A robust audio-based symbol recognition system using machine learning techniques
| dc.contributor.advisor | Ghaziasgar, Mehrdad | |
| dc.contributor.advisor | Connan, James | |
| dc.contributor.advisor | Dodds, Reg | |
| dc.contributor.author | Wu, Qiming | |
| dc.date.accessioned | 2020-12-02T09:38:43Z | |
| dc.date.accessioned | 2024-10-30T14:00:59Z | |
| dc.date.available | 2020-12-02T09:38:43Z | |
| dc.date.available | 2024-10-30T14:00:59Z | |
| dc.date.issued | 2020-02 | |
| dc.description | Masters of Science | en_US |
| dc.description.abstract | This research investigates the creation of an audio-shape recognition system that is able to interpret a user’s drawn audio shapes—fundamental shapes, digits and/or letters— on a given surface such as a table-top using a generic stylus such as the back of a pen. The system aims to make use of one, two or three Piezo microphones, as required, to capture the sound of the audio gestures, and a combination of the Mel-Frequency Cepstral Coefficients (MFCC) feature descriptor and Support Vector Machines (SVMs) to recognise audio shapes. The novelty of the system is in the use of piezo microphones which are low cost, light-weight and portable, and the main investigation is around determining whether these microphones are able to provide sufficiently rich information to recognise the audio shapes mentioned in such a framework. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10566/16989 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of the Western Cape | en_US |
| dc.rights.holder | University of the Western Cape | en_US |
| dc.subject | Audio shape | en_US |
| dc.subject | Recognition system | en_US |
| dc.subject | Piezo microphones | en_US |
| dc.subject | Support vector machines | en_US |
| dc.subject | Mel-frequency cepstral coefficients | en_US |
| dc.title | A robust audio-based symbol recognition system using machine learning techniques | en_US |