A Biologically Inspired Supervised Learning Rule for Audio Classification with Spiking Neural Networks

Peterson, Dylan George

A Biologically Inspired Supervised Learning Rule for Audio Classification with Spiking Neural Networks

dc.contributor.advisor	Leung, Henry
dc.contributor.author	Peterson, Dylan George
dc.contributor.committeemember	Westwick, David
dc.contributor.committeemember	Uddin, Gias
dc.contributor.committeemember	Wang, Xin
dc.date	2021-11
dc.date.accessioned	2021-06-21T21:32:11Z
dc.date.available	2021-06-21T21:32:11Z
dc.date.issued	2021-06-15
dc.description.abstract	Audio classification has many practical applications such as noise pollution monitoring, wildlife monitoring, audio surveillance, speech recognition, and more. For many of these applications, deploying classifiers on low powered devices for persistent monitoring is desirable. Artificial neural networks (ANN) have achieved significant success for audio classification tasks. However, it may not always be feasible to deploy current state-of-the-art ANNs to embedded devices due to their memory footprint and power consumption. Biologically inspired neural networks, also known as spiking neural networks (SNN), have been shown to significantly reduce power consumption during inference when compared with equivalent ANNs. They have also been theoretically proven to be more computationally powerful per unit than ANNs. These two properties make SNNs an attractive solution for machine learning tasks on low powered embedded devices, such as at the edge in an Internet of Things (IoT) sensor network. However, SNNs tend to lag behind in performance when compared to ANNs. This is partially because training SNNs is difficult since the standard backpropagation algorithm is not directly applicable due to the non-differentiable spiking nature of SNNs. Encoding data into spike trains compatible with SNNs is also an unresolved question when applying SNNs. This work compares different spike encoding schemes for audio data, and a learning algorithm for multilayer SNNs inspired by biologically plausible learning rules is developed. The proposed learning rule is then successfully applied to simple pattern recognition and audio classification tasks.	en_US
dc.identifier.citation	Peterson, D. G. (2021). A Biologically Inspired Supervised Learning Rule for Audio Classification with Spiking Neural Networks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.	en_US
dc.identifier.doi	http://dx.doi.org/10.11575/PRISM/38941
dc.identifier.uri	http://hdl.handle.net/1880/113519
dc.language.iso	eng	en_US
dc.publisher.faculty	Schulich School of Engineering	en_US
dc.publisher.institution	University of Calgary	en
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.	en_US
dc.subject	Spiking Neural Network	en_US
dc.subject	Classification	en_US
dc.subject	Machine Learning	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering--Electronics and Electrical	en_US
dc.title	A Biologically Inspired Supervised Learning Rule for Audio Classification with Spiking Neural Networks	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Electrical & Computer	en_US
thesis.degree.grantor	University of Calgary	en_US
thesis.degree.name	Master of Science (MSc)	en_US
ucalgary.item.requestcopy	true	en_US