Name
Abstract | ||
|---|---|---|
Abstract: At the present day the world wide web is full of music. Highly effective algorithms for music compression and high data storage has made it easy to access all kind of music easily. However, it is not possible to look for a similar piece of music or a sound as easily as to google for a similar kind of text. Music is filtered by its title or artist. Although musicians can publish their compositions in a second, they will only be found by high youtube ratings or by market basket analysis. Less known artists need much luck to get heard, although their music might just be what people want to hear. To approach this issue, we propose a new framework called MIRA (Music Information Retrieval Application) for analyzing audio files with existing Information Retrieval (IR) methods. Text retrieval has already yielded many highly efficient and generally accepted methods to assess the semantic distance of different text. We use these methods by translating music into equivalent audio words based on chroma features. We show that our framework can easily match music interpreted even by different artists. |
Year | Venue | Field |
|---|---|---|
2015 | BTW Workshops | Semantic similarity,Publication,Music information retrieval,Information retrieval,Musical,Computer science,Luck,Affinity analysis,Dynamic range compression,Text retrieval |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Englmeier | 1 | 0 | 0.68 |
| Nina Hubig | 2 | 13 | 4.64 |
| Sebastian Goebl | 3 | 37 | 4.02 |
| Christian Böhm | 4 | 2494 | 528.46 |