A master thesis from Aalborg University
Classification of Room Characteristics Using Convolutional Neural Networks: Machine Learning and Room Acoustics
[Convolutional Neural Networks Anvendt Til Klassificering af Rumkarakteristikker]
Author(s)
Term
4. semester
Education
Publication year
2019
Submitted on
2019-06-06
Pages
101 pages
Abstract
Classification of room characteristics based on Room Transfer Functions (RTFs) relates to various topics in acoustics, e.g. room response equalisation and forensic audio analysis. In this Master’s thesis, convolutional neural network models are proposed for room volume and wall admittance classification. The proposed models are trained using supervised learning based on simulated RTFs from a sound field model derived from the Helmholtz equation for wave propagation in small rooms. The classification performance of the models benefit from having microphones placed in structured grids compared to randomly placed microphones. For classification of the room volume, a classification accuracy above approximately 90% for more than one microphone is achieved, while for wall admittance classification the accuracy is above 80%. The models for room volume classification were more sensitive to additive noise than the wall admittance models, which still provided better then initial accuracy at 0dB SNR.
Keywords
Documents
Colophon: This page is part of the AAU Student Projects portal, which is run by Aalborg University. Here, you can find and download publicly available bachelor's theses and master's projects from across the university dating from 2008 onwards. Student projects from before 2008 are available in printed form at Aalborg University Library.
If you have any questions about AAU Student Projects or the research registration, dissemination and analysis at Aalborg University, please feel free to contact the VBN team. You can also find more information in the AAU Student Projects FAQs.