Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Lernen, Wissen, Daten, Analysen 2022 |
| Untertitel | Proceedings of the LWDA 2022 Workshops: FGWM, FGKD, and FGDB |
| Seiten | 213-226 |
| Seitenumfang | 14 |
| Publikationsstatus | Veröffentlicht - 11 Feb. 2023 |
| Veranstaltung | LWDA 2022 Workshops: FGWM, FGKD, and FGDB - Hildesheim, Deutschland Dauer: 5 Okt. 2022 → 7 Okt. 2022 |
Publikationsreihe
| Name | CEUR Workshop Proceedings |
|---|---|
| Herausgeber (Verlag) | CEUR Workshop Proceedings |
| Band | 3341 |
| ISSN (Print) | 1613-0073 |
Abstract
Various versions of musical works are published on YouTube, such as remixes or reaction videos. While some research has focused on tasks like audio-based version identification of these videos, it is still unclear how to effectively retrieve a large amount of relevant versions with textual queries. In this paper, we formulate search queries with YouTube search suggestions, evaluate these based on multiple dimensions and compute optimal ranks of queries on work-level. We show that queries containing the artist string retrieve results with higher relevance, but have higher overlaps. Additionally, we demonstrate that the amount of reasonable queries can be increased by applying frequently suggested expansions to works which tend to contextualize queries to the music domain.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Lernen, Wissen, Daten, Analysen 2022: Proceedings of the LWDA 2022 Workshops: FGWM, FGKD, and FGDB. 2023. S. 213-226 (CEUR Workshop Proceedings; Band 3341).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Music Version Retrieval from YouTube
T2 - LWDA 2022 Workshops: FGWM, FGKD, and FGDB
AU - Hachmeier, Simon
AU - Jäschke, Robert
AU - Saadatdoorabi, Hadi
PY - 2023/2/11
Y1 - 2023/2/11
N2 - Various versions of musical works are published on YouTube, such as remixes or reaction videos. While some research has focused on tasks like audio-based version identification of these videos, it is still unclear how to effectively retrieve a large amount of relevant versions with textual queries. In this paper, we formulate search queries with YouTube search suggestions, evaluate these based on multiple dimensions and compute optimal ranks of queries on work-level. We show that queries containing the artist string retrieve results with higher relevance, but have higher overlaps. Additionally, we demonstrate that the amount of reasonable queries can be increased by applying frequently suggested expansions to works which tend to contextualize queries to the music domain.
AB - Various versions of musical works are published on YouTube, such as remixes or reaction videos. While some research has focused on tasks like audio-based version identification of these videos, it is still unclear how to effectively retrieve a large amount of relevant versions with textual queries. In this paper, we formulate search queries with YouTube search suggestions, evaluate these based on multiple dimensions and compute optimal ranks of queries on work-level. We show that queries containing the artist string retrieve results with higher relevance, but have higher overlaps. Additionally, we demonstrate that the amount of reasonable queries can be increased by applying frequently suggested expansions to works which tend to contextualize queries to the music domain.
KW - audio based version identification
KW - music on youtube
KW - query formulation
UR - http://www.scopus.com/inward/record.url?scp=85148603357&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85148603357
T3 - CEUR Workshop Proceedings
SP - 213
EP - 226
BT - Lernen, Wissen, Daten, Analysen 2022
Y2 - 5 October 2022 through 7 October 2022
ER -