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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2412.10481 |
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| _version_ | 1866929629827170304 |
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| author | Naik, Canishk Chew, Elaine |
| author_facet | Naik, Canishk Chew, Elaine |
| contents | Tipping points are moments of change that characterise crucial turning points in a piece of music. This study presents a first step towards quantitatively and systematically describing the musical properties of tipping points. Timing information and computationally-derived tonal tension values which correspond to dissonance, distance from key, and harmonic motion are compared to tipping points in Ashkenazy's recordings of six Chopin Mazurkas, as identified by 35 listeners. The analysis shows that all popular tipping points but one could be explained by statistically significant timing deviations or changepoints in at least one of the three tension parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10481 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tipping Points, Pulse Elasticity and Tonal Tension: An Empirical Study on What Generates Tipping Points Naik, Canishk Chew, Elaine Sound Artificial Intelligence Human-Computer Interaction Audio and Speech Processing Tipping points are moments of change that characterise crucial turning points in a piece of music. This study presents a first step towards quantitatively and systematically describing the musical properties of tipping points. Timing information and computationally-derived tonal tension values which correspond to dissonance, distance from key, and harmonic motion are compared to tipping points in Ashkenazy's recordings of six Chopin Mazurkas, as identified by 35 listeners. The analysis shows that all popular tipping points but one could be explained by statistically significant timing deviations or changepoints in at least one of the three tension parameters. |
| title | Tipping Points, Pulse Elasticity and Tonal Tension: An Empirical Study on What Generates Tipping Points |
| topic | Sound Artificial Intelligence Human-Computer Interaction Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.10481 |