  This report presents the technical details of our submission to the 2023
Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge. The
task is to learn the mapping from audio samples to their corresponding action
labels. To achieve this goal, we propose a simple yet effective single-stream
CNN-based architecture called AudioInceptionNeXt that operates on the
time-frequency log-mel-spectrogram of the audio samples. Motivated by the
design of the InceptionNeXt, we propose parallel multi-scale depthwise
separable convolutional kernels in the AudioInceptionNeXt block, which enable
the model to learn the time and frequency information more effectively. The
large-scale separable kernels capture the long duration of activities and the
global frequency semantic information, while the small-scale separable kernels
capture the short duration of activities and local details of frequency
information. Our approach achieved 55.43% of top-1 accuracy on the challenge
test set, ranked as 1st on the public leaderboard. Codes are available
anonymously at https://github.com/StevenLauHKHK/AudioInceptionNeXt.git.
