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Speaker-independent source cell-phone identification for re-compressed and noisy audio recordings

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Abstract

With the rapid increase in user-generated multimedia content, extensive outreach over social media, and their potential in critical applications such as law enforcement, sourcey identification from re-compressed and noisy multimedia are of great importance. This paper proposes a system for speaker-independent cell-phone identification from recorded audio. This system is capable of dealing with test audio with different speech content and a different speaker compared to the training audio. Each recorded audio has the device fingerprint implicitly embedded in it, which encourages us to design a CNN-based system for learning the device-specific signatures directly from the magnitude of discrete Fourier transform of the audio. This paper also addresses the scenario where the recorded audio is re-compressed due to efficient storage and network transmission requirements, which is a common phenomenon in this age of social media. The scenario of the cell-phone classification from the audio recordings in the presence of additive white Gaussian noise is addressed as well. We show that our proposed system performs as well as the state-of-art systems for the speaker-dependent case with clean audio recordings and exhibits much higher robustness in the speaker-independent case with clean, re-compressed, and noisy audio recordings.

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Data Availability

The dataset created by the authors might be made publicly available depending on the permissions received from the funding agency.

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The code created by the authors for this paper might be made publicly available depending on the permissions received from the funding agency.

Notes

  1. https://in.mathworks.com/videos/managing-and-sharing-matlab-code-98671.html

  2. https://in.mathworks.com/videos/top-10-productivity-tools-in-matlab-95250.html

  3. https://in.mathworks.com/videos/spectral-analysis-with-matlab-95557.html

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Acknowledgements

We would like to thank Mr. Da Luo of Shenzhen University for providing us the feature extraction code of [25]. This material is based upon work partially supported by a grant from the Department of Science and Technology (DST), New Delhi, India, under Award Number ECR/2015/000583. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

Funding

This material is based upon work partially supported by a grant from the Department of Science and Technology (DST), New Delhi, India, under Award Number ECR/2015/000583.

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Authors and Affiliations

  1. Multimedia Analysis and Security (MANAS) Lab, Electrical Engineering, Indian Institute of Technology Gandhinagar (IITGN), Gujarat, India

    Vinay Verma & Nitin Khanna

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  1. Vinay Verma
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  2. Nitin Khanna
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Correspondence to Nitin Khanna.

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A preliminary version of this paper appeared in IEEE CVPR’19 Workshop on Media Forensics [38].

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Verma, V., Khanna, N. Speaker-independent source cell-phone identification for re-compressed and noisy audio recordings. Multimed Tools Appl 80, 23581–23603 (2021). https://doi.org/10.1007/s11042-020-10205-z

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