Music Synthesis based on Impression and Emotion of Input Narratives
Saya Kanno
Ochanomizu University
saya@itolab.is.ocha.ac.jp

Takayuki Itoh
Ochanomizu University
itot@is.ocha.ac.jp

Hiroya Takamura
Tokyo Institute of Technology
takamura@pi.titech.ac.jp

This paper presents a technique to synthesize the music
based on the impression and emotion of the input narratives. The technique prepares a dictionary which records
the sensibility polarity values of arbitrary words. The technique also supposes that users listen to the sample chords
and rhythms, and input the fitness values to the pre-defined
impression word pairs, to learn the relations between features of chords/rhythms and these impression. After theseprocesses, the technique interactively synthesizes the music for input narratives. It estimates the fitness values of an
input narrative to the impression word pairs using the dictionary, and then selects the chord and rhythm progressions
those impressions and emotions are the closest to the narrative. Finally, the technique synthesizes the output tune
by combining the chord and rhythm. We suppose this technique encourages to express impression and emotion of the
input narratives by generating music.

We had a questionnaire “Would you like to listen to themusic based on the impression and emotion of the narratives after you read that narratives?” performed on theWeb, and received answers from 57 participants. As a result, 24 participants answered “Strongly want to listen”,
and 26 of others answered “Rather want to listen”. This
result suggests that 88% of the participants are interested
in listening to the music based on the impression of thenarratives. We expect the music generated based on theimpression of the input narratives is effective to stimulateinterest of users. We can imagine the abstract impressionof the narrative before reading by listening to the music
generated by the presented system. Or, we can play the
background music that matches the impression of the narratives which we are reading them. Moreover, we expect
this technique is useful to publish and intercommunicate
the impression of literature by uploading the music. We
also expect the uploaded tunes bring interest and imagination to the narratives.

1. INTRODUCTION

2. RELATED WORK

We may want to express the impression and emotion of
narratives by creating another media. We sometimes write
our sentiments as review documents, or illustrate the scenesof the novels. Here, we may provide too much information
to the readers while writing reviews even though if they do
not want to know the contents and details before reading
the narratives. Or, we may provide unnecessary or inadequate impression from the illustrations. Our motivationof this study is to express the impression and emotion of
narratives applying music.
This paper presents a technique to express the impression
and emotion of narratives by synthesizing music. Music
has effects to derive imagination, behavior, and emotion to
the listeners. These are effective to every people, whether
the listeners are deeply interested in the music or not [15].
We expect such effects are helpful to feel the impression
and emotion of input narratives, without knowing too muchinformation. Moreover, we have been already familiar withmulti-modal arts which combines music and other expression, such as Opera and Ballet. Therefore, we expect thatpeople may feel new types of sensation and inspiration bylistening to the music created for the narratives while thepeople are reading them.

There has been a long history of studies on automatic music generation with different media including images andnarratives. Many of the studies generate music based onhigh-level semantics such as structures of stories or scenes,
while many other studies are based on abstract impression.
Background music generation for slideshows, movies, computer animations, or real spaces is a recent active researchissue. Some of the studies are based on high-level semantics [3] [11] while some of others are based on abstract
impression [1] [12] [14]. The study presented in this paper
is closer to the latter type of studies.
There have been smaller number of studies on music synthesis adapting to narratives applying natural language processing techniques. Endo et al. [4] presented a techniqueto generate music based on arguments and grammaticalstructures of input narratives. This technique is not basedon the impression and emotion of the narratives. Kitahara et al. [10] presented a technique to automatically compose music by a note sequence generation from the impression and emotion of the input narratives. We subjectively
suppose this approach is not always suitable to generateuser-preferable music, because it just automatically generates sequences of notes without learning the impressionof listeners and utilizing the user-preferred music patterns.
Cruz et al. [2] also presented a technique to generate music
based on the emotion of input narratives. Again, this technique does not adopt preferences or impression of users to
the generation of music. On the other hand, our technique

