Please use this identifier to cite or link to this item:
http://arks.princeton.edu/ark:/88435/dsp01nv935527tFull metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Hazan, Elad | - |
| dc.contributor.author | Crawford, Christopher | - |
| dc.date.accessioned | 2016-06-22T15:12:23Z | - |
| dc.date.available | 2016-06-22T15:12:23Z | - |
| dc.date.created | 2016-04-29 | - |
| dc.date.issued | 2016-06-22 | - |
| dc.identifier.uri | http://arks.princeton.edu/ark:/88435/dsp01nv935527t | - |
| dc.description.abstract | Musical Key-detection is a sub-problem of the growing field of Music Information Retrieval. Key-detection and other technologies offered by MIR have increasing business and consumer applications given the rise of automated streaming music services that attempt to recommend music and generate playlists. This research offers several suggestions for the direction of improving key-detection and highlights specific extractable audio-features that improve detection. | en_US |
| dc.format.extent | 60 pages | * |
| dc.language.iso | en_US | en_US |
| dc.title | Machine Learning Approach to Musical Key Detection | en_US |
| dc.type | Princeton University Senior Theses | - |
| pu.date.classyear | 2016 | en_US |
| pu.department | Computer Science | en_US |
| pu.pdf.coverpage | SeniorThesisCoverPage | - |
| Appears in Collections: | Computer Science, 1988-2020 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| Crawford_Christopher_thesis.pdf | 1.31 MB | Adobe PDF | Request a copy |
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