Now ForReal: Towards Practical Forensic Room Identification with Real-World Audio Data

Recovering the place of origin of, e.g., a phone call can aid the reconstruction of events in a criminal case. For audio forensics, identifying the recording location exclusively from an audio signal still poses a challenge. While various works address this task, they evaluate on semi-synthetic reverberant speech data in a supervised setting. Thus, there barely exist any empirical insights on practical forensic recording environment identification, i.e., the handling of real-world audio data from case-dependent locations that are unknown to a tool in advance. In this work, we take a first step towards such a practical scenario. We collect a set of real-world speech from several rooms under varying recording parameters. In forensic cases, audio evidence usually stems from uncontrolled sources, such that factors like the recording position, speaker or microphone can be unknown and reverberation characteristics are of mixed quality. The influence of such factors for room identification is analysed in detail, with several results. For example, we find that prior knowledge about the recording position strongly aids classification, and that characteristics of a speaker’s voice notably impact performance. Instructions on how to obtain the data set are online: https://faui1-gitlab.cs.fau.de/mmsec/forreal