Prediction of subjective assessments for a noise map using deep neural networks

In this paper, we investigate a method of creating noise maps that take account of human senses. Physical measurements are not enough to design our living environment and we need to know subjective assessments. To predict subjective assessments from loudness values, we propose to use metadata related to where, who and what is recording. The proposed method is implemented using deep neural networks because these can naturally treat a variety of information types. First, we evaluated its performance in predicting five-point subjective loudness levels based on a combination of several features: location-specific, participant-specific, and sound-specific features. The proposed method achieved a 16.3 point increase compared with the baseline method. Next, we evaluated its performance based on noise map visualization results. The proposed noise maps were generated from the predicted subjective loudness level. Considering the differences between the two visualizations, the proposed method made fewer errors than the baseline method.