MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification

• URL: http://arxiv.org/abs/2506.11331v1
• Date: Thu, 12 Jun 2025 22:02:07 GMT
• Title: MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
• Authors: Jihoon Yun, Chengzhang Li, Dhrubojyoti Roy, Anish Arora,
• Abstract summary: Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments.<n>Mote-scale Unsupervised Domain Adaptation for Sounds (MUDAS) is a framework developed for multi-label sound classification in resource-constrained IoT settings.
• Score: 4.60781637717838
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments where data distribution shifts can degrade performance. Existing UDA algorithms are designed for single-label tasks and rely on significant computational resources, limiting their use in multi-label scenarios and in resource-constrained IoT devices. Overcoming these limitations is particularly challenging in contexts such as urban sound classification, where overlapping sounds and varying acoustics require robust, adaptive multi-label capabilities on low-power, on-device systems. To address these limitations, we introduce Mote-scale Unsupervised Domain Adaptation for Sounds (MUDAS), a UDA framework developed for multi-label sound classification in resource-constrained IoT settings. MUDAS efficiently adapts models by selectively retraining the classifier in situ using high-confidence data, minimizing computational and memory requirements to suit on-device deployment. Additionally, MUDAS incorporates class-specific adaptive thresholds to generate reliable pseudo-labels and applies diversity regularization to improve multi-label classification accuracy. In evaluations on the SONYC Urban Sound Tagging (SONYC-UST) dataset recorded at various New York City locations, MUDAS demonstrates notable improvements in classification accuracy over existing UDA algorithms, achieving good performance in a resource-constrained IoT setting.

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