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            <title xml:lang="en">Modelling Cadence Perception Via Musical Parameter Tuning to Perceptual Data</title>
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            <idno type="halRefHtml">&lt;i&gt;12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)&lt;/i&gt;, Sep 2016, Thessaloniki, Greece. pp.552-561, &lt;a target="_blank" href="https://dx.doi.org/10.1007/978-3-319-44944-9_49"&gt;&amp;#x27E8;10.1007/978-3-319-44944-9_49&amp;#x27E9;&lt;/a&gt;</idno>
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                <title xml:lang="en">Modelling Cadence Perception Via Musical Parameter Tuning to Perceptual Data</title>
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                    <forename type="first">Maximos</forename>
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                  <title>12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)</title>
                  <date type="start">2016-09-16</date>
                  <date type="end">2016-09-18</date>
                  <settlement>Thessaloniki</settlement>
                  <country key="GR">Greece</country>
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                <editor>Lazaros Iliadis</editor>
                <editor>Ilias Maglogiannis</editor>
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              <p>Conceptual blending when used as a creative tool combines the features of two input spaces, generating new blended spaces that share the common structure of the inputs, as well as different combinations of their non-common parts. In the case of music, conceptual blending has been employed creatively, among others, in generating new cadences (pairs of chords that conclude musical phrases). Given a specific set of input cadences together with their blends, this paper addresses the following question: are some musical features of cadences more salient than others in defining perceived relations between input and blended cadences? To this end, behavioural data from a pairwise dissimilarity listening test using input and blended cadences as stimuli were collected, thus allowing the construction of a ‘ground-truth’ human-based perceptual space of cadences. Afterwards, the salience of each cadence feature was adjusted through the Differential Evolution (DE) algorithm, providing a system-perceived space of cadences that optimally matched the ground-truth space. Results in a specific example of cadence blending indicated that some features were distinguishably more salient than others. This pilot study was a first step towards building self-aware blending systems and revealed that the salience of features in conceptual blending is an essential part for producing perceptually relevant blends.</p>
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