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Target sound detection (TSD) aims to detect the target sound from a mixtureaudio given the reference information. Previous methods use a conditionalnetwork to extract a sound-discriminative embedding from the reference audio,and then use it to detect the target sound from the mixture audio. However, thenetwork performs much differently when using different reference audios (e.g.performs poorly for noisy and short-duration reference audios), and tends tomake wrong decisions for transient events (i.e. shorter than $1$ second). Toovercome these problems, in this paper, we present a reference-aware andduration-robust network (RaDur) for TSD. More specifically, in order to makethe network more aware of the reference information, we propose an embeddingenhancement module to take into account the mixture audio while generating theembedding, and apply the attention pooling to enhance the features of targetsound-related frames and weaken the features of noisy frames. In addition, aduration-robust focal loss is proposed to help model different-duration events.To evaluate our method, we build two TSD datasets based on UrbanSound andAudioset. Extensive experiments show the effectiveness of our methods.

RaDur: A Reference-aware and Duration-robust Network for Target Sound Detection