ABSTRACTc
Copyright: ⃝2015
Saya Kanno et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution 3.0 Unported License, which
permits unrestricted use, distribution, and reproduction in any medium, provided
the original author and source are credited.

is based on the mash up of user-prepared chord and rhythm
progressions, and learning of the impression and emotionof the chords and rhythms. This approach can take into
account the impression of the listeners to the music, andutilize the user-prepared musical patterns.
Ishizuka et al. [9] presented a theme music arrangement
system based on impression of story scenes. Its architecture is close to our study since it arranges input theme tunes
based on the numeric impression values. However, it does
not deeply discuss how to calculate impression values andlearn users’ own impressions. Also, this technique does
not apply user-prepared musical patterns.
3. PRESENTED TECHNIQUE
This section describes the processing flow and implementation detail of the presented technique. The techniqueconsists of the following three technical components:
1. Preliminary data construction: Selection of impression words and musical features, and dictionary
construction applying a semantic orientation calculation technique [16]. These steps are applied onceby the system developer.
2. Learning: Calculation of coherency between the musical features and these impressions. This step is applied once for each user as a preprocessing.
3. Interactive process: Music synthesis for input narratives.
Here, we prepared the chords and rhythms by reference tothe commonly used patterns in pops, rock, jazz, and classical music. Our current implementation supposes BPM(Beat per Minute) and number of measures of all the chords
and rhythms are equal.
Our current implementation generates music by just synthesizing chords and rhythms. It is our on-going work to
implement the procedure to synthesize melodies to generate the music, since the presented mechanism to selectchords and rhythms can be similarly applied to melodies.
The below subsections describe the processing flow andimplementation detail of the each technical component.
3.1 Preliminary data construction
Our preliminary data construction phase includes the following two processes: 1) selection of impression wordpairs and musical features, and 2) dictionary construction.
Our current implementation constructs Japanese dictionary;
however, the presented mechanism is not limited to specific natural languages.
3.1.1 Selection of impression word pairs and musicalfeaturesThis step firstly selects impression word pairs used for semantic orientation calculation, and musical features used
for the selection of chords and rhythms. We listed the impression word pairs and musical features shown in Table 1
as the candidates. We learned impression word pairs from

Table 1. Candidates of impression word pairs and musicalfeatures.
Impression word pairs for chord progression
Bright - Dark
Light - Heavy
Enjoyable - Wistful
Brassy - Simple
Tripping - Quiet
Energetic - Calm
Musical features for chord progression
Average of tones
Distribution of tones
Number of simultaneous tones
Ratio of inharmonic tones
Frequency of major, minor, seventh, major seventh,and minor seventh chords
Impression word pairs for rhythm progression
Fast - Slow
Light - Heavy
Quiet - Loud
Brassy - Simple
Energetic - Calm
Musical features for rhythm progression
Frequency of tones for each of drums
Total number of tones
Frequency of 16-, 8-, and 4-beat notes
Frequency of triplets

Ikezoe et al. [8], and musical features from Hasegawa etal. [6]. Also, we subjectively added frequency of several
chords, ratio of inharmonic tones, and frequency of tones
for each of drums, because they are often effective to express the particular mood or emotion.
Then, we conducted a questionnaire for the selection ofimpression word pairs and musical features. We asked
participants to listen to the sample chord and rhythm progressions, and answer the subjective fitness between thesamples and impression word pairs in the five-point Likert
scale. We calculated the correlativity between each of thefitness values and each of the musical feature values. If afitness value was not well correlated with any of the musical feature values, we removed the impression word pair.
At the same time, we removed a musical feature value fromthe candidate, if it was not well correlated with any of thefitness values. As a result, we listed the impression wordpairs and musical features shown in Table 2 in our implementation.
3.1.2 Dictionary construction
We constructed a Japanese dictionary containing noun, verb,
adjective, and adverb, with normalized values representing
the fitness to all the impression word pairs. Here, we defined the fitness value corresponding to one of the impression words as “1”, and the value corresponding to the otherword as “-1”. We calculated the fitness values of each wordin the dictionary for each of the impression word pairs, applying a semantic orientation calculation technique [16].
As a result, the j-th word in the dictionary has a Mw di-

