Generative Melody Composition with
Human-in-the-Loop Bayesian Optimization
Yijun Zhou1 , Yuki Koyama2 , Masataka Goto2 , and Takeo Igarashi1?
2

1
The University of Tokyo
National Institute of Advanced Industrial Science and Technology (AIST)

Abstract. Deep generative models allow even novice composers to generate various melodies by sampling latent vectors. However, finding thedesired melody is challenging since the latent space is unintuitive andhigh-dimensional. In this work, we present an interactive system that supports generative melody composition with human-in-the-loop Bayesian
optimization (BO). This system takes a mixed-initiative approach; thesystem generates candidate melodies to evaluate, and the user evaluatesthem and provides preferential feedback (i.e., picking the best melody
among the candidates) to the system. This process is iteratively performed based on BO techniques until the user finds the desired melody.
We conducted a pilot study using our prototype system, suggesting thepotential of this approach.
Keywords: Melody composition; deep generative model; Bayesian optimization

1

Introduction

Deep generative models, such as generative adversarial networks (GANs)(Goodfellow et al., 2014) and variational auto-encoders (VAEs) (Kingma &Welling, 2014), have been applied to support creative work, showing great
potentials in images, videos, and music (Vondrick et al., 2016; Zhu et al., 2016;
Dong et al., 2018). When we focus our scope on the music domain, many research
projects have tried to learn latent spaces of melodies (Yang et al., 2017; Fernández& Vico, 2013; Simões et al., 2019; Herremans et al., 2017). By sampling latentvectors from the smooth latent space produced by one of these generative melody
models, even novice composers can quickly generate various meaningful melodies(Ghosh et al., 2019; Roberts, Engel, et al., 2018).
However, end-users may face significant challenges when exploring the latent
space to find a desired melody, since the latent space is often unintuitive andhigh-dimensional. Researchers have developed many interaction techniques (e.g.,
mixing two melodies) to assist users in exploring the latent space to achieve
their creative goals in music (Vogl & Knees, 2016; Roberts, Hawthorne, & Simon,?

This work was supported in part by JST CREST Grant Number JPMJCR17A1,
Japan, and JST ACCEL Grant Number JPMJAC1602, Japan.

2

Yijun Zhou, Yuki Koyama, Masataka Goto, and Takeo Igarashi2018). These techniques have shown opportunities to enhance the user experienceof music composition.
In this work, we propose a new approach to facilitate generative melody composition for novices, which is based on human-in-the-loop Bayesian optimization(BO) techniques (Brochu et al., 2007; Koyama et al., 2017, 2020). Specifically, we
present a melody-composition system that employs a human-AI mixed-initiativeinteraction: the system generates candidate melodies to evaluate, and the user
evaluates them and provides preferential feedback (i.e., picking the best melody
among the observed candidates) to the system. This process is iteratively performed until the user finds the desired melody. The generation of candidate
melodies by the system is based on BO, which tries to make the query to theuser as effective in terms of the number of necessary iterations as possible.
We evaluated our approach through both a simulated experiment and apilot user study, suggesting the potential of this approach. To the best of ourknowledge, this work is the first to investigate the potential of human-in-the-loop
BO in the context of generative melody composition and AI music creativity.

2

Related Work

2.1

Human-in-the-Loop Bayesian Optimization

BO is a black-box optimization technique (Shahriari et al., 2016). It is especially
useful when handling expensive-to-query objective functions since it tries tominimize the number of necessary iterations by selecting the most effective queryin each iteration. Thus, it is widely used for, for example, hyperparameter tuningof deep learning models (Akiba et al., 2019).
Human’s preference is expensive to query. Thus, a search problem with anobjective function defined by human perceptual assessment (e.g., preference)
should be solved with a minimal number of queries. This motivates researchers
to develop human-in-the-loop BO methods (Brochu et al., 2007; Koyama et al.,2017, 2020; Chong et al., 2019). As preferential assessment needs to be “relative”(e.g., which option is the better?) rather than “absolute” (e.g., how good is thisoption?) (Brochu et al., 2010; Tsukida & Gupta, 2011), these methods takerelative preferential feedback as input, unlike typical BO algorithms. Thus, thisapproach is also called as preferential BO (PBO). The PBO method proposedby Brochu et al. (2007) takes pairwise-comparison results as input, which isthe simplest form of preferential feedback. To make the necessary number ofqueries even smaller, the methods proposed by Koyama et al. (2017, 2020) takesubspace-search results as input, where human is iteratively asked to find thebest possible option from a one- or two-dimensional search subspaces.
In this work, we apply this PBO3 approach to generative melody composition.
Our system iteratively asks the user to compare multiple melody candidates
sampled from a one-dimensional subspace (Koyama et al., 2017) and to selectthe preferred one. In addition, we allow the user to directly edit the selected one
to provide more controllability to the user.
3

In the following sections, we simply call it BO when no confusion can arise.

