Detecting Advertising in Radio using Machine Learning
| dc.contributor.author | M¨uller-Cajar, Robin | |
| dc.date.accessioned | 2017-12-05T03:01:20Z | |
| dc.date.available | 2017-12-05T03:01:20Z | |
| dc.date.issued | 2007 | en |
| dc.description.abstract | We present an algorithm that can distinguish between advertising and music without understanding it, by extracting key attributes from a radio audio stream. Our method combines advanced filtering of an audio stream with machine learning algorithms to recognise the filtered variables. The result is lightweight enough to run on an embedded processor, and could thus be used to create a device that gives the listener the ability to filter advertising from radio broadcasts | en |
| dc.identifier.uri | http://hdl.handle.net/10092/14836 | |
| dc.identifier.uri | http://dx.doi.org/10.26021/1546 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.publisher | University of Canterbury | en |
| dc.rights | All Right Reserved | en |
| dc.rights.uri | https://canterbury.libguides.com/rights/theses | en |
| dc.title | Detecting Advertising in Radio using Machine Learning | en |
| dc.type | Theses / Dissertations | en |
| thesis.degree.grantor | University of Canterbury | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Other | en |
| uc.college | Faculty of Engineering | en |