Table 2. Finally selected impression word pairs and musical features.
Impression word pairs for chord progression
Bright - Dark
Enjoyable - Wistful
Tripping - Quiet
Energetic - Calm
Musical features for chord progression
Average of tones
Distribution of tones
Number of simultaneous tones
Ratio of inharmonic tones
Frequency of major, major seventh,and minor seventh chords
Impression word pairs for rhythm progression
Fast - Slow
Light - Heavy
Brassy - Simple
Musical features for rhythm progression
Frequency of tones for Toms, Snare drums,
Bass drums, cymbals, and High-hats
Total number of tones
Frequency of 16-beat notes
Frequency of triplets
mensional vector gj = {gj1 , gj2 , ..., gjMw }, where Mw is
the number of impression word pairs, and gji is the fitness
value of the j-th word to the i-th impression word pair.
3.2 Learning of impression and emotion
This step learns the relationships between the musical features and these impressions. This step is applied once foreach user as a preprocessing. Our current implementation supposes to ask a user to listen to the sample chordand rhythm progressions, and answer their fitness to theimpression word pairs. This process then calculates therelationships between the musical features of the sample
chords/rhythms and the fitness values answered by the user.
Here, our current implementation applies a linear multiregression analysis to solve the relationships, because we
suppose the relationships can be approximated as linear
functions. This study applies the following equation to express the relationships between the musical features andthe fitness values:

fi =

M
∑

aij mj(1)

j=1Here, fi is the fitness value for the i-th impression wordpair, M is the number of musical features, mj is the j-thmusical feature, and aij is the coefficient for the i-th impression word pair and the j-th musical feature. The linear
multi-regression analysis solves the values of the coefficients, given the set of values of fi and mj . Our implementation applies this process independently to chords andrhythms. We can estimate the impression of later provided
chord and rhythm progressions by calculating the fitness

values using the above equation, after solving the coefficients of the equation.
3.3 Interactive processThis step synthesizes music adopting to the impression andemotion of input narratives. Our implementation interactively provides the synthesized music when a narrative is
given. This process is divided into the following components: document analysis, selection of chord and rhythm,
and music synthesis.
3.3.1 Document analysis
This step firstly applies a morphological analysis to the input narratives. Our current implementation applies an open
source Japanese morphological analysis software MeCab[17] to the narratives. It then extracts noun, verb, adjective, and adverb from the result, and then calculates theaverage of fitness values recorded in the dictionary generated by the preliminary data construction process, for eachof the impression word pairs. The technique treats the average values as the estimated impression and emotion of
the narrative.
Here, long narratives or novels contain progression andvariation of impression and emotion. Therefore, it is not always adequate to calculate the average impression/emotionof the whole narrative. Our implementation calculates theaverages of the fitness values scene-by-scene. Currently
we suppose that input narratives are manually divided tomultiple scenes. We would like to apply automatic scene
recognition techniques to divide the input narratives as a
future work.
As a result, we express the impression of a scene of the input narrative as a Mw dimensional vector h = {h1 , h2 , ..., hMw }.
We calculate the∑fitness value to the i-th impression wordM
pair as hi = M1s j s gji , where Ms is the total number ofwords appeared in the scene. gji is the fitness value of thej-th word to the i-th impression word pair, extracted fromthe preliminary constructed dictionary.
3.3.2 Selection of chord and rhythm
Next, this step selects chord and rhythm progressions. The
technique calculates the musical features mj for all the
prepared chords and rhythms, and then estimates the fitness
values fi for the chords and rhythms by using the equation(1). Our current implementation uses the four-dimensional
fitness values for the following impression word pairs, [Bright- Dark], [Enjoyable - Wistful], [Tripping - Quiet], and [Energetic - Calm] for the chord progressions. For the rhythm
progression, it uses the three-dimensional fitness values forthe following impression word pairs, [Fast - Slow], [Light- Heavy], and [Brassy - Simple].
Our technique generates trajectories of the fitness values
consisting of Nscene vertices and Nscene − 1 segments,
where Nscene is the number of scenes. It then selects thechords and rhythms so that the trajectories of the fitness
values of the selected chords or rhythms looks similar to
the trajectory generated from the input narrative. Our current implementation selects sets of chords or rhythms of
the scenes which minimizes the following formula:

