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

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.

Source Microphone Identification Using Swin Transformer

“…The method in [23] uses deep-representation learning to extract features from the speech recordings and then applies spectral clustering to group the users into clusters based on those features. O. Eskidere [24] uses wavelet-based features to extract characteristics of the speech signal and then uses a machine-learning algorithm to classify the signal based on these features. In [25], a model for verifying a person s identity using speech recordings taken from their cell phone is presented.…”