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Freely available and easy-to-use audio editing tools make it straightforwardto perform audio splicing. Convincing forgeries can be created by combiningvarious speech samples from the same person. Detection of such splices isimportant both in the public sector when considering misinformation, and in alegal context to verify the integrity of evidence. Unfortunately, most existingdetection algorithms for audio splicing use handcrafted features and makespecific assumptions. However, criminal investigators are often faced withaudio samples from unconstrained sources with unknown characteristics, whichraises the need for more generally applicable methods.  With this work, we aim to take a first step towards unconstrained audiosplicing detection to address this need. We simulate various attack scenariosin the form of post-processing operations that may disguise splicing. Wepropose a Transformer sequence-to-sequence (seq2seq) network for splicingdetection and localization. Our extensive evaluation shows that the proposedmethod outperforms existing dedicated approaches for splicing detection [3, 10]as well as the general-purpose networks EfficientNet [28] and RegNet [25].

Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks