Leveraging User-Generated Metadata of Online Videos for Cover Song Identification
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913614189821952 |
|---|---|
| author | Hachmeier, Simon Jäschke, Robert |
| author_facet | Hachmeier, Simon Jäschke, Robert |
| contents | YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11818 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Leveraging User-Generated Metadata of Online Videos for Cover Song Identification Hachmeier, Simon Jäschke, Robert Multimedia Information Retrieval YouTube is a rich source of cover songs. Since the platform itself is organized in terms of videos rather than songs, the retrieval of covers is not trivial. The field of cover song identification addresses this problem and provides approaches that usually rely on audio content. However, including the user-generated video metadata available on YouTube promises improved identification results. In this paper, we propose a multi-modal approach for cover song identification on online video platforms. We combine the entity resolution models with audio-based approaches using a ranking model. Our findings implicate that leveraging user-generated metadata can stabilize cover song identification performance on YouTube. |
| title | Leveraging User-Generated Metadata of Online Videos for Cover Song Identification |
| topic | Multimedia Information Retrieval |
| url | https://arxiv.org/abs/2412.11818 |