Shared Representation Learning for Reference-Guided Targeted Sound Detection
This work addresses targeted sound detection for audio processing applications, offering incremental improvements in performance and generalization.
The paper tackles the problem of detecting and localizing a target sound in a mixture using a reference audio, proposing a unified encoder with shared representation learning. It achieves a segment-level F1 score of 83.15% and overall accuracy of 95.17% on the URBAN-SED dataset, establishing a new state-of-the-art.
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.