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Generating a chord progression from a monophonic melody is a challengingproblem because a chord progression requires a series of layered notes playedsimultaneously. This paper presents a novel method of generating chordsequences from a symbolic melody using bidirectional long short-term memory(BLSTM) networks trained on a lead sheet database. To this end, a group offeature vectors composed of 12 semitones is extracted from the notes in eachbar of monophonic melodies. In order to ensure that the data shares uniform keyand duration characteristics, the key and the time signatures of the vectorsare normalized. The BLSTM networks then learn from the data to incorporate thetemporal dependencies to produce a chord progression. Both quantitative andqualitative evaluations are conducted by comparing the proposed method with theconventional HMM and DNN-HMM based approaches. Proposed model achieves 23.8%and 11.4% performance increase from the other models, respectively. Userstudies further confirm that the chord sequences generated by the proposedmethod are preferred by listeners.

Chord Generation from Symbolic Melody Using BLSTM Networks