Date of Award

Spring 5-31-2023

Document Type

Thesis (Undergraduate)


Computer Science

First Advisor

Michael Casey


A problem in guitar practice is choosing chord voicings that fit together in sequence, a process known as voice leading. In jazz, a guitarist follows voice leading by maintaining stepwise or limited motion for smoother harmony. The main avenues to learn jazz guitar voice leading theory are through a guitar instructor or chord books. To our knowledge, no computational method of generating voice-leading given chord labels exists. First, we demonstrate the complexity of this problem by presenting a graph search algorithm to optimize for a simplified version of voice leading. Then, we present a novel approach to algorithmically derive tablature sequences for a given chord progression with a sequence-to-sequence long short-term memory (LSTM) model. We present a new dataset consisting of guitar chord names and tablature from multiple professional jazz guitarists. We then boost this data by transposing it to all twelve diatonic keys. We tokenize into an alphabet of all seen chord labels and tablatures. With the boosted data and our alphabet, we train our LSTM model on chord sequences of length three with a mean reciprocal rank metric. In cross-validation, our model consistently ranked held-out ground-truth expert fingerings among the top predicted choices from hundreds of possible tablature sequences.

Data (73 kB)

Available for download on Tuesday, May 28, 2024