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This report presents the technical details of our submission to the 2023Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. Thetask is to learn the mapping from audio samples to their corresponding actionlabels. To achieve this goal, we propose a simple yet effective single-streamCNN-based architecture called AudioInceptionNeXt that operates on thetime-frequency log-mel-spectrogram of the audio samples. Motivated by thedesign of the InceptionNeXt, we propose parallel multi-scale depthwiseseparable convolutional kernels in the AudioInceptionNeXt block, which enablethe model to learn the time and frequency information more effectively. Thelarge-scale separable kernels capture the long duration of activities and theglobal frequency semantic information, while the small-scale separable kernelscapture the short duration of activities and local details of frequencyinformation. Our approach achieved 55.43% of top-1 accuracy on the challengetest set, ranked as 1st on the public leaderboard. Codes are availableanonymously at https://github.com/StevenLauHKHK/AudioInceptionNeXt.git.

AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023