  Predicting odor's pleasantness simplifies the evaluation of odors and has the
potential to be applied in perfumes and environmental monitoring industry.
Classical algorithms for predicting odor's pleasantness generally use a manual
feature extractor and an independent classifier. Manual designing a good
feature extractor depend on expert knowledge and experience is the key to the
accuracy of the algorithms. In order to circumvent this difficulty, we proposed
a model for predicting odor's pleasantness by using convolutional neural
network. In our model, the convolutional neural layers replace manual feature
extractor and show better performance. The experiments show that the
correlation between our model and human is over 90% on pleasantness rating. And
our model has 99.9% accuracy in distinguishing between absolutely pleasant or
unpleasant odors.
