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 |