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An Intelligent Evaluation Algorithm for the Matching Degree of Music Lyrics Based on LabVIEW Digital Image

Published:22 November 2022Publication History

ABSTRACT

The emotion matching model is a method suitable for evaluating the matching degree of words and songs, but only through the emotion matching model cannot make a correct evaluation for evaluating the matching degree of words and songs. In order to improve this problem, this paper proposes an intelligent evaluation algorithm for the matching degree of lyrics and songs based on LabVIEW digital images. On the basis of analyzing the relationship between LabVIEW digital images and music melody, emotional feature analysis technology and algorithm description, the MagnaTagATune data set and the MusiClef data set are selected. And the 300 music in the MirexMood dataset to verify the model matching effect through experiments. The experimental results show that the calculation model can reasonably evaluate the emotional content of music lyrics, and make accurate and intelligent evaluation of music.

References

  1. Sharp A, Bacon B A, Champoux F. Enhanced tactile identification of musical emotion in the deaf. Experimental Brain Research, 2020, 238(4): 1-8.Google ScholarGoogle Scholar
  2. Manno F, Lau C, J Fernandez-Ruiz, The human amygdala disconnecting from auditory cortex preferentially discriminates musical sound of uncertain emotion by altering hemispheric weighting. Scientific Reports, 2019, 9(1): 1-18.Google ScholarGoogle ScholarCross RefCross Ref
  3. Rosenberg S, Reardon-Smith H. OF BODY, OF EMOTION: A TOOLKIT FOR TRANSFORMATIVE SOUND USE. Breast Cancer Online, 2020, 74(292): 64-73.Google ScholarGoogle Scholar
  4. Shorner-Johnson. Music and the Sin of Sloth: The Gendered Articulation of Worthy Musical Time in Early American Music. Philosophy of Music Education Review, 2019, 27(1): 51.Google ScholarGoogle ScholarCross RefCross Ref
  5. Panwar S, Rad P, Choo K, Are you emotional or depressed? Learning about your emotional state from your music using machine learning. Journal of Supercomputing, 2019, 75(6): 2986-3009.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. You S, Sun L, Li X, Contextual prediction modulates musical tension: Evidence from behavioral and neural responses. Brain and Cognition, 2021, 152(1): 105771.Google ScholarGoogle ScholarCross RefCross Ref
  7. Chen H B, Wu D D, He J, Emotion Recognition in Patients with Parkinson Disease. Cognitive and Behavioral Neurology, 2019, 32(4): 247-255.Google ScholarGoogle ScholarCross RefCross Ref
  8. Zhou L, Liu F, Jiang J, Impaired emotional processing of chords in congenital amusia: Electrophysiological and behavioral evidence. Brain and cognition, 2019, 135(Oct.):103577.1-103577.11.Google ScholarGoogle Scholar
  9. Rutherford S. "Loud and Open Speaking in 'the People's' Mighty Name": Eliza Cook, Music and Politics. Journal of British Studies, 2021, 60(2): 416-429.Google ScholarGoogle ScholarCross RefCross Ref
  10. Wei W L, Lin J C, Liu T L, Learning To Visualize Music Through Shot Sequence For Automatic Concert Video Mashup. IEEE Transactions on Multimedia, 2020, (99): 1-1.Google ScholarGoogle Scholar
  11. Campbell S. 'Agitate, educate, organise': partisanship, popular music and the Northern Ireland conflict. Popular Music, 2020, 39(2): 233-256.Google ScholarGoogle ScholarCross RefCross Ref
  12. Varni G, Mancini M, Fadiga L, The change matters! Measuring the effect of changing the leader in joint music performances. IEEE Transactions on Affective Computing, 2019, (99): 1-1.Google ScholarGoogle Scholar

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  1. An Intelligent Evaluation Algorithm for the Matching Degree of Music Lyrics Based on LabVIEW Digital Image

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    • Published in

      cover image ACM Other conferences
      ICGSP '22: Proceedings of the 6th International Conference on Graphics and Signal Processing
      July 2022
      91 pages
      ISBN:9781450396370
      DOI:10.1145/3561518

      Copyright © 2022 ACM

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      Publication History

      • Published: 22 November 2022

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