Detect what you want: Target Sound Detection
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Human beings can perceive a target sound that we are interested in from a multi-source environment by the selective auditory attention, however, such functionality was hardly ever explored in machine hearing.This paper address the target sound detection (TSD), which aims to detect the target sound signal from a mixture audio when a target sound's reference audio is given.We present a novel target sound detection network (TSDNet) which consists of two main parts: A conditional and a detection network. The former aims at generating a sound-discriminative conditional embedding vector representing the global information of the target sound. The latter takes both the mixture audio and the conditional embedding vector as inputs, and produces the detection result. These two networks can be jointly optimized with a multi-task learning approach to further improve the performance. In addition, we study both supervised and weakly supervised strategies to train TSDNet.To evaluate our methods, we build a target sound detection dataset (TSD Dataset) based on URBAN-SED and URBAN-SOUND8K datasets. Experimental results indicate our system can get better performance than universal sound event detection.
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