Chord Generation from Symbolic Melody Using BLSTM Networks
This addresses the challenge of automated music composition for musicians and AI applications, representing an incremental improvement over existing methods.
The paper tackled the problem of generating chord progressions from monophonic melodies by proposing a novel method using bidirectional long short-term memory (BLSTM) networks, achieving performance increases of 23.8% and 11.4% over conventional HMM and DNN-HMM approaches, with user studies confirming listener preference.
Generating a chord progression from a monophonic melody is a challenging problem because a chord progression requires a series of layered notes played simultaneously. This paper presents a novel method of generating chord sequences from a symbolic melody using bidirectional long short-term memory (BLSTM) networks trained on a lead sheet database. To this end, a group of feature vectors composed of 12 semitones is extracted from the notes in each bar of monophonic melodies. In order to ensure that the data shares uniform key and duration characteristics, the key and the time signatures of the vectors are normalized. The BLSTM networks then learn from the data to incorporate the temporal dependencies to produce a chord progression. Both quantitative and qualitative evaluations are conducted by comparing the proposed method with the conventional HMM and DNN-HMM based approaches. Proposed model achieves 23.8% and 11.4% performance increase from the other models, respectively. User studies further confirm that the chord sequences generated by the proposed method are preferred by listeners.