( N
scene
∑
min

α

∑

Nscene −1∥fi − gi ∥ + (1 − α)

i

(
1−

′

′

f i,i+1 · g i,i+1|f ′ i,i+1 ||g′ i,i+1 |i(2)Here, α is a user-defined constant real value satisfying
0 ≤ α ≤ 1, fi is a fitness value vector of a chord or
rhythm for the i-th scene, gi is a fitness value vector of
the i-th scene calculated from the input narrative. f ′ i,i+1
and g′ i,i+1 are defined as follows:′

Table 4. The fitness for impression word pairs in each)) scene of the input narrative.

′

f i,i+1 = fi+1 − fi , g i,i+1 = gi+1 − gi .

Bright - Dark
Enjoyable - Wistful
Tripping - Quiet
Energetic - Calm
Fast - Slow
Light - Heavy
Brassy - Simple

Scene 1-0.034480-0.115318-0.149857
0.325041-0.122990
0.564965
0.521458

Scene 20.013741
0.171532
0.617872
0.528052
0.236133
0.436106
0.468449

Scene 3-0.176004
0.122126
0.580159
0.531251
0.220244-0.091217-0.241370(3)

The first term of equation (2) attempts to minimize thesum of distances between the fitness value vectors of thechords/rhythms and narratives, while the second term attempts to minimize the geometric differences of the trajectory between the chords/rhythms and narratives. We expect
this definition realizes the selection of chords and rhythms
taking into account the total variation of the generated music in addition to the similarity of impressions.
3.3.3 Music synthesis
Finally, this step synthesizes the selected chord and rhythm.
Our current implementation supposes that the chords andrhythms are stored as independent MIDI files which contain a single track for the chord or rhythm. It simply copies
the tracks of selected chord and rhythm into another MIDI
file consisting of the two tracks. This implementation will
be extended to incorporate melodies as a future work.
4. EXPERIMENT AND DISCUSSION
This section introduces our experiments with the presented
technique. We had the following steps for the experiments:
Step 1: We asked participants to listen to the sample chords
and rhythms. Then, we asked them to answer the fitness of the impression word pairs in six-point Likert
scale, to learn their sensibility for the music.
Step 2: We generated tunes for input narratives applying
the learning results of each of the participants. Then,
we asked them to evaluate the degree of coincidenceof the impression between the narrative and tune in
five-point Likert scale. We also asked them to freely
comment their impressions for the output tunes.
Here, all chords recorded in the sample MIDI files wereplayed as half notes, and their timbre specified by the program number was piano, in this experiments. Lengths ofall sample MIDI files of chords and rhythms were eightmeasures, and their tempo was 120 BPM (Beat Per Minute).
Table 3. The contents of the input narrative.
Scene 1 your profile of when you sing with smile wasvery beautiful.
Scene 2 I fell in love with you by looking that profile.
Scene 3 But, I was disappointed in love.

