Fake Audio Detection and Audio Analysis System Using Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2026.0802198Keywords:
Fake Audio Detection, Deepfake Audio, Machine Learning, MFCC, Audio Feature Extraction, Gender Classification, Language IdentificationAbstract
ABSTRACT: In recent years, the rapid development of artificial intelligence has made it possible to generate highly realistic synthetic audio, commonly known as deep-fake audio. These fake audio clips can be used for misinformation, fraud, and impersonation, creating serious concerns for digital security and trust. Therefore, there is a growing need for systems that can automatically detect whether an audio sample is real or artificially generated.
In this project, we propose a machine learning-based system for fake audio detection and audio analysis. The system not only identifies whether the audio is real or fake but also performs additional classifications such as gender detection and language identification. The model uses feature extraction techniques like Mel Frequency Cepstral Coefficients (MFCC) to capture important characteristics of the audio signal.
A balanced dataset consisting of real and synthetic audio samples is used for training and testing. Machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Gradient Boosting are applied to classify the audio. The system is designed to work efficiently with smaller datasets while still providing reliable results.
The experimental results show that the proposed system is capable of detecting fake audio with good accuracy. This project demonstrates how machine learning can be used to build practical and efficient solutions for audio verification and security.
References
1. H. Tak, J. Patino and N. Evans, "End-to-End Anti-Spoofing with RawNet2," ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing, 2021, pp. 6369–6373.
2. T. Kinnunen, M. Sahidullah, H. Delgado et al., "The ASVspoof 2019 Challenge: Spoofing Countermeasures for the Detection of Synthesized Speech," Computer Speech & Language, 2020.
3. M. Todisco, H. Delgado and N. Evans, "Constant Q Cepstral Coefficients: A Spoofing Countermeasure for Automatic Speaker Verification," Computer Speech & Language, 2017.
4. B. McFee et al., "Librosa: Audio and Music Signal Analysis in Python," Proceedings of the 14th Python in Science Conference, 2015.
5. F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, 2011, pp. 2825–2830.
6. L. Breiman, "Random Forests," Machine Learning, 45(1), 2001, pp. 5–32.
7. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, 20(3), 1995, pp. 273–297.
8. J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics, 2001, pp. 1189–1232.
9. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
10. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
11. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
12. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
13. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
14. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
15. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
16. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
17. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
18. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
19. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
20. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
21. B. Logan, "Mel Frequency Cepstral Coefficients for Music Modeling," International Symposium on Music Information Retrieval, 2000.
22. X. Wang, J. Yamagishi and M. Todisco, "A Comparative Study of Recent Neural Spoofing Countermeasures," IEEE Journal of Selected Topics in Signal Processing, 2020.
23. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
24. Padmapriya, V. M., Thenmozhi, K., Hemalatha, M., Thanikaiselvan, V., Lakshmi, C., Chidambaram, N., & Rengarajan, A. (2025). Secured IIoT against trust deficit-A flexi cryptic approach. Multimedia Tools and Applications, 84(9), 5625-5652.
25. Pandi Prabha, S., & Rengarajan, A. (2025, February). Decentralized Resource Allocation Model Using Multi-agent Reinforcement Learning for Cloud Environment. In International Conference on Universal Threats in Expert Applications and Solutions (pp. 71-82). Singapore: Springer Nature Singapore.
26. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
27. Gopinathan, V. R. (2025). AI-Powered Kubernetes Orchestration for Complex Cloud-Native Workloads. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13215-13225.
