What song am I thinking of?

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Information Need (IN) is a complex phenomenon due to the difficulty experienced when realising and formulating it into a query format. This leads to a semantic gap between the IN and its representation (e.g., the query). Studies have investigated techniques to bridge this gap by using neurophysiological features. Music Information Retrieval (MIR) is a sub-field of IR that could greatly benefit from bridging the gap between IN and query, as songs present an acute challenge for IR systems. A searcher may be able to recall/imagine a piece of music they wish to search for but still need to remember key pieces of information (title, artist, lyrics) used to formulate a query that an IR system can process. Although, if a MIR system could understand the imagined song, it may allow the searcher to satisfy their IN better. As such, in this study, we aim to investigate the possibility of detecting pieces from Electroencephalogram (EEG) signals captured while participants “listen” to or “imagine” songs. We employ six machine learning models on the publicly available data set, OpenMIIR. In the model training phase, we devised several experiment scenarios to explore the capabilities of the models to determine the potential effectiveness of Perceived and Imagined EEG song data in a MIR system. Our results show that, firstly, we can detect perceived songs using the recorded brain signals, with an accuracy of 62.0% (SD 5.4%). Furthermore, we classified imagined songs with an accuracy of 60.8% (SD 13.2%). Insightful results were also gained from several experiment scenarios presented within this paper. Overall, the encouraging results produced by this study are a crucial step towards information retrieval systems capable of interpreting INs from the brain, which can help alleviate the semantic gap’s negative impact on information retrieval.
Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science
Subtitle of host publication9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, Renato Umeton
Place of PublicationCham
PublisherSpringer-Verlag
Pages418-432
Number of pages15
ISBN (Electronic)9783031539664
ISBN (Print)9783031539657
DOIs
Publication statusPublished - 15 Feb 2024
EventThird Symposium on Artificial Intelligence and Neuroscience - Grasmere, United Kingdom
Duration: 22 Sept 202326 Sept 2023

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14506
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThird Symposium on Artificial Intelligence and Neuroscience
Abbreviated titleACAIN 2023
Country/TerritoryUnited Kingdom
CityGrasmere
Period22/09/2326/09/23

Keywords

  • information systems
  • information retrieval
  • music retrieval
  • brain
  • machine learning

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