Identifying acquisition devices from recorded speech signals using wavelet-based features

Authors

ÖMER ESKİDERE

DOI

10.3906/elk-1312-193

Abstract

Speech characteristics have played a critical role in media forensics, particularly in the investigation of evidence. This study proposes two wavelet-based feature extraction methods for the identification of acquisition devices from recorded speech. These methods are discrete wavelet-based coefficients (DWBCs) and wavelet packet-based coefficients, which are mainly based on a multiresolution analysis. These features' ability to capture characteristics of acquisition devices is compared to conventional mel frequency cepstral coefficients and subband-based coefficients. In the experiments, 14 different audio acquisition devices were trained and tested using support vector machines. Experimental results showed that DWBCs can effectively be used in source audio acquisition device identification problems.

Keywords

Media forensics, acquisition device identification, wavelet packet transform, discrete wavelet transform

First Page

1942

Last Page

1954

Recommended Citation

ESKİDERE, ÖMER (2016) "Identifying acquisition devices from recorded speech signals using wavelet-based features," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 24: No. 3, Article 87. https://doi.org/10.3906/elk-1312-193
Available at: https://journals.tubitak.gov.tr/elektrik/vol24/iss3/87