Audio forensic examination

Maher, Robert C.

doi:10.1109/msp.2008.931080

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2011

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Cited by 101 publications

(33 citation statements)

References 12 publications

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“…In the field of law enforcement audio collection, severely degraded audio is commonplace, reducing the intelligence value of the audio by making it inadmissible as evidence in a court of law [1]. In less extreme conditions, a loss in quality is encountered which has adverse effects on transcription speed resulting in higher transcription costs [2].…”

Section: Introductionmentioning

confidence: 99%

A data-driven non-intrusive measure of speech quality and intelligibility

Sharma

Wang

Naylor

et al. 2016

Speech Communication

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Section: Introductionmentioning

confidence: 99%

A data-driven non-intrusive measure of speech quality and intelligibility

Sharma

Wang

Naylor

et al. 2016

Speech Communication

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“…To prevent illegal distribution of digital audio through the BitTorrent, identification methods of audio fragments of MP3 and FLAC formats were proposed in [7,8]. In [9,10], audio identification method is also used in forensic technology under a hypothesis that the audio is correctly decoded.…”

Section: Introductionmentioning

confidence: 99%

Audio Fragment Identification System

Jin

Kim

2014

IJMUE

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“…An example of such application is the forensic acquisition, analysis, and evaluation of admissible audio recordings as evidence in court cases [2]. Current audio authenticity approaches are categorized according to the artifacts extracted from the signal itself.…”

Section: Introductionmentioning

confidence: 99%

Blind identification of source mobile devices using VoIP calls

Jahanirad

Wahab

Anuar

et al. 2014

2014 Ieee Region 10 Symposium

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Sources such as speakers and environments from different communication devices produce signal variations that result in interference generated by different communication devices. Despite these convolutions, signal variations produced by different mobile devices leave intrinsic fingerprints on recorded calls, thus allowing the tracking of the models and brands of engaged mobile devices. This study aims to investigate the use of recorded Voice over Internet Protocol calls in the blind identification of source mobile devices. The proposed scheme employs a combination of entropy and mel-frequency cepstrum coefficients to extract the intrinsic features of mobile devices and analyzes these features with a multi-class support vector machine classifier. The experimental results lead to an accurate identification of 10 source mobile devices with an average accuracy of 99.72%.

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Audio forensic examination

Cited by 101 publications

References 12 publications

A data-driven non-intrusive measure of speech quality and intelligibility

A data-driven non-intrusive measure of speech quality and intelligibility

Audio Fragment Identification System

Blind identification of source mobile devices using VoIP calls

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