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Machine learning algorithms, when trained on audio recordings from a limitedset of devices, may not generalize well to samples recorded using other deviceswith different frequency responses. In this work, a relatively straightforwardmethod is introduced to address this problem. Two variants of the approach arepresented. First requires aligned examples from multiple devices, the secondapproach alleviates this requirement. This method works for both time andfrequency domain representations of audio recordings. Further, a relation tostandardization and Cepstral Mean Subtraction is analysed. The proposedapproach becomes effective even when very few examples are provided. Thismethod was developed during the Detection and Classification of Acoustic Scenesand Events (DCASE) 2019 challenge and won the 1st place in the scenario withmis-matched recording devices with the accuracy of 75%. Source code for theexperiments can be found online.

Spectrum Correction: Acoustic Scene Classification with Mismatched Recording Devices