Shared Representation Learning for Reference-Guided Targeted Sound Detection

• URL: http://arxiv.org/abs/2603.17025v1
• Date: Tue, 17 Mar 2026 18:05:21 GMT
• Title: Shared Representation Learning for Reference-Guided Targeted Sound Detection
• Abstract summary: We propose a unified encoder architecture that processes both the reference and mixture audio within a shared representation space.<n>We establish a new state-of-the-art benchmark for targeted sound detection with a segment-level F1 score of 83.15%.
• Score: 12.339689044414401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human listeners exhibit the remarkable ability to segregate a desired sound from complex acoustic scenes through selective auditory attention, motivating the study of Targeted Sound Detection (TSD). The task requires detecting and localizing a target sound in a mixture when a reference audio of that sound is provided. Prior approaches, rely on generating a sound-discriminative conditional embedding vector for the reference and pairing it with a mixture encoder, jointly optimized with a multi-task learning approach. In this work, we propose a unified encoder architecture that processes both the reference and mixture audio within a shared representation space, promoting stronger alignment while reducing architectural complexity. This design choice not only simplifies the overall framework but also enhances generalization to unseen classes. Following the multi-task training paradigm, our method achieves substantial improvements over prior approaches, surpassing existing methods and establishing a new state-of-the-art benchmark for targeted sound detection, with a segment-level F1 score of 83.15% and an overall accuracy of 95.17% on the URBAN-SED dataset.

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