Generative Melody Composition with Human-in-the-Loop BO

2.2

3

Tools for Deep Music Generation

The expanding use of deep generative models encourages researchers to work on
interfaces and tools to make the interactive music generation experience moreaccessible (Huang et al., 2018; Roberts et al., 2019). Vogl et al. (2016) presented
an AI-powered software prototype for generating drum pattern variations. Esling
et al. (2019) applied a VAE to increase the controllability of sound synthesizers.
Roberts et al. (2018) proposed a recurrent VAE, MusicVAE, which utilized ahierarchical decoder for improved modeling sequences with long-term structure.
Our system uses the pre-trained MusicVAE, considering its smooth latent space.
Magenta.js (Roberts, Hawthorne, & Simon, 2018) provides developers with APIs
to design and deploy web-based interface of generative melody models. Louie etal. (2020) investigated AI-steering tools to give users more control over exploring
latent spaces of deep generative models, and presented a human-AI co-creation
tool. While we share a similar motivation, we focus on investigating the approachof formulating the task as a human-in-the-loop optimization.

3

System Overview

Our system is web-based, and it consists of two main modes: composing mode
(Figure 1 (left)) and searching mode (Figure 1 (right)). The searching mode works
as a dialog of the system. The front-end is implemented in JavaScript, while
the back-end is managed by a Flask (Python) server (Grinberg, 2014). All themusical data represented on the interfaces and transferred between front-end andback-end are on the symbolic MIDI representation (Rothstein, 1996). A video
demo of the system can be found at our web source4 .

Fig. 1. System interface. (Left) Composing mode: (A) metronome on/off and BPM;
(B) note sheet; (C) add/subtract more bars and play; (D) select two bars and open the
searching mode. (Right) Searching mode: (E) current candidates that BO generated;(F) editing widget; (G) back to the initial state, go to the next iteration, finish, or
cancel the searching.
4yijunzhou.xyz/index/generative-melody-composition-with-human-in-the-loop-bo

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Yijun Zhou, Yuki Koyama, Masataka Goto, and Takeo Igarashi

Composing Mode. The composing mode allows the user to compose melodies by
inputting notes. The interface resembles standard composing software interface,providing a piano-roll timeline and essential functions. Vertical axis represents
pitch, and horizontal axis represents timeline. Each column represents a 16th
note duration, and a set of 16 columns represents a bar. The user can change thelength of the melody by adding and removing bars. Each row represents a pitchof piano key. Since we focus on the main melody composition, we limit the pitch
range into three octaves, from C3 to B5. The user can draw and delete notes,
and change notes’ pitches on the note sheet. The composed melody is used as
the starting point of the search when entering the searching mode.
Searching Mode. The searching mode is designed to iteratively show multiple
candidates generated by BO based on the user’s preference. In each iteration, it
shows n candidates (n = 4 in our case), from which it allows the user to selectthe one they prefer. After selecting a candidate, they can edit it in the editing
widget before going to the next iteration. The user is not allowed to edit theother bars from the composing mode in the editing widget. They can always goback to the initial state of the current iteration. The iteration goes on by showing
the user another batch of candidates until the user finds the desired one.
Interaction Procedure. (1) Every time the user gets stuck at composing, they can
select a two-bar melody clip that they wish to see variations and then click the“Search” button in the composing mode. (2) After the searching mode pops up,
the user will see the original melody clip in the editing widget and n candidates
on the top, which are drawn by BO from the latent space5 ; the user can listen
to them and select the most favorite one among the candidates and the current
melody. (3) The candidate the user selected will be shown in the editing widget,and the user can tweak details if they want. (4) After editing the candidate, theuser can click the “Next Iteration” button to go to the next iteration, where they
will see a new batch of candidates generated by BO based on their preferential
input; the edited melody in the last iteration stays at the editing widget. (5) The
user can go back to the initial state of the current iteration by clicking “Back”.
(6) Once the user finds the desired one, they can click the “Finish” button tofinish the iteration, and then the searching result will be applied to their melody
in the composing mode; Or, they can also click the “Cancel” button to discard
all the iterations and go back.

4

Algorithms and Implementations

4.1

Generative Model of Melodies

Our implementation uses the pre-trained MusicVAE model (Roberts, Engel, etal., 2018) for melody generation, whose latent space is 512-dimensional. Before we
5

At the first iteration, we generate candidates by random sampling since BO does nothave data yet.

