Acoustic-URL: Multisignal-Domain and Multichannel Fusion for Unsupervised Representation Learning in Acoustic Sensing

Abstract

The proliferation of multimicrophone IoT devices—such as smartphones, smart speakers, and wearables—has enabled diverse acoustic sensing applications. However, scarcely labeled data and poor cross-task transferability continue to limit traditional supervised methods. To address this, we propose acoustic-URL, an unsupervised representation learning framework using cross-domain fusion (cdf) and cross-channel contrastive learning (CCCL) to extract transferable features from unlabeled multichannel audio. Our approach integrates time-domain waveforms and spatial-domain channel impulse responses (CIRs) to capture both temporal and spatial information. By aligning features from different microphones that capture the same acoustic event, the model learns spatially consistent representations. Evaluations on four tasks—digit recognition, writer identification, gesture recognition, and footstep-based identification—under varying supervision and task-domain shifts show that acoustic-URL consistently outperforms supervised baselines, especially in low-label settings, and demonstrates strong cross-task generalization.