Abstract
The recording device along with the acoustic environment plays a major role in digital audio forensics. We propose an acoustic source identification system in this paper, which includes identifying both the recording device and the environment in which it was recorded. A hybrid Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) is used in this study to automatically extract environments and microphone features from the speech sound. In the experiments, we investigated the effect of using the voiced and unvoiced segments of speech on the accuracy of the environment and microphone classification. We also studied the effect of background noise on microphone classification in 3 different environments, i.e., very quiet, quiet, and noisy. The proposed system utilizes a subset of the KSU-DB corpus containing 3 environments, 4 classes of recording devices, 136 speakers (68 males and 68 females), and 3600 recordings of words, sentences, and continuous speech. This research combines the advantages of both CNN and RNN (in particular bidirectional LSTM) models, called CRNN. The speech signals were represented as a spectrogram and were fed to the CRNN model as 2D images. The proposed method achieved accuracies of 98% and 98.57% for environment and microphone classification, respectively, using unvoiced speech segments.
Highlights
Forensics refers to the science that uses scientific methods or expertise to investigate crimes or examine an evidence which may be presented in a court of law
Digital media forensics is a branch of forensic science that involves ensuring that the digital content is accurate and authentic [1]
We studied the effect of background noise on microphone classification in different environments and the effect of microphone quality on the environment classification
Summary
Forensics refers to the science that uses scientific methods or expertise to investigate crimes or examine an evidence which may be presented in a court of law. Digital audio forensic includes different activities, such as identifying speakers from the audio, identifying the environment or the recording device, and checking the integrity of the audio content. Contentbased authentication analyzes the actual content of the audio recording, including the Electric Network Frequency (ENF) analysis, acquisition device, and environment identification. There is no previous study that used the proposed model to classify environments and the microphones using the KSU-DB corpus.
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