Audio recordings may provide important evidence in criminal investigations.
One such case is the forensic association of the recorded audio to the
recording location. For example, a voice message may be the only investigative
cue to narrow down the candidate sites for a crime. Up to now, several works
provide tools for closed-set recording environment classification under
relatively clean recording conditions. However, in forensic investigations, the
candidate locations are case-specific. Thus, closed-set tools are not
applicable without retraining on a sufficient amount of training samples for
each case and respective candidate set. In addition, a forensic tool has to
deal with audio material from uncontrolled sources with variable properties and
quality.
In this work, we therefore attempt a major step towards practical forensic
application scenarios. We propose a representation learning framework called
EnvId, short for environment identification. EnvId avoids case-specific
retraining. Instead, it is the first tool for robust few-shot classification of
unseen environment locations. We demonstrate that EnvId can handle forensically
challenging material. It provides good quality predictions even under unseen
signal degradations, environment characteristics or recording position
mismatches.
Our code and datasets will be made publicly available upon acceptance.

通过环境识别的鲁棒性少样本分类系统 EnvId，我们实现了对未知环境位置的取证调查，能够处理取证材料中的挑战性问题，包括信号降级、环境特征和录音位置不匹配等。