JOURNAL ARTICLE

Benefit of tonal context on relative pitch perception in musicians and non-musicians

Sara M. K. MadsenAndrew J. Oxenham

Year: 2020 Journal:   The Journal of the Acoustical Society of America Vol: 148 (4_Supplement)Pages: 2463-2463   Publisher: Acoustical Society of America

Abstract

The ability to access pitch differences between successive notes is essential for recognition of melodies and enables the detection of wrong tones in a familiar melody. The ability to compare pitch interval size is generally poor when the tones are presented without context but might improve if embedded in a tonal context. The present study measured pitch interval discrimination and aimed to determine (1) the effect of tonal context (novel or familiar melody), (2) the effect of standard interval size (2 and 9 semitones) and (3) the effect of musical training including ear training. Musicians and non-musicians between 18 and 30 years were tested. The musicians had played/sung for at least 5 years, had at least 1 semester of ear training, and still played/sung. The non-musicians had played for maximum 5 years, never had ear training lessons, and did not currently play/sing. The preliminary results show a benefit of tonal context for context presented before but not after the target interval. The benefit of the musicians is similar for the familiar and novel melodies whereas the performance of the non-musicians is markedly better for familiar compared to novel melodies. Performance is generally better for the smaller standard interval.

Keywords:
Melody Context (archaeology) Interval (graph theory) Semitone Absolute pitch Relative pitch Perception Acoustics Speech recognition Pitch (Music) Audiology Musical Computer science Psychology Mathematics History Art Visual arts Physics Medicine

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Topics

Music and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Neuroscience and Music Perception
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Music Technology and Sound Studies
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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