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Sound event detection (SED) is one of tasks to automate function by humanauditory system which listens and understands auditory scenes. Therefore, wewere inspired to make SED recognize sound events in the way human auditorysystem does. Spectro-temporal receptive field (STRF), an approach to describethe relationship between perceived sound at ear and transformed neural responsein the auditory cortex, is closely related to recognition of sound. In thiswork, we utilized STRF as a kernel of the first convolutional layer in SEDmodel to extract neural response from input sound to make SED model similar tohuman auditory system. In addition, we constructed two-branched SED model namedas Two Branch STRFNet (TB-STRFNet) composed of STRF branch and baseline branch.While STRF branch extracts sound event information from auditory neuralresponse, baseline branch extracts sound event information directly from themel spectrogram just as conventional SED models do. TB-STRFNet outperformed theDCASE baseline by 4.3% in terms of threshold-independent macro F1 score,achieving 4th rank in DCASE Challenge 2023 Task 4b. We further improvedTB-STRFNet by applying frequency dynamic convolution (FDYConv) which alsoleveraged domain knowledge on acoustics. As a result, two branch model appliedwith FDYConv on both branches outperformed the DCASE baseline by 6.2% in termsof the same metric.

Auditory Neural Response Inspired Sound Event Detection Based on Spectro-temporal Receptive Field