A Odor Labeling Convolutional Encoder-Decoder for Odor Sensing in Machine Olfaction

Nowadays, machine olfaction has been widely used in many fields. In this paper, we proposed an odor labeling convolutional encoder-decoder (OLCE) for odor sensing. In order to demonstrate the performance of the model, a comparison of several algorithms that had been applied to machine olfaction was implemented. Experiment results demonstrated that OLCE had a decent performance using in machine olfaction. The challenge of establishing general odor characterization was discussed.

POP-CNN: Predicting Odor's Pleasantness with Convolutional Neural Network

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.