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Subband Aware CNN for Cell-Phone Recognition

Identifying the model of a cell-phone with which an audio recording is made serves an important forensic purpose. In this paper, we propose a channel attention mechanism based on subband awareness to focus on the most relevant parts of the frequency bands to produce more efficient feature representations for cell-phone recognition task. A multi-stream network is introduced to fully exploit difference among frequency bands that provide critical clues to identify the fingerprints from the built-in microphones, and thus is able to recognize cell-phones from different manufacturers and even different models from the same manufacturer. The effectiveness of the proposed design is validated by a collection of speaker-independent audio recordings from 20 models of cell-phones made by 5 major manufacturers. In particular, the fusion of data augmentation and attention strategy greatly increases the robustness of the scheme when additive noise is present in the recorded audios. Finally, the salient regions infer the critical subbands for the recognition of different cell-phone models.