Current Developments and Future Trends in Audio Authentication

Gupta, Sushma; Cho, Seongho; Kuo, C.-C. Jay

doi:10.1109/mmul.2011.74

Cited by 65 publications

(20 citation statements)

References 15 publications

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“…This particular domain of forgery detection is in immediate need of attention. There are several noteworthy developments in the field of audio authentication (details of which can be obtained from the survey [144]), which are currently concentrating on detection of audio manipulations in audio recordings, recordings of phone conversations, and commercially distributed audio files (songs and music albums). If combined with frame-based analyses that are generally performed in the video forensics domain, such techniques (or their more flexible and potent modified versions) could certainly help further the cause of video content authentication.…”

Section: Complete Oversight Of the Audio Component Of Digital Videosmentioning

confidence: 99%

Video content authentication techniques: a comprehensive survey

Singh

Aggarwal

2017

Multimedia Systems

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Section: Complete Oversight Of the Audio Component Of Digital Videosmentioning

confidence: 99%

Video content authentication techniques: a comprehensive survey

Singh

Aggarwal

2017

Multimedia Systems

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“…Cemal had studied and analyzed the characteristic parameters and recognition model of recording equipment, its characteristic parameters and recognition model are based on the existing speaker recognition features and models, either Fourier Transformation parameters or MFCC is not for a special recording device identification parameters [10]. Characteristic parameters that specifically for recording equipment are still very few.…”

Section: Introductionmentioning

confidence: 99%

Recording Device Identification Based on Cepstral Mixed Features

Zhong¹,

Kong²,

You³

et al. 2015

Proceedings of the 5th International Conference on Computer Engineering and Networks — PoS(CENet2015)

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The authenticity of the recording evidence is the foundation of legitimacy and relevance, which is the primary condition of recording evidence. With the springing up of private recording evidence, there is an urgent need for authenticity identification of recordings. That the evidence shall be from an accurate and legitimate source is a prerequisite for three elements. Recording equipment identification is the core content of sources of evidence. This article studies the characteristics of the recording device parameters, proposing three characteristic parameters of recording equipment such as the proportion of time-domain low roughness, etc. And combined with improved Mel Frequency Cepstrum Coefficient (MFCC) feature parameters characteristic parameters constitute a hybrid 92-dimensional. According to experimental analysis, with 10 different brands and models of recording device (including five different brands and models commonly used in voice recorder and five kinds of commonly used different brands and models of mobile phones), 60 young men and women, each of 10 different voice, the same type of equipment to record each 2, shows that mixed characteristic parameters can effectively characterize the characteristics of the recording equipment. Recognition rate increases by more than 6% compared with ordinary cepstrum.

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“…An overview of the existing audio forensics methods can be found in [4] and references. Existing audio forensics methods based on signal characteristics can be broadly divided into the following categories:…”

Section: Introductionmentioning

confidence: 99%

Acoustic environment identification using unsupervised learning

Malik

Mahmood

2014

Secur Inform

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Acoustic environment leaves its characteristic signature in the audio recording captured in it. The acoustic environment signature can be modeled using acoustic reverberations and background noise. Acoustic reverberation depends on the geometry and composition of the recording location. The proposed scheme uses similarity in the estimated acoustic signature for acoustic environment identification (AEI). We describe a parametric model to realize acoustic reverberation, and a statistical framework based on maximum likelihood estimation is used to estimate the model parameters. The density-based clustering is used for automatic AEI using estimated acoustic parameters. Performance of the proposed framework is evaluated for two data sets consisting of hand-clapping and speech recordings made in a diverse set of acoustic environments using three microphones. Impact of the microphone type variation, frequency, and clustering accuracy and efficiency on the performance of the proposed method is investigated. Performance of the proposed method is also compared with the existing state-of-the-art (SoA) for AEI.

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Current Developments and Future Trends in Audio Authentication

Cited by 65 publications

References 15 publications

Video content authentication techniques: a comprehensive survey

Video content authentication techniques: a comprehensive survey

Recording Device Identification Based on Cepstral Mixed Features

Acoustic environment identification using unsupervised learning

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