated content. To enhance the system's efficiency and adaptability, we introduce feature selection and extraction techniques, with a focus on MFCCs for robust feature representation. The machine learning model employed is an SVM (Support Vector Machine), offering effective classification capabilities. In addition to the technical aspects, our project prioritizes user-friendliness and accessibility. We provide an interactive user interface that allows users to input audio in various formats. The system seamlessly converts these inputs into the required WAV format for further processing, simplifying the user experience. This comprehensive approach, combining automated feature selection, MFCC based feature extraction, SVM modeling, and an intuitive user interface, empowers our system to accurately detect AI-generated and manipulated audio. This not only contributes to safeguarding against misinformation and privacy violations but also ensures that the detection process is accessible and user-friendly for a wider audience
IJCRT's Publication Details
Unique Identification Number - IJCRT2405582
Paper ID - 256499
Author type - Indian Author
Page Number(s) - f448-f452
Pubished in - Volume 12 | Issue 5 | May 2024
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No Of Downloads - 37
Author Country - India, 411043, Pune, Pune, 411043, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Ketki Dandgavale,Anjali Mundhe,Siddhi Choughule,Kanchan Warke, "AI GENERATED AND HUMAN AUDIO DETECTION", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 5, pp.f448-f452, May 2024, Available at :http://www.ijcrt.org/papers/IJCRT2405582.pdf
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The International Journal of Creative Research Thoughts (IJCRT) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies.
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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