Generative Melody Composition with Human-in-the-Loop BO

5

feed 2-bar melody to MusicVAE, we covert it from MIDI format to NoteSequence
representation (Magenta, 2013). As it is a VAE architecture, it can encode a
melody (which we denote by x ∈ X ) to a latent vector (which we denote byz ∈ Z) as well as decode a latent vector to a melody. We denote the encoder by
E : X → Z and the decoder by D : Z → X . That is, given a latent vector z, thedecoder maps it to a melody x; similarly, given a melody x, the encoder maps it
to a latent vector z. Note that any other deep generative models can be used aslong as they support both encoding and decoding.
4.2

Dimension Reduction of Latent Space

It is known that BO does not work efficiently with very high-dimensional problems(e.g., more than 20 dimensions) (Wang et al., 2016; Chiu et al., 2020), andthus we need to apply dimensionality reduction to obtain a reasonably lowerdimensional latent space. We tested several techniques for this purpose, including
principle component analysis, auto-encoders, and VAEs. We empirically foundthat VAEs worked well in terms of minimizing reconstruction errors and generating
convincing and various melodies. We also tested several different dimensionalities
and found that reducing into a 4-dimensional latent space would be a reasonablechoice. Thus, we decided to train a VAE whose latent space is 4-dimensional.
Note that this choice is consistent with the previous work by Dinculescu et al.
(2019). For the training, we selected ten songs (resulting in 284 two-bar melody
fragments) from the MusicVAE training dataset, the genres of which cover pop,
rock, country, and R&B.
In the following sections, we will still denote the (reduced) latent vector
by z and the (reduced) latent space by Z; but notice that the dimension is 4,
not 512. Also, we will suppose that the encoder E and decoder D include thedimensionality conversion process.
Note that this dimensionality reduction would somewhat sacrifice the original
expressiveness of the generative model; we consider this is a limitation in our
approach. However, we found that the reduced model can still produce various
melodies and is sufficiently inspiring and useful for helping novices find the desired
melodies.
4.3

Problem Formulation

Following the previous work (Brochu et al., 2007; Koyama et al., 2017, 2020), we
formulate the search of the user’s desired melody as an optimization problem:z∗ = arg max f (D(z)),

(1)

z∈Z

where the function f : X → R represents the user’s subjective preference overmelodies, or how good the melody is (thus it is called a “goodness function”(Koyama et al., 2017)), and z∗ is the latent vector that generates the mostsatisfying melody. Note that we do not know the landscape of f , which is the

6

Yijun Zhou, Yuki Koyama, Masataka Goto, and Takeo Igarashi

challenge of this problem. The goal here is to solve this optimization problem
by iteratively presenting candidate solutions to the user and asking them tochoose preferred ones. In the current implementation, we assume a boundary
Z = [−2.5, 2.5]4 to make the search problem tractable.
We generate candidate melodies by a preferential variant of BO (Brochu etal., 2007; Koyama et al., 2017, 2020). In short, new candidates are determinedbased on all the user’s feedback obtained so far so that every new candidate
is as effective as possible (subsection 4.4). As in the previous work, we assume
a Gaussian process (GP) prior on the goodness function. We jointly estimate
the underlying goodness values and the hyperparameters in GP by maximum aposteriori (MAP) estimation; refer to (Koyama et al., 2017) for details.
The algorithm takes the user’s relative assessment as input. In our system,
the set of candidates involved at the i-th iteration consists of (1) x+
i , which isthe current-best candidate (i.e., the user’s selection at the last iteration) and(j)(j)(2) xi = D(zi ) (j = 1, 2, 3, 4), which are candidates sampled by latent-spaceinterpolation between the current-best candidate and a BO-generated candidate(see subsection 4.4), where the corresponding interpolation weights are 0.25, 0.5,
0.75, and 1.0. We use Slerp for the interpolation following (Roberts, Engel, et al.,2018). In addition, (3) xedited, which is the candidate after the user’s editing, isialso involved here. We interpret this situation as relative-comparison feedback inlatent space as
(1)

(2)

(3)

(4)

E(xedited)  {E(x+ii ), zi , zi , zi , zi },(2)which means that the latent vector E(xedited) is preferred over the other latenti
vectors on the right-hand side. This preferential feedback can be probabilisticallymodeled by the Bradley–Terry–Luce (BTL) model (Tsukida & Gupta, 2011).
4.4

Candidate Selection

In BO methods, the next candidates are determined by maximizing an acquisition
function, which calculates the effectiveness of a candidate as the next query. Thereexist several acquisition functions (Shahriari et al., 2016), but the most popular
choice is probably the one called the expected improvement (EI), as used in(Brochu et al., 2007; Koyama et al., 2017, 2020; Chong et al., 2019). It is definedin our problem setting asaEI (z) = E[max{0, f (D(z)) − f + }],(3)

where f + is the estimated goodness value of the current-best melody. The
maximization of the EI function is efficiently solved by standard optimization
algorithms (e.g., L-BFGS).

