In this study, we introduce a new augmentation technique to enhance the
resilience of sound event classification (SEC) systems against device
variability through the use of CycleGAN. We also present a unique dataset to
evaluate this method. As SEC systems become increasingly common, it is crucial
that they work well with audio from diverse recording devices. Our method
addresses limited device diversity in training data by enabling unpaired
training to transform input spectrograms as if they are recorded on a different
device. Our experiments show that our approach outperforms existing methods in
generalization by 5.2% - 11.5% in weighted f1 score. Additionally, it surpasses
the current methods in adaptability across diverse recording devices by
achieving a 6.5% - 12.8% improvement in weighted f1 score.

通过引入 CycleGAN 作为新的增强技术，本研究提出了一种针对设备差异性提高声音事件分类系统鲁棒性的方法，并展示了一个独特的数据集用于评估该方法。实验结果显示，该方法在加权 F1 分数上的泛化性能优于现有方法 5.2% - 11.5%，在不同录制设备上的适应性表现也超过当前方法，加权 F1 分数提高了 6.5% - 12.8%。

麦克风转换：减轻声音事件分类中的设备差异

Microphone Conversion: Mitigating Device Variability in Sound Event  Classification

IoT (Internet of Things) refers to the network of interconnected physical
devices, vehicles, home appliances, and other items embedded with sensors,
software, and connectivity, enabling them to collect and exchange data. IoT
Forensics is collecting and analyzing digital evidence from IoT devices to
investigate cybercrimes, security breaches, and other malicious activities that
may have taken place on these connected devices. In particular, EM-SCA has
become an essential tool for IoT forensics due to its ability to reveal
confidential information about the internal workings of IoT devices without
interfering these devices or wiretapping their networks. However, the accuracy
and reliability of EM-SCA results can be limited by device variability,
environmental factors, and data collection and processing methods. Besides,
there is very few research on these limitations that affects significantly the
accuracy of EM-SCA approaches for the crossed-IoT device portability as well as
limited research on the possible solutions to address such challenge.
Therefore, this empirical study examines the impact of device variability on
the accuracy and reliability of EM-SCA approaches, in particular
machine-learning (ML) based approaches for EM-SCA. We firstly presents the
background, basic concepts and techniques used to evaluate the limitations of
current EM-SCA approaches and datasets. Our study then addresses one of the
most important limitation, which is caused by the multi-core architecture of
the processors (SoC). We present an approach to collect the EM-SCA datasets and
demonstrate the feasibility of using transfer learning to obtain more
meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT
devices. Our study moreover contributes a new dataset for using deep learning
models in analysing Electromagnetic Side-Channel data with regards to the
cross-device portability matter.

物联网取证中，设备变异性对机器学习方法的电磁侧信道分析（EM-SCA）的准确性和可靠性有重要影响，并且跨设备可移植性是一个需要解决的挑战。本研究通过实证研究探讨了设备变异性对 EM-SCA 方法的影响，并提出了使用迁移学习解决 EM-SCA 在物联网取证中的跨设备可移植性问题。