Name
Abstract | ||
|---|---|---|
We describe a method of estimating subjective music similarity from acoustic music similarity. Recently, there have been many studies on the topic of music information retrieval, but there continues to be difficulty improving retrieval precision. For this reason, in this study we analyze the individuality of subjective music similarity. We collected subjective music similarity evaluation data for individuality analysis using songs in the RWC music database, a widely used database in the field of music information processing. A total of 27 subjects listened to pairs of music tracks, and evaluated each pair as similar or dissimilar. They also selected the components of the music (melody, tempo/rhythm, vocals, instruments) that were similar. Each subject evaluated the same 200 pairs of songs, thus the individuality of the evaluation can be easily analyzed. Using the collected data, we trained individualized distance functions between songs, in order to estimate subjective similarity and analyze individuality. |
Year | Venue | Keywords |
|---|---|---|
2012 | Signal & Information Processing Association Annual Summit and Conference | acoustic signal detection,acoustic signal processing,information retrieval,music,RWC music database,acoustic music similarity,data collection,individuality analysis,music information retrieval,subjective music similarity evaluation data |
DocType | ISSN | ISBN |
Conference | 2309-9402 | 978-1-4673-4863-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shota Kawabuchi | 1 | 0 | 0.34 |
| Chiyomi Miyajima | 2 | 345 | 45.71 |
| Norihide Kitaoka | 3 | 277 | 43.70 |
| Kazuya Takeda | 4 | 1301 | 195.60 |