Documents

DOI

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning.
Original languageEnglish
Title of host publicationWWW'18 Companion Proceedings of the The Web Conference 2018
Place of PublicationRepublic and Canton of Geneva, Switzerland
PublisherInternational World Wide Web Conferences Steering Committee
Pages1929-1934
Number of pages6
ISBN (Print)978-1-4503-5640-4
DOIs
Publication statusPublished - 2018
EventWWW 2018: The Web Conference - Bridging natural and artificial intelligence worldwide - Lyon, France
Duration: 23 Apr 201827 Apr 2018
https://www2018.thewebconf.org

Conference

ConferenceWWW 2018
Abbreviated titleWWW 2018
CountryFrance
CityLyon
Period23/04/1827/04/18
Internet address

    Research areas

  • music information retrieval, multi-task learning, transfer learning, neural network

ID: 45678209