Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning
Statistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and percepti...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2019.00070/full |
| _version_ | 1819064662232137728 |
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| author | Tatsuya Daikoku |
| author_facet | Tatsuya Daikoku |
| author_sort | Tatsuya Daikoku |
| collection | DOAJ |
| description | Statistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and perception. The present study investigated how statistical structure interacts with tonalities in music based on various-order statistical models. To verify this in all 24 major and minor keys, the transition probabilities of the sequences containing the highest pitches in Bach's Well-Tempered Clavier, which is a collection of two series (No. 1 and No. 2) of preludes and fugues in all of the 24 major and minor keys, were calculated based on nth-order Markov models. The transition probabilities of each sequence were compared among tonalities (major and minor), two series (No. 1 and No. 2), and music types (prelude and fugue). The differences in statistical characteristics between major and minor keys were detected in lower- but not higher-order models. The results also showed that statistical knowledge in music might be modulated by tonalities and composition periods. Furthermore, the principal component analysis detected the shared components of related keys, suggesting that the tonalities modulate statistical characteristics in music. The present study may suggest that there are at least two types of statistical knowledge in music that are interdependent on and independent of tonality, respectively. |
| first_indexed | 2024-12-21T15:34:08Z |
| format | Article |
| id | doaj.art-3995bf12339e42a5a9268471ecf5ad59 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T15:34:08Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-3995bf12339e42a5a9268471ecf5ad592022-12-21T18:58:40ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882019-10-011310.3389/fncom.2019.00070467122Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical LearningTatsuya DaikokuStatistical learning is a learning mechanism based on transition probability in sequences such as music and language. Recent computational and neurophysiological studies suggest that the statistical learning contributes to production, action, and musical creativity as well as prediction and perception. The present study investigated how statistical structure interacts with tonalities in music based on various-order statistical models. To verify this in all 24 major and minor keys, the transition probabilities of the sequences containing the highest pitches in Bach's Well-Tempered Clavier, which is a collection of two series (No. 1 and No. 2) of preludes and fugues in all of the 24 major and minor keys, were calculated based on nth-order Markov models. The transition probabilities of each sequence were compared among tonalities (major and minor), two series (No. 1 and No. 2), and music types (prelude and fugue). The differences in statistical characteristics between major and minor keys were detected in lower- but not higher-order models. The results also showed that statistical knowledge in music might be modulated by tonalities and composition periods. Furthermore, the principal component analysis detected the shared components of related keys, suggesting that the tonalities modulate statistical characteristics in music. The present study may suggest that there are at least two types of statistical knowledge in music that are interdependent on and independent of tonality, respectively.https://www.frontiersin.org/article/10.3389/fncom.2019.00070/fullcreativityMarkov modeln-graminformation theorycorpusprediction |
| spellingShingle | Tatsuya Daikoku Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning Frontiers in Computational Neuroscience creativity Markov model n-gram information theory corpus prediction |
| title | Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning |
| title_full | Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning |
| title_fullStr | Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning |
| title_full_unstemmed | Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning |
| title_short | Tonality Tunes the Statistical Characteristics in Music: Computational Approaches on Statistical Learning |
| title_sort | tonality tunes the statistical characteristics in music computational approaches on statistical learning |
| topic | creativity Markov model n-gram information theory corpus prediction |
| url | https://www.frontiersin.org/article/10.3389/fncom.2019.00070/full |
| work_keys_str_mv | AT tatsuyadaikoku tonalitytunesthestatisticalcharacteristicsinmusiccomputationalapproachesonstatisticallearning |