Sight-reading is the act of performing a piece of music at first sight. This can be a difficult task to master, because it requires extensive knowledge of music theory, practice, quick thinking, and most importantly, a wide variety of musical material. A musician can only effectively sight-read with a new piece of music. This not only requires many resources, but also musical pieces that are challenging while also within a player's abilities.
This thesis presents PiaNote, a sight-reading web application for pianists that algorithmically generates music based on human performance. PiaNote's goal is to alleviate some of the hassles pianists face when sight-reading. PiaNote presents musicians with algorithmically generated pieces, ensuring that a musician never sees the same piece of music twice. PiaNote also monitors player performances in order to intelligently present music that is challenging, but within the player's abilities. As a result, PiaNote offers a sight-reading experience that is tailored to the player.
On a broader level, this thesis explores different methods in effectively creating a sight-reading application. We evaluate PiaNote with a user study involving novice piano players. The players actively practice with PiaNote over three fifteen-minute sessions. At the end of the study, users are asked to determine whether PiaNote is an effective practice tool that improves both their confidence in sight-reading and their sight-reading abilities. Results suggest that PiaNote does improve user's sight-reading confidence and abilities, but further research must be conducted to clearly validate PiaNote's effectiveness. We conclude that PiaNote has potential to become an effective sight-reading application with slight improvements and further research.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-2765 |
| Date | 01 June 2016 |
| Creators | Schulz, Drew |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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