It first extracts two types of meta-information from the lead sheet, namely the time signature and the key signature, and uses them to create the beat sequence b 1 : T and to transpose the melody to C-major/A-minor, respectively. 1 shows how AutoHarmonizer generates a chord sequence based on a given melody. In this paper, we proposed a harmonic rhythm-controllable melody harmonization system, AutoHarmonizer, that consists of a harmonic rhythm model and a chord model. Sheet Dataset, which is the largest lead sheet dataset to date. With 40,925 traditional Irish folk songs with harmonies, named the Session Lead To harmonize the Session Dataset (which were originally chordless), and ended Quality than baseline at different settings. ![]() Rhythms comparable to the human level, but generates chords with overall better Progressions from humans, the system proposed in this paper and the baseline.Įxperimental results show that AutoHarmonizer not only generates harmonic The performance of AutoHarmonizer, we use nine metrics to compare the chord The corresponding harmonic rhythm sequence previously generated. Harmonic rhythm model provides coarse-grained chord onset information, while aĬhord model generates specific pitches for chords based on the given melody and This system mainly consists of two parts: a Network-based melody harmonization system that can generate denser or sparserĬhord progressions with the use of a new sampling method for controllable Rhythm-controllable melody harmonization, we propose AutoHarmonizer, a neural Of them can generate flexible harmonic rhythms. ![]() Network-based systems can effectively generate an appropriate chord progressionįor a melody, few studies focus on controllable melody harmonization, and none ![]() Melody, remains a challenging task to this day. Melody harmonization, namely generating a chord progression for a user-given
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