Summary of Adversarial Domain Adaptation For Metal Cutting Sound Detection: Leveraging Abundant Lab Data For Scarce Industry Data, by Mir Imtiaz Mostafiz (1) et al.
Adversarial Domain Adaptation for Metal Cutting Sound Detection: Leveraging Abundant Lab Data for Scarce Industry Data
by Mir Imtiaz Mostafiz, Eunseob Kim, Adrian Shuai Li, Elisa Bertino, Martin Byung-Guk Jun, Ali Shakouri
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel adversarial domain adaptation (DA) approach leverages abundant lab data to learn from scarce industry data, training a cutting-sound detection model using machine learning (ML) models inspired by experienced machinists. The approach projects features into two separate latent spaces, enabling the learning of domain-independent representations. Two mechanisms for adversarial learning are analyzed, with the discriminator working as an adversary or critic in separate settings. The model outperforms multi-layer perceptron based vanilla domain adaptation models on curated datasets, achieving near 92%, 82%, and 85% accuracy respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve manufacturing efficiency and tool life is by using machine learning (ML) to detect cutting sounds. This method can be used in a complex manufacturing environment without being too expensive or intrusive. However, getting the data needed for training the model can be time-consuming and costly. To solve this problem, researchers propose an innovative approach that uses abundant lab data to learn from scarce industry data. They use two separate latent spaces to project features, which helps to create domain-independent representations. The approach is tested on real-world datasets and shows promising results. |
Keywords
» Artificial intelligence » Domain adaptation » Machine learning
