Investigation of machine learning algorithms to model perception of sound

Predicting human response to complex sound is a nontrivial task. Besides large differences among subjects and practically infinite types of stimuli, human response to sound is typically quantified by a few parameters having nonlinear behavior. Still, such predictions are valuable for the assessment of the perception of sound, which is a critical step toward the development of systems that offer improved human comfort, productivity and wellness.The overall objective of this work is to learn about human perception of sound through the use of machine learning algorithms. Learning algorithms are ideal for modeling the complex behavior of subjective parameters and identifying new trends in behavior using knowledge accumulated from different experiments. This work compares the performance of four learning algorithms (linear regression (LR), support vector machines (SVM), decision trees (DTs), and bagged DTs/random forests (BDTRF)) used to construct models capable of predicting annoyance due to complex sound. Co...