EnvId：一种用于未见环境法医少样本识别的度量学习方法
EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments
AI总结：
针对法医调查中录音环境识别需处理特定候选地点且监督学习不适用的问题，提出度量学习框架EnvId，将任务建模为少样本分类以避免特定案件重训练，可处理未见信号退化等挑战性材料并提供高质量预测。
摘要：
录音可能在刑事调查中提供重要证据。其中一种情况是将录音与录制地点进行法医关联。例如，语音消息可能是缩小犯罪候选地点范围的唯一调查线索。到目前为止，已有若干研究在相对干净的录音条件下提供了用于闭集录制环境识别的监督分类工具。然而，在法医调查中，候选地点是特定于案件的。因此，若不对每个案件及相应候选集的足够训练样本重新训练分类器，监督学习技术则不适用。此外，法医工具还需处理来自不受控来源、具有可变属性和质量的音频材料。在这项工作中，我们因此尝试朝着实际法医应用场景迈出重要一步。我们提出一种名为EnvId（环境识别的缩写）的表示学习框架。EnvId通过将任务建模为少样本分类问题来避免特定于案件的重新训练。我们证明EnvId能够处理具有法医挑战性的材料，即使在未见的信号退化、分布外的混响特征或录制位置不匹配的情况下，也能提供高质量的预测。
英文摘要：
Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of a recorded audio to its 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 supervised classification tools for closed-set recording environment identification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, supervised learning techniques are not applicable without retraining a classifier 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 by modeling the task as a few-shot classification problem. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, out-of-distribution reverberation characteristics or recording position mismatches.
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