Exploring A New Method for Food Likability Rating Based on DT-CWT Theory

In this paper, we mainly investigate subjects' food likability based on audio-related features as a contribution to EAT ? the ICMI 2018 Eating Analysis and Tracking challenge. Specifically, we conduct 4-level Double Tree Complex Wavelet Transform decomposition of an audio signal, and obtain five sub-audio signals with frequencies ranging from low to high. For each sub-audio signal, not only 'traditional' functional-based features but also deep learning-based features via pretrained CNNs based on SliCQ-nonstationary Gabor transform and a cochleagram map, are calculated. Besides, the original audio signals based Bag-of-Audio-Words features extracted by the openXBOW toolkit are used to enhance the model as well. Finally, the early fusion of all these three kinds of features can lead to promising results, yielding the highest UAR of 79.2 % by means of a leave-one-speaker-out cross-validation, which holds a 12.7 % absolute gain compared with the baseline of 66.5 % UAR.