An approach based on sound classification to predict soundscape perception through machine learning
A growing amount of literature and a series of ISO standards focus on concept, data collection, and data analysis methods of soundscapes. Yet, this field of research still lacks predictive models. We hypothesize that machine learning methods can be used to develop a predictive model by identifying the audio content of soundscapes and correlating it with individuals’ perceived affective response to the soundscapes. Therefore, this research aims to identify machine learning-based sound classification methods for analyzing the audio content of soundscapes and using its output in a second model for evaluating the association between the audio content and perception of the soundscape. We focused on museum soundscapes to conduct our research. The methodology of this thesis is divided into two parts. For the first part, we used Convolutional Neural Networks for classifying the audio content of the soundscape. Due to their limitations, we used a different approach rather than the typical environmental sound classification methods. We used musical instruments for the training dataset and optimized the neural network for this type of task. The convolutional neural network classified the audio content of the soundscapes based on their similarities to the musical instruments of the dataset. We conducted an online soundscape perception survey to measure participants' affective responses to different museum soundscapes for the second part. To predict individuals’ perception of soundscapes, we developed a feedforward neural network model. This model used the audio content output from the sound classification model and the soundscape survey data to predict the perceived affective quality of soundscapes. We concluded the thesis by conducting statistical analyses to explore the association between the variable used in the predictive model.