We address a practical scenario of anomaly detection for industrial sound data, where the sound of a target machine is corrupted by background noise and interference from neighboring machines. Overcoming this challenge is difficult since the interference is often virtually indistinguishable from the target machine without additional information. To address the issue, we propose SSAD, a framework of source separation (SS) followed by anomaly detection (AD), which leverages machine activity information, often readily available in practical settings. SSAD consists of two components: (i) activity-informed SS, enabling effective source separation even given interference with similar timbre, and (ii) two-step masking, robustifying anomaly detection by emphasizing anomalies aligned with the machine activity. Our experiments demonstrate that SSAD achieves comparable accuracy to a baseline with full access to clean signals, while SSAD is provided only a corrupted signal and activity information. In addition, thanks to the activity-informed SS and AD with the two-step masking, SSAD outperforms standard approaches, particularly in cases with interference. It highlights the practical efficacy of SSAD in addressing the complexities of anomaly detection in industrial sound data.

Our workdays are getting noisier. Industry research shows that employees at larger organizations use an average of 36 cloud services at work, including tools for productivity, project management, communication, and storage. This information overload is a key source of pain for people at work--and a prime opportunity to leverage the help of machine intelligence. When we talk about machine intelligence at Dropbox, we mean the whole range of applied machine learning, from simple linear classifiers to advanced deep learning networks. For many years we've been building a world-class machine learning team, improving our platform behind the scenes.