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Content Provider | IEEE Xplore Digital Library |
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Author | Ikram, S. Malik, H. |
Copyright Year | 2010 |
Description | Author affiliation: Information Systems, Security, and Forensics Lab., Electrical & Computer Engineering Department, University of Michigan - Dearborn, 4901 Evergreen Road, Dearborn, MI 48128 (Ikram, S.; Malik, H.) |
Abstract | This paper presents a new audio forensics method based on background noise in the audio signals. The traditional speech enhancement algorithms improve the quality of speech signals, however, existing methods leave traces of speech in the removed noise. Estimated noise using these existing methods contains traces of speech signal, also known as leakage signal. Although this speech leakage signal has low SNR, yet it can be perceived easily by listening to the estimated noise signal, it therefore cannot be used for audio forensics applications. For reliable audio authentication, a better noise estimation method is desirable. To achieve this goal, a two-step framework is proposed to estimate the background noise with minimal speech leakage signal. A correlation based similarity measure is then applied to determine the integrity of speech signal. The proposed method has been evaluated for different speech signals recorded in various environments. The results show that it performs better than the existing speech enhancement algorithms with significant improvement in terms of SNR value. |
Starting Page | 106 |
Ending Page | 110 |
File Size | 1636397 |
Page Count | 5 |
File Format | |
ISBN | 9781424474912 |
ISSN | 1945788X |
e-ISBN | 9781424474936 |
DOI | 10.1109/ICME.2010.5582981 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-07-19 |
Publisher Place | Singapore |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Speech Noise Noise measurement Speech enhancement Harmonic analysis Forensics Estimation harmonic analysis Spectral subtraction audio forensics noise estimation geometric approach |
Content Type | Text |
Resource Type | Article |
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