Recognition of Audio Source Recording Device using MFCC and RNN

Abstract

Accurate identification of audio source recording devices is paramount in digital forensic investigations, including topics like copyright protection, tamper detection, and audio source forensics. This work presented a novel method for learning feature representations using temporal audio characteristics, such as Mel Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform (CQT), obtained from segmented acoustic features. Subsequently creates a structured representation learning model by combining Long Short-Term Memory Networks (LSTM) with Recurrent Neural Networks (RNN). This model efficiently condenses spatial information, resulting in accurate recognition, by utilizing temporal modelling and time-frequency representation. The performance of the proposed methods is tested on 10-second audio signals recorded with four different audio recording devices. The outcomes of the experiment show an amazing degree of accuracy with 96% in classifying four types of recording audio source devices. This method promises improved efficacy in a variety of forensic circumstances and represents a substantial development in audio forensic analysis. The performance metrics of audio source recording using CQT-RNN and MFCC-RNN are compared, and also compared with state-of-the-art methods. A user interface has been developed to facilitate the recognition of the source device for test audio signals using the proposed method. Overall, this research marks a substantial advancement in audio forensic analysis, providing a robust, accurate, and user friendly solution for the identification of audio source recording devices, and underscoring its potential for widespread forensic applications.