Paper Title
Audioshield:An AI Enabled Fake Audio Detection
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Abstract
Fake audio is a growing issue across various fields. It includes news media, politics, entertainment, etc. This kind of fake audio can spread false information, manipulate people’s thinking, and even harm someone’s reputation. Reliable detection of fake audio is therefore essential. This can be done by first extracting MFCC features from the audio signal. MFCCs are used to capture the spectral characteristics of audio data. The features are fed into a hybrid model, which includes a convolutional neural network (CNN) with a recurrent neural network (RNN). The CNN extracts feature from the spatial domain, by identifying spatial patterns within the audio. Meanwhile, the RNN extracts features from the temporal domain, by capturing changes and patterns over time, which is crucial for understanding the temporal aspects of audio data. This method yields accurate results and can be useful in real-world applications including content control, media forensics, and cybersecurity. To make this system more user-friendly, it is made into an application. So that the user would simply need to upload the audio file to the application, and the results would be displayed as either” fake” or” real”, along with a percentage indicating how confident the system is in its decision. This helps to identify if the audio file is manipulated. Such user-friendly tools are essential for safeguarding the integrity of information and protecting individuals from the harmful effects of fake audio.
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How To Cite (APA)
A K Gokul, Jithin S, Ajal Prem, Akarsh B, & Albin Thomas (June-2024). Audioshield:An AI Enabled Fake Audio Detection. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), c835-c846. https://ijnrd.org/papers/IJNRD2406277.pdf
Issue
Volume 9 Issue 6, June-2024
Pages : c835-c846
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Paper Reg. ID: IJNRD_219489
Published Paper Id: IJNRD2406277
Downloads: 000122261
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: Kannur, Kerala, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2406277.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406277
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