Blind source mobile device identification based on recorded call

Jahanirad, Mehdi; Wahab, Ainuddin Wahid Abdul; Anuar, Nor Badrul; Idris, Mohd Yamani Idna; Ayub, Mohamad Nizam Bin

doi:10.1016/j.engappai.2014.08.008

Cited by 12 publications

(6 citation statements)

References 23 publications

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“…Analyses of the audio revealed no dominant harmonicity, fundamental frequencies nor reliable temporal envelope descriptions. Zero crossing and spectral shape statistics required a minimum signal level above the noise-floor to be of any use as an input to a machine learning classifier [8]. In other words, peak levels at -50 dBFS and average levels around -72 dbFS in our 16-bit system corresponded to a very low dynamic range and a minimum signal-to-noise ratio, which limited the signal analysis options.…”

Section: Feature Extractionmentioning

confidence: 99%

“…For our specific task with "dead air" audio, we know of only one related effort, which focused on mobile device identification using Mel-frequency cepstral coefficients (MFCC) features. To account for the absence of an audible speech signal, Jahanirad et al computed the entropy of the Mel-cepstrum, discovering that sections of "silent" signal resulted in high-valued entropy-MFCC features which were effective in discriminating between different mobile device models [8]. This work suggests there are characteristic features of a source device or the transmission channel even in the absence of a speech signal.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Audio Spectrogram Factorization for Classification of Telephony Signals below the Auditory Threshold

Orife,

Walker,

Flaks

2018

Preprint

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Traffic Pumping attacks are a form of high-volume SPAM that target telephone networks, defraud customers and squander telephony resources. One type of call in these attacks is characterized by very low-amplitude signal levels, notably below the auditory threshold. We propose a technique to classify so-called "dead air" or "silent" SPAM calls based on features derived from factorizing the caller audio spectrogram. We describe the algorithms for feature extraction and classification as well as our data collection methods and production performance on millions of calls per week.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audio Spectrogram Factorization for Classification of Telephony Signals below the Auditory Threshold

Orife,

Walker,

Flaks

2018

Preprint

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“…For example, establishing the source of audio or multimedia evidence in the court of law increases the authenticity of the evidence [2,3]. This is because digital audio technology, at present, has facilitated the processing, manipulation and editing of audio by using sophisticated tools and software without leaving any perceptible trace [4]. Furthermore, knowing recording device characteristics has the potential to help other critical speech applications such as speech recognition and speaker verification by normalizing recording devices' variability.…”

Section: Introductionmentioning

confidence: 99%

“…Aggarwal et al [14] proposed an MFCC feature extraction from an estimated noisy region of the speech. Jahanirad et al [4,15] investigated the use of entropy of Mel-cepstrum coefficients from near-silent segments. Anshan et al [16] used device self-noise estimated from the silent segments.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention Pooling Network for Recording Device Classification Using Neutral and Whispered Speech

Naini

Singhal

Ghosh

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we proposed a method for recording device classification using the recorded speech signal. With the rapid increase in different mobile and professional recording devices, determining the source device has many applications in forensics and in further improving various speech-based applications. This paper proposes dual and single attention pooling-based convolutional neural networks (CNN) for recording device classification using neutral and whispered speech. Experiments using five recording devices with simultaneous direct recordings from 88 speakers speaking both in neutral and whisper and recordings from 21 mobile devices with simultaneous playback recordings reveal that the proposed dual attention pooling based CNN method performs better than the best baseline scheme. We show that we achieve a better performance in recording device classification with whispered speech recordings than corresponding neutral speech. We also demonstrate the importance of voiced/unvoiced speech and different frequency bands in classifying the recording devices.

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“…Most existing literature related to this problem focus on microphone identification [4][5][6][7][8][9], telephone handset identification [3,[10][11][12][13][14][15] and cell phone identification [15][16][17][18][19][20]. In particular, source cell phone recognition from speech recordings was first pointed out by Hanilçi et al [17].…”

Section: Introductionmentioning

confidence: 99%

Cell phone verification from speech recordings using sparse representation

Zou

Feng

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Source recording device recognition is an important emerging research field of digital media forensic. Most of the prior literature focus on the recording device identification problem. In this study we propose a source cell phone verification scheme based on sparse representation. We employed Gaussian supervectors (GSVs) based on Mel-frequency cepstral coefficients (MFCCs) extracted from the speech recordings to characterize the intrinsic fingerprint of the cell phone. For the sparse representation, both exemplar based dictionary and dictionary learned by K-SVD algorithm were examined to this problem. Evaluation experiments were conducted on a corpus consists of speech recording recorded by 14 cell phones. The achieved equal error rate (EER) demonstrated the feasibility of the proposed scheme.

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Blind source mobile device identification based on recorded call

Cited by 12 publications

References 23 publications

Audio Spectrogram Factorization for Classification of Telephony Signals below the Auditory Threshold

Audio Spectrogram Factorization for Classification of Telephony Signals below the Auditory Threshold

Dual Attention Pooling Network for Recording Device Classification Using Neutral and Whispered Speech

Cell phone verification from speech recordings using sparse representation

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