  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.
