# Activity-Guided Industrial Anomalous Sound Detection against Interferences

arXiv: https://arxiv.org/abs/2409.01885 · HTML: https://mlsanity.com/paper/2409.01885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [Anomalous sound detection based on interpolation deep neural network](https://mlsanity.com/paper/2005.09234/) (2020)
- [All for One and One for All: Improving Music Separation by Bridging Networks](https://mlsanity.com/paper/2010.04228/) (2020)
- [Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining](https://mlsanity.com/paper/2304.03588/) (2023)
- [Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation](https://mlsanity.com/paper/1609.04557/) (2016)
- [Multitask learning for instrument activation aware music source separation](https://mlsanity.com/paper/2008.00616/) (2020)
- [Exploring Aligned Lyrics-Informed Singing Voice Separation](https://mlsanity.com/paper/2008.04482/) (2020)
- [Anomalous Sound Detection Based on Machine Activity Detection](https://mlsanity.com/paper/2204.07353/) (2022)
- [In-Network Processing Acoustic Data for Anomaly Detection in Smart Factory](https://mlsanity.com/paper/2110.01381/) (2021)
- [Amicable examples for informed source separation](https://mlsanity.com/paper/2110.05059/) (2021)
- [Anomalous Sound Detection Based on Sound Separation](https://mlsanity.com/paper/2305.15859/) (2023)

## Abstract (arXiv)

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.