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

翻译：机器学习算法,如果在有限的一套装置的录音方面受过培训,则可能无法很好地概括到使用不同频率响应的其他装置所记录的样品。在这项工作中,采用了相对简单的方法来解决这个问题。提出了两种方法的变式。首先需要多个装置的一致例子,第二种方法减轻了这一要求。这种方法既能用于时间,也能用于频率,对录音的显示方式。此外,还分析了标准化和Cepstrales 中值减法的关系。即使提供了极少的例子,拟议的方法也变得有效。这种方法是在2019年声频和事件探测和分类(DCASE)挑战期间开发的,并赢得了情景中第一位置,记录装置的误配率为75%。实验的源代码可以在网上找到。