Towards automatic music recommendation for audio branding scenarios

Martin  Herzog; Steffen Lepa; Hauke Wolfgang Egermann

Towards automatic music recommendation for audio branding scenarios

Martin Herzog, Steffen Lepa, Hauke Wolfgang Egermann

Music

Research output: Contribution to conference › Paper › peer-review

Abstract

Within the MIR community, most prediction models of musical impact on listeners focus on mood or emotional effects (perceived or induced).
The ABC_DJ project investigates the associative impact of music on listeners from the specific perspective of music branding that surrounds us in our everyday lives. We pre- sent a general concept for applying automatic music rec- ommendation within this domain. Creating a scientifically validated basic terminology for communicating brand at- tributes and human emotions in this field is the key chal- lenge.
As a first result, we introduce the Music Branding Expert Terminology (MBET), a comprehensive terminology of verbal attributes used in music branding, upon which a pre- diction model will be developed to facilitate automatic mu- sic recommendation in the context of music branding.

Original language	English
Number of pages	3
Publication status	Published - 2016
Event	17th International Society for Music Information Retrieval Conference, - New York, United States Duration: 7 Aug 2016 → 11 Aug 2016

Conference

Conference	17th International Society for Music Information Retrieval Conference,
Abbreviated title	ISMIR
Country/Territory	United States
City	New York
Period	7/08/16 → 11/08/16

Access to Document

https://github.com/ismir/ismir2016-lbd/blob/master/submissions/herzog-towards.pdfLicence: CC BY

ABC_DJ: Artist-to-Business-to-Business-to-Consumer Audio Branding System
Egermann, H.
1/01/16 → 31/12/18
Project: Research project (funded) › Research

Cite this

@conference{45823e823c024a049ba71d22edf37006,

title = "Towards automatic music recommendation for audio branding scenarios",

abstract = "Within the MIR community, most prediction models of musical impact on listeners focus on mood or emotional effects (perceived or induced).The ABC_DJ project investigates the associative impact of music on listeners from the specific perspective of music branding that surrounds us in our everyday lives. We pre- sent a general concept for applying automatic music rec- ommendation within this domain. Creating a scientifically validated basic terminology for communicating brand at- tributes and human emotions in this field is the key chal- lenge.As a first result, we introduce the Music Branding Expert Terminology (MBET), a comprehensive terminology of verbal attributes used in music branding, upon which a pre- diction model will be developed to facilitate automatic mu- sic recommendation in the context of music branding.",

author = "Martin Herzog and Steffen Lepa and Egermann, {Hauke Wolfgang}",

year = "2016",

language = "English",

note = " 17th International Society for Music Information Retrieval Conference,, ISMIR ; Conference date: 07-08-2016 Through 11-08-2016",

}

TY - CONF

T1 - Towards automatic music recommendation for audio branding scenarios

AU - Herzog, Martin

AU - Lepa, Steffen

AU - Egermann, Hauke Wolfgang

PY - 2016

Y1 - 2016

N2 - Within the MIR community, most prediction models of musical impact on listeners focus on mood or emotional effects (perceived or induced).The ABC_DJ project investigates the associative impact of music on listeners from the specific perspective of music branding that surrounds us in our everyday lives. We pre- sent a general concept for applying automatic music rec- ommendation within this domain. Creating a scientifically validated basic terminology for communicating brand at- tributes and human emotions in this field is the key chal- lenge.As a first result, we introduce the Music Branding Expert Terminology (MBET), a comprehensive terminology of verbal attributes used in music branding, upon which a pre- diction model will be developed to facilitate automatic mu- sic recommendation in the context of music branding.

AB - Within the MIR community, most prediction models of musical impact on listeners focus on mood or emotional effects (perceived or induced).The ABC_DJ project investigates the associative impact of music on listeners from the specific perspective of music branding that surrounds us in our everyday lives. We pre- sent a general concept for applying automatic music rec- ommendation within this domain. Creating a scientifically validated basic terminology for communicating brand at- tributes and human emotions in this field is the key chal- lenge.As a first result, we introduce the Music Branding Expert Terminology (MBET), a comprehensive terminology of verbal attributes used in music branding, upon which a pre- diction model will be developed to facilitate automatic mu- sic recommendation in the context of music branding.

M3 - Paper

T2 - 17th International Society for Music Information Retrieval Conference,

Y2 - 7 August 2016 through 11 August 2016

ER -

Towards automatic music recommendation for audio branding scenarios

Abstract

Conference

Access to Document

Projects

ABC_DJ: Artist-to-Business-to-Business-to-Consumer Audio Branding System

Cite this