Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.

Real-time Musical Pair Improvisation on Mobile Devices

This thesis discusses the design and implementation of a real-time musical pair improvisation scenario for mobile devices. In the scenario transferring musical information over a network connection was required. The suitability of available wireless communication technologies was evaluated and communication was analyzed and designed on multiple layers of TCP/IP protocol stack. Also an application layer protocol was designed and implemented for the scenario. The implementation was integrated into a mobile musical software for children using available software components and libraries although the used platform lead to hardware and software constraints. Software limitations were taken into account in design. The results show that real-time musical improvisation can be implemented with wireless communication and mobile technology. The results also show that link layer had the most significant effect on real-time communication in the scenario.

Horjahteleva johdatus neuromusikologiaan

Kirjallisuusarvostelu

Fredrik Pacius oli suurmies

Kirjallisuusarvostelu

Betoniporsas

1. Muna vai kana FIS9D1100003 2. Anna lantti makeisiin FIS9D1100004 3. Makaronilaulu FIS9D1100005 4. Hämähäkkimies raparperin alla FIS9D1100006 5. Mörön karkotus FIS9D1100007 6. Beduiinin kyynel FIS9D1100008 7. Unkka donkkaa FIS9D1100009 8. Vesipeto FIS9D1100010 9. Kaksi Ahtia FIS9D1100011 10. Sikabetoni-yhtyeen arki FIS9D1100012 11. Rämpätirää FIS9D1100013 12. Nuukan vaarin hammastauti FIS9D1100014 13. Teatterissa FIS9D1100015

"Väinön kannel" ja "Lauluiloa nuorisolle" : Kurkelan myytit ja oppositiot

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