The work continues with comparative examinations of harmony in Fischer’s composition ‘The Early Years’, which shows parallels to passages from Shostakovich’s first symphony and Fischer’s solo piano interpretation of the folk song ‘Du, Du liegst mir im Herzen’, which reveals significant differences from the mainstream in tonal jazz as exemplified by Bill Evans’ harmonic treatment of the folk tune ‘Danny Boy’. The study discusses aspects of Fischer’s status and influence within the jazz community, his stylistic network, his work in the popular music sector, the concept of emotion in his music, and his relationship in socio-cultural and musical terms with Dimitri Shostakovich and Bill Evans. Various methodological and theoretical considerations are outlined and a pragmatic approach towards the examinations of both Fischer’s socio-cultural context and distinctive harmonic process proposed. The harmonic component includes detailed comparative analyses of one composition and one solo piano performance, which reveal aspects of influence and considerable detail regarding Fischer’s idiosyncratic harmonic style. It incorporates not only the results of interviews with Fischer himself, family members and long-time colleagues, but also introduces a large collection of private letters, used exclusively in this study by permission of Fischer’s wife Donna. This study presents socio-cultural and harmonic analyses of Clare Fischer’s compositional approach. This research further opens a path for research concerning chord progression generation for vocals, taking into account the extraction of words, emotional factor and the tune from actual voice of the user. Around 250 lead sheets are used to train this system using data driven and heuristic approach and the evaluation results represented 80% user satisfaction of the prototype. In order to embed the emotional factor, the Hidden Markov Model is dynamically created, and HMM properties are generated at run time according to the emotional factor and the input pitch classes (melody). The melody is taken as an audio file to the system where pitch class profile is created at run time representing the pitch content o the file over time. ChordATune uses a machine learning approach with Hidden Markov Model (HMM) along with dynamic programming to generate the chord progression for a given melody, embedding the emotional factor. Further, ChordATune provides a mechanism to arrange chords according to the genre, drum beats and tempo according to user preference. This paper discusses a solution for the tedious task of harmonization by introducing ChordATune, an interactive Machine Learning tool to harmonize melodies and generate chord progression according to user emotions. However, not all piano players and song writers are gifted with the musical talent to be able to harmonize piano melodies effectively because piano players need to keep track of extensive set of western music rules and concepts, years of training and practice and also some musicality within them to harmonize a melody accurately. Harmonization enriches piano melodies by adding variations such as mood, sound enhancements and beats that are the key building blocks of piano music.
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