Identification of AMR decompressed audio |
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| Affiliation: | 1. College of Information Engineering, Shenzhen University, Shenzhen 518060, China;2. Shenzhen Key Laboratory of Media Security, Shenzhen 518060, China;3. School of Information Management, Sun Yat-sen University, Guangzhou 510006, China;1. Blida University—LATSI Laboratory, 09000, Blida, Algeria;2. University of Rennes—IRISA/ENSSAT, 22305 Lannion, France;1. Research Institute for Signals, Systems and Computational Intelligence, sinc(i), Facultad de Ingeniería, Universidad Nacional del Litoral–CONICET CC217, Ciudad Universitaria, Paraje El Pozo, S3000, Santa Fe, Argentina;2. Dpto. de Ingeniería Eléctrica, UAM-Iztapalapa, Mexico;3. Laboratorio de Cibernética, Facultad de Ingeniería-Universidad Nacional de Entre Ríos, Argentina;4. CONICET, Argentina;1. Trivedi Consults, LLC, 1020 Curtis Street, Albany, CA 94706, USA;2. VHA Performance Services, 521 East Morehead Street, Suite 300, Charlotte, NC 28202, USA;1. Meteksan Savunma, Ankara, Turkey;2. Bilkent University, Electrical and Electronics Engineering Department, Ankara, Turkey;3. Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology, Ankara, Turkey |
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| Abstract: | More and more conversation recordings from phone calls are used as forensic evidence. To decide whether an unknown speech recording comes from mobile phone or not becomes an important issue in digital audio forensics. The communicating conversation recorded by mobile phones is encoded by Adaptive Multi-Rate (AMR) audio codec, which was adopted as the standard speech codec by 3GPP and widely used in GSM and UMTS. Therefore, AMR decompressed audio detection can be used to identify the source of the digital audio recording. Furthermore, it is helpful to locate the forgery position of the splicing AMR decompressed audio for forensic purposes. In this article, we focus on the identification of AMR decompressed audio, namely, given the waveform of an audio, we wish to identify whether it has been previously compressed by AMR codec or not. The artifacts introduced by the AMR codec will help to detect the source of the recordings. Based on our analysis, we find that the sample repetition rate of the AMR decompressed waveform is significantly greater than the regular waveform. Therefore, we employ the sample repetition rate as a feature to identify the AMR decompressed audio. The experimental results show that this feature is robust and effective. |
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| Keywords: | Decompression audio identification AMR Sample repetition rate Audio forensics |
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