Microphone Classification Using Fourier Coefficients

Buchholz, Robert; Kraetzer, Christian; Dittmann, Jana

doi:10.1007/978-3-642-04431-1_17

Cited by 73 publications

(41 citation statements)

References 8 publications

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“…To this end, identification of the mi-20 crophone and recording environment have been addressed in several studies [9,10,11,12,13,14,15]. For example, [10] studied classification of 4 different microphones and 10 different environments (rooms) using different time-domain features while [11] used Fourier coefficients to classify 7 different microphones. The authors of [13] studied identification of 10 telephone handsets using Mel-and linear-frequency cepstral coefficients 25 (MFCCs and LFCCs), reporting higher than 90 % classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Source cell-phone recognition from recorded speech using non-speech segments

Hanilçi

Kinnunen

2014

Digital Signal Processing

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In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yields higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42 % to 98.39 % and equal error rate (EER) reduces from 1.20% to 0.47 % when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.

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Section: Introductionmentioning

confidence: 99%

Source cell-phone recognition from recorded speech using non-speech segments

Hanilçi

Kinnunen

2014

Digital Signal Processing

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“…The best correct microphone classification results were achieved as 75.99% by the Bayesian classification and as 41.57% by the k-means clustering. In [11], the authors showed that fast Fourier transform coefficients could be used in order to determine the microphone model. They tested 7 different microphones and achieved a 93.5% correct classification rate with linear regression models.…”

Section: Introductionmentioning

confidence: 99%

“…In [12], it was pointed out that fusion operations, such as the match level, rank level, and decision level, could be implemented for reliable microphone classification. Using the same microphones that were used in [10,11], 100% accuracy was reached via the method of rank level fusion. Moreover, Garcia-Romero and Espy-Wilson [13] investigated the performance of MFCCs and linear scale cepstral coefficients with the support vector machine (SVM) classifier.…”

Section: Introductionmentioning

confidence: 99%

Source microphone identification from speech recordings based on a Gaussian mixture model

Eskidere¹

2014

Turk J Elec Eng & Comp Sci

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Abstract:Microphone identification is a specific type of media forensics that investigates whether it is possible to identify the source microphone from speech recordings. The main aim of this study is to find out which of the several feature extraction techniques are best suited to the source microphone identification systems. We perform microphone identification experiments with 16 different microphones using 3 datasets. In order to improve the results on the datasets, we also investigate the important parameters that may affect the microphone identification performance. Our experimental results show that the proposed method is comparable to the existing studies in a closed-set identification rate.

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“…2. Statistical pattern recognition based techniques [9][10][11][12][13][14][15][16][17] have been proposed for recording location and device identification. However, these methods are limited by their low accuracy and inability to uniquely map an audio recording to the source.…”

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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Microphone Classification Using Fourier Coefficients

Cited by 73 publications

References 8 publications

Source cell-phone recognition from recorded speech using non-speech segments

Source cell-phone recognition from recorded speech using non-speech segments

Source microphone identification from speech recordings based on a Gaussian mixture model

Acoustic environment identification using unsupervised learning

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