Authors: Qi Li (University of Guelph), Giuliano Sovernigo (University of Guelph), and Xiaodong Lin (University of Guelph)
DFRWS USA 2022
Historically, criminal investigations hinging on recorded audio data required manual application of forensic techniques to extract relevant information. These methods usually focus mainly on voices and speaker identification, but rarely focus on the wealth of forensic information available in the background noises present in the recording. Our paper introduces methods of automatically extracting, separating, and classifying background noises, allowing for the difficult, time-consuming process of audio analysis to be handled by software. Once the audio has been classified and examined by our proposed tools, the results can be used by investigators and forensic experts to aid in traditional investigative methods. Using location information as an example, we propose a fully automated location inference process based on background noise. Detailed experimental results show that our scheme is effective and fast. Our proposed framework intends to provide a neat, automated, and accurate analysis of the information present in background audio, and to provide a new source of forensic information for investigators to leverage. In contrast to existing similar work, our scheme not only realistically considers mixed human voice speech, but also considers the case of multiple background noise mixes. To the best of our knowledge, this is the first forensic work that considers background noise in a complex environment.