KaraMIR: A project for cover song identification and singing voice analysis using a karaoke songs dataset

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10378483" target="_blank" >RIV/00216208:11320/18:10378483 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/18:10240408

Výsledek na webu

<a href="https://www.worldscientific.com/doi/abs/10.1142/S1793351X18400202" target="_blank" >https://www.worldscientific.com/doi/abs/10.1142/S1793351X18400202</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S1793351X18400202" target="_blank" >10.1142/S1793351X18400202</a>

Jazyk výsledku

angličtina

Název v původním jazyce

KaraMIR: A project for cover song identification and singing voice analysis using a karaoke songs dataset

Popis výsledku v původním jazyce

We introduce KaraMIR, a musical project dedicated to karaoke song analysis. Within KaraMIR we define Kara1k, a dataset composed of 1,000 cover songs provided by Recisio Karafun application, and the corresponding 1,000 songs by the original artists. Kara1k is mainly dedicated toward cover song identification and singing voice analysis. For both tasks, Kara1k offers novel approaches, as each cover song is a studio-recorded song with the same arrangement as the original recording, but with different singers and musicians. Essentia, harmony-analyser, Marsyas, Vamp plugins and YAAFE have been used to extract audio features for each track in Kara1k. We provide metadata such as the title, genre, original artist, year, International Standard Recording Code and the ground truths for the singer&apos;s gender, backing vocals, duets, and lyrics&apos; language. KaraMIR project focuses on defining new problems and describing features and tools to solve them. We thus provide a comparison of traditional and new features for a cover song identification task using statistical methods, as well as the dynamic time warping method on chroma, MFCC, chords, keys, and chord distance features. A supporting experiment on the singer gender classification task is also proposed. The KaraMIR project website facilitates the continuous research.

Název v anglickém jazyce

KaraMIR: A project for cover song identification and singing voice analysis using a karaoke songs dataset

Popis výsledku anglicky

We introduce KaraMIR, a musical project dedicated to karaoke song analysis. Within KaraMIR we define Kara1k, a dataset composed of 1,000 cover songs provided by Recisio Karafun application, and the corresponding 1,000 songs by the original artists. Kara1k is mainly dedicated toward cover song identification and singing voice analysis. For both tasks, Kara1k offers novel approaches, as each cover song is a studio-recorded song with the same arrangement as the original recording, but with different singers and musicians. Essentia, harmony-analyser, Marsyas, Vamp plugins and YAAFE have been used to extract audio features for each track in Kara1k. We provide metadata such as the title, genre, original artist, year, International Standard Recording Code and the ground truths for the singer&apos;s gender, backing vocals, duets, and lyrics&apos; language. KaraMIR project focuses on defining new problems and describing features and tools to solve them. We thus provide a comparison of traditional and new features for a cover song identification task using statistical methods, as well as the dynamic time warping method on chroma, MFCC, chords, keys, and chord distance features. A supporting experiment on the singer gender classification task is also proposed. The KaraMIR project website facilitates the continuous research.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Semantic Computing

ISSN

1793-351X

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

22

Strana od-do

501-522

Kód UT WoS článku

000453524500003

EID výsledku v databázi Scopus

2-s2.0-85058802251