{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T16:36:45Z","timestamp":1787935005208,"version":"build-2784847793"},"reference-count":33,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T00:00:00Z","timestamp":1699142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Acoustic sensing provides crucial data for anomalous sound detection (ASD) in condition monitoring. However, building a robust acoustic-sensing-based ASD system is challenging due to the unsupervised nature of training data, which only contain normal sound samples. Recent discriminative models based on machine identity (ID) classification have shown excellent ASD performance by leveraging strong prior knowledge like machine ID. However, such strong priors are often unavailable in real-world applications, limiting these models. To address this, we propose utilizing the imbalanced and inconsistent attribute labels from acoustic sensors, such as machine running speed and microphone model, as weak priors to train an attribute classifier. We also introduce an imbalanced compensation strategy to handle extremely imbalanced categories and ensure model trainability. Furthermore, we propose a score fusion method to enhance anomaly detection robustness. The proposed algorithm was applied in our DCASE2023 Challenge Task 2 submission, ranking sixth internationally. By exploiting acoustic sensor data attributes as weak prior knowledge, our approach provides an effective framework for robust ASD when strong priors are absent.<\/jats:p>","DOI":"10.3390\/s23218984","type":"journal-article","created":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T07:35:06Z","timestamp":1699169706000},"page":"8984","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Acoustic-Sensing-Based Attribute-Driven Imbalanced Compensation for Anomalous Sound Detection without Machine Identity"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9682-7212","authenticated-orcid":false,"given":"Yifan","family":"Zhou","sequence":"first","affiliation":[{"name":"Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0924-408X","authenticated-orcid":false,"given":"Yanhua","family":"Long","sequence":"additional","affiliation":[{"name":"Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China"},{"name":"Unisound AI Technology Co., Ltd., Beijing 100089, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0666-5551","authenticated-orcid":false,"given":"Haoran","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of ECE, University of Texas at Dallas, Richardson, TX 75080, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"ref_1","unstructured":"Kawaguchi, Y., Imoto, K., Koizumi, Y., Harada, N., Niizumi, D., Dohi, K., Tanabe, R., Purohit, H., and Endo, T. 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