5

Algorithm Evaluation

We conducted an experiment from the algorithmic viewpoint, where we artificiallysimulated user behaviors. The goal is to verify whether our algorithm can find theGenerative Melody Composition with Human-in-the-Loop BO

7

desired melody in a reasonable number of iterations. We designed this experimentbased on the assumption that there is one specific melody, which is the user’s
desired melody. In the experiment, we used soft dynamic time warping (s-DTW),
an improvement of the original DTW, as the quantitative similarity metricbetween two melodies in latent space (Cuturi & Blondel, 2017).
We simulated user behaviors as follows. First, we generated a desired melody,
xtarget , by random sampling on the latent space. Given the set of candidate(1)(4)
melodies in the t-th iteration, {x+t , xt , . . . , xt }, we selected the one with thesmallest s-DTW to xtarget as the best preferred one at the iteration. We iterated
this procedure for t = 1, . . . , 50. We performed 100 trials in total. In addition to
the s-DTW values, we also recorded the 4-dimensional latent vectors of all theselected candidate melodies and calculated the Euclidean distances to the latentvector of the desired melody. Note that we regenerated the desired melody forevery trial.
Figure 2 (left) shows the plot of the s-DTW values, and Figure 2 (right)
shows the plot of the corresponding latent-space Euclidean distances. Each dot
on the plot represents the mean of 100 trials. We could observe that the s-DTW
values decreased as iterations went on, which was our expectation. We foundthe Euclidean distances in the latent space also decreased consistently, whichindicates the smoothness of the latent space. The results suggest that BO could
effectively find melodies that are reasonably similar to the target one after around
20 iterations in the 4-dimensional latent space.

Euclidean distance

2500

s-DTW

2000
1500
1000
500

12
10
8
6

0

5 10 15 20 25 30 35 40 45 50

Iteration0

5 10 15 20 25 30 35 40 45 50

Iteration

Fig. 2. Simulation experiment results. Each plot shows the mean of 100 trials with thecolored region showing the standard deviation. The left shows s-DTW and the rightshows the Euclidean distance in the 4-dimensional latent space.

6

Pilot Study

To obtain initial qualitative feedback on the proposed approach, we conducted an
informal pilot study with two novice composers, who had wished to compose music
but had no formal composition training. We asked one of the two participants

8

Yijun Zhou, Yuki Koyama, Masataka Goto, and Takeo Igarashi(P1 ) to compose a 32-bar melody, and the other one (P2 ) to compose a 16-bar
melody with our system. Each participant was first tutored by the experimenter
about the features of the system. During the experiment, the participants were
free to compose melodies by themselves or use the “Search” function to get help
from the system. When they used the “Search” function, they were asked to go
for at least 20 iterations.
The composed melodies can be found at the our web source4 . P1 finishedthe composing within 2.5 hours, while P2 finished it within 1.5 hours. In theexperiment, P1 used the “Search” function 8 times, while P2 used it 6 times.
In the follow-up interview, both the participants explicitly expressed that thesystem had effectively provided them more ideas than they had, which inspired
their creativity. “Before I used the ‘Search’ function, I didn’t realize that there
could be so many possibilities developed from my idea” (P1 ). “After I heard thecandidates suggested, I got some fantastic ideas based on them” (P2 ). Both of theparticipants also mentioned that the system made the process of composition
easier. “I tried to compose before but got stuck in extending my idea. But in thisexperiment, just ‘Search’. It saves my life” (P1 ). “I liked it because it seemed thatthose four candidates in each iteration were sorted from similar to different, as
compared to my original idea. It made it easier to select one to fit my melody”(P2 ).
However, they also mentioned room for improvement or drawbacks they
noticed. First, P1 mentioned that she wanted more control on the search function,saying “It would be better if I can set the degree of how different, or how aggressive
the candidates to be. Because I already had some ideas on how the melody goes”.
Besides, P2 wanted the system to take the sequential musical information into
account when searching for candidates, saying “Even though it found somevariations on the melody clip I composed in a previous paragraph, sometimes thecandidates found did not fit a new paragraph”.

7

Conclusion

In this paper, we demonstrated that human-in-the-loop BO had the potential towork effectively and efficiently on searching for the user’s desired melody from thelatent melody space. We also showed that BO could suggest satisfying candidates
within a reasonable number of iterations. The pilot study result suggested thatit also inspired the user’s creativity and lowered the learning barriers of novice
composers. In the future, we plan to investigate how to apply BO to a higher
dimensionality (e.g., 512) efficiently and effectively in an interactive system. We
also want to provide more control in terms of the searching behavior, and to
consider the sequential information, as indicated by the pilot study participants.
Finally, we hope our work can inspire researchers to more deeply investigate
designing music creation tools, or even creativity support tools in a broader
context, by incorporating the human-AI mixed-initiative interaction mechanism.

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9

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