Table 5. Results and evaluation of participants A and B.
Participant A
Chord
Rhythm
Evaluation
Scene 1chord 12 rhythm 2
4
Scene 2
chord 9rhythm 3
5
Scene 3
chord16 rhythm 11
1
Total evaluation
2
Participant B
Chord
Rhythm
Evaluation
Scene 1chord 9 rhythm 19
2
Scene 2
chord 11 rhythm 19
3
Scene 3
chord 16 rhythm 16
4Total evaluation
4

In the Step 1, we provided the same sample chords andrhythms for both the participants. After asking participants
to listen to the generated tunes in the Step 2, we showed
participants the contents of input narratives and explained
how the scenes are split. We did not explain how the narrative was split in order, because we wanted participants tobe unconscious of contents of the input narrative while they
were evaluating the output music. We prepared a dramatic
short narrative divided to three scenes in this experiment.
Participants evaluated three tunes generated for the scenesof the narrative. Table 3 describes the scenes, and Table 4shows the fitness of impression word pairs.
The participants of this experiment were two female students in the master’s course, who had experiences and expert skills of musical instruments and vocals, and a certain
level of musical knowledge. The length of the MIDI files
were unified in 16 seconds in this experiment. As a result,lengths of all the tunes synthesized by the proposed technique were 48 seconds. We prepared 23 pieces of chords
and rhythms for this experiment.
Table 5 shows the chord/rhythm selection results for twoparticipants, and the evaluations by the participants. The
result denotes the synthesized tunes are different despite
completely same narrative was provided to the two participants. This suggests our technique can generate different
tunes according to the sensibility of each user.
Let us discuss on the results of participant A. Table 6 andTable 7 show the fitness values for chords and rhythms respectively.
Participant A mentioned that “I had an impression thatwistful event was happened but I thought it was not a serious scene” while listening to the tune for Scene 1. The fit-

Table 6. The fitness values of participant A for the chordsin each scene.
Scene 1
Scene 2
Scene 3Bright - Dark
-0.166850 0.888388 0.014798
Enjoyable - Wistful -0.270462 0.165336 -0.190081
Tripping - Quiet
0.090049 0.443500 0.382450
Energetic - Calm
0.042111 0.653096 -0.068819

Table 7. The fitness values of participant A for the rhythms
in each scene.
Scene 1
Scene 2
Scene 3
Fast - Slow
0.075839 0.496695 0.033937
Light - Heavy
0.145658 0.437712 0.752391
Brassy - Simple 0.075839 0.996108 0.485212

ness value for [Enjoyable - Wistful] of the chord for Scene
1 was negative, close to “Wistful”. The above commentfor Scene 1 is consistent to the fitness value for the chord.
Participant A also mentioned “This scene seems happy,
and more exciting than the previous scene” for Scene 2.
Here, all fitness values for the chord and rhythm in Scene
2 are higher than the fitness values in Scene 1, as shown in
Table 6 and Table 7. This variation of fitness values conforms to the transition of the fitness values of input narrative shown in Table 4. Again, these results denote theabove comment for Scene 2 is consistent to the variationof fitness values. Actually, evaluations of the participant A
for Scenes 1 and 2 were 4 and 5, relatively high, as shownin Table 5. On the other hand, participant A mentioned that
“I had an impression that a good event was happened andend up from music, however, the narrative of this scene
was sad.” for Scene 3. This comment denotes impressionof participant A for music and input document was really
opposite. Many of the fitness values of Scene 3 in the input narrative are positive as shown in Table 4, which suggest nimble and energetic impression. These fitness values
are inconsistent to the comment that participant A felt sad
impression after reading Scene 3. To solve this problem,
we would like to extend our implementation of document
analysis component customizable to users’ sensibility.
Next, let us discuss on the results of participant B. Table 8
and Table 9 show the fitness values for chords and rhythms
respectively.
Participant B mentioned that “Although I received a cheerful impression like exercising from the music, it was actually a quiet scene with no movements” for Scene 1. However, the fitness value for [Tripping - Quiet] and [Energetic

Table 8. The fitness values of participant B for the chords
B in each scene.
Scene 1
Scene 2
Scene 3Bright - Dark
0.059933 0.896135 0.043383
Enjoyable - Wistful -0.141256 0.108170 0.216329
Tripping - Quiet-0.235864 0.969589 -0.123636
Energetic - Calm
-0.041046 0.015811 -0.111593

