Name
Abstract | ||
|---|---|---|
The rise of digital music has led to a parallel rise in the need to manage music collections of several thousands of songs on a single device. Manual selection of songs for a music listening experience is a cumbersome task. In this paper, we present an initial exploration of the feasibility of using song signal properties and user context information to assist in automatic song selection. Users listened to music over the course of a month while their context and song selections were tracked. Initial results suggest the use of context information can improve automated song selection when patterns are learned for each individual. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1459359.1459516 | ACM Multimedia 2001 |
Keywords | Field | DocType |
user context information,automated song selection,song selection,context information,manual selection,digital music,automatic song selection,music collection,song signal property,initial exploration,music,context model,prediction,modeling,sensors | Computer science,Active listening,Digital audio,Multimedia | Conference |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Olufisayo Omojokun | 1 | 44 | 5.03 |
| Michael Genovese | 2 | 3 | 0.77 |
| Charles L. Isbell | 3 | 504 | 65.79 |