用统一编码器让参考音与混响音对齐，提升声音定位精度。
Shared Representation Learning for Reference-Guided Targeted Sound Detection
- 共享表示空间统一处理参考音和混响音，减少结构复杂度
- 在URBAN-SED数据集上达83.15%段级F1和95.17%准确率
- 适合做声音分离与听觉注意力相关研究的读者
人类听众能通过选择性听觉注意从复杂声景中分离出目标声音，这启发了目标声音检测（TSD）的研究。该任务要求在提供目标声音参考音频的前提下，检测并定位混合音频中的目标声音。现有方法依赖为参考音频生成声学判别性条件嵌入向量，并与混合音频编码器配对，采用多任务学习联合优化。本文提出一种统一编码器架构，将参考音频与混合音频共同映射到共享表示空间，强化对齐效果的同时降低模型复杂度。遵循多任务训练范式，该方法显著优于先前方法，在URBAN-SED数据集上实现83.15%的段级F1分数和95.17%的整体准确率，建立新的基准。
原文摘要 · Abstract (English)
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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