Table 9. The fitness values of participant B for the rhythms
in each scene.
Scene 1
Scene 2
Scene 3
Fast - Slow
0.507989 0.507989 0.322039
Light - Heavy
0.185770.18577
0.363906
Brassy - Simple 0.642532 0.642532 -0.111264- Calm] of the chord for Scene 1 were negative, as shownin Table 8, where these values denote the quiet impression.
This result suggests our experiment might not successfully
learn the sensibility of participant B. In addition, participant B mentioned that “I felt that there was no change
between Scene 1 and 2, because the rhythm of Scenes 1
and 2 were the same”. This comment suggests rhythm wasan important musical factor for participant B, and we mayneed to analyze which musical factor participants remark.
Participant B mentioned that “I felt the sudden change tohave a serious feeling” while listening to the tune for Scene
3. Actually, many fitness values of chords and rhythms for
Scene 3 got smaller than those for Scene 2, as shown in
Table 8 and Table 9. This result demonstrates our technique could represent the significant changes of the impression between the scenes. Participant B mentioned that
“Entire flow of music substantially coincides with the flowof story I imagined from the music”, and finally rated thetotal evaluation as 4.
Our experiment assigned the same chord progression for
Scene 3 for participants A and B, as shown in Table 6 andTable 8. However, participant A mentioned “I got the impression like happy ending”, while participant B answered“It is a serious scene”. These comments are actually opposite. This result suggests that the same chord and rhythm
progression may give different impression to different users.
The results introduced in this section suggest that the impression of input narratives was close to the impression ofgenerated music in many cases. We would like to customize the document analysis based on users’ sensibility
so that we can improve the degree of coincidence of theimpression between the music and narratives.
5. CONCLUSION
We proposed a technique to synthesize music based onemotion and impression of the input narratives. This technique selects the chords and rhythms to the scenes of thenarratives using by estimating the fitness values for the
impression word pairs from the musical features. In the
preliminary data construction step, we selected sets of impression word pairs as a result of survey on the correlations between musical feature values and pairs of sensibility words. This paper introduced experiments which generated different impression of music in response to the sensibility of the participants. The result suggests that we could
generate tunes which roughly match to the fitness values
specified by the sensibility polarity dictionary, while we
still have issues on document analysis.
Our future issues include the following:• Review of the association of impression word pairsand musical features.
• Re-design of interface to input the sensibility of users.
• Implementation of automated recognition of scene
breaks in the narratives.
• Association of temporal deployment of music andnarrative.
• Embedding melodies while the synthesis of music.
The presented study listed candidates of music features ofchords and rhythms excluding several important ones suchas velocity of the notes. We suppose participants might
have bias in representation of impressions and feelings because such important features were constant in our sample chords and rhythms. We would like to review the music features again, and then extend our implementation byadding important features.
We received several comments from the participants ofthe presented experiment regarding the methodology of thelearning step. One of the typical comments was that “I
could not keep the constant criteria in mind, while listening
to the sample chords or rhythms, and answering the fitness
values.” Reflecting these comments, we would like to testother interfaces to input the sensibility of users. Hevnerpresented a music evaluation method [7] which asks participants to choose one of the word groups which matches tothe listened tunes. Several studies on music evaluation applied tournament methods which ask participants to comparatively select one of the tunes, and finally specify thebest tune. We would like to apply such methods to appropriately learn the preferences of participants.
Our current implementation learns users’ sensibility justfor automatic selection of chords and rhythms. We expect
our technique can be improved by re-defining fitness values of characteristic words in the preliminary constructed
dictionary according to the semantics of the narratives orusers’ sensibility. We would like to implement a mechanism to customize the dictionary based on this discussion.
Finally, we would like to develop the melody selection
process, in addition to larger database construction for chords
and rhythms, to realize the generation of more impressive
and emotional music. Our on-going work applies an automatic music composition method featuring a genetic algorithm [13] and maximum likelihood estimation [5].
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