=Paper=
{{Paper
|id=Vol-2747/paper6
|storemode=property
|title=Recognition of valence judgments in music perception using electrocardiographic signals and machine learning
|pdfUrl=https://ceur-ws.org/Vol-2747/paper6.pdf
|volume=Vol-2747
|authors=Ennio Idrobo-Ávila,Humberto Loaiza-Correa,Flavio Muñoz-Bolaños,Leon van Noorden,Rubiel Vargas-Cañas
}}
==Recognition of valence judgments in music perception using electrocardiographic signals and machine learning==
https://ceur-ws.org/Vol-2747/paper6.pdf
Recognition of valence judgments in music perception
using electrocardiographic signals and machine learning
Ennio Idrobo-Ávila1, Humberto Loaiza-Correa1, Flavio Muñoz-Bolaños2, Leon van
Noorden3, Rubiel Vargas-Cañas4
1 PSI - Percepción y sistemas inteligentes, Escuela de Ingeniería Eléctrica y Electrónica, Uni-
versidad del Valle, Cali, Colombia
{ennio.idrobo,humberto.loaiza}@correounivalle.edu.co
2
CIFIEX - Ciencias Fisiológicas Experimentales, Departamento de Ciencias Fisiológicas,
Universidad del Cauca, Popayán, Colombia
fgmunoz@unicauca.edu.co
3 IPEM - Institute for Psycho-acoustics and Electronic Music, Department of Art, Music and
Theatre Sciences, Ghent University, Ghent, Belgium
leonvannoorden@mac.com
4
SIDICO – Sistemas Dinámicos de Instrumentación y Control, Departamento de Física, Uni-
versidad del Cauca, Popayán, Colombia
rubiel@unicauca.edu.co
Abstract. Music can interact with humans through sentiment and emotions and
aid in relieving stress. While human - computer interaction (HCI) has begun to
use ECG as a means of recognizing music-elicited emotions, the need remains
for research related to implicit interaction interfaces in HCI systems, to deter-
mine how they might enhance human bandwidth in control or interaction. A
system is proposed to recognize or classify emotional responses to sounds -
from music and nature - based on ECG signals and a single scale of perception.
It was possible to recognize the valence judgment of each subject using binary
evaluation and ECG signals. Outcomes revealed a performance of 0.91 AUC
and 0.86 accuracy in recognizing emotional valence elicited by the music of-
fered. Subjects were found to give more uniform responses to nature sounds
than to music.
Keywords: Emotion, Music, Nature Sounds, Perception
1 Introduction
Stress may affect human beings in beneficial ways, preserving cell homeostasis [1]
and enhancing the manner in which people learn and remember [2]. Chronic exposure
to severe conditions of stress, however, can lead to a number of obstacles to human
health. Stress disorders affect the central nervous system, immune system function,
and the condition of the gastrointestinal, endocrine, and cardiovascular systems [1].
Stress can furthermore produce changes in heart-brain interaction [3]. Due to the role
of stress in human health, physicians require a wide range of therapeutic tools for
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
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treatment. Pharmacological medication and nutraceuticals and non-pharmacological
interventions are all relevant [1]. Non-pharmacological music interventions have
shown a reduction in anxiety [4] and stress in psychophysiological variables [5] [6].
Research has examined how music may help with stress management. Music inter-
acts with humans through their sentiments and emotions. The use of electrocardio-
graphic signals (ECG) and heart rate variability (HRV) can help to assess states of
stress [7] [8] [9] as well as emotions elicited by music [3] [10]. HRV revealed the
varying music capacity for reducing stress [9] and how music could produce arousal
responses and ease stress [11]. Pleasant and unpleasant music produced changes in
periodic repolarization dynamics [12] [13]. ECG and galvanic skin response (GSR)
were also considered in assessing the influence of sleep on emotional perception of
music, revealing an association between emotion and sleep duration [14]. Another
study showed that an increase in heart and respiratory rates occurred while subjects
listened to happy music, whereas only heart rate increased with sad music; valence
modified heart rate whereas arousal produced changes in respiratory rate [15].
Others used ECG and developed systems to classify different states or responses to
music. Systems were developed for instance to recognize conditions before and after
listening to music using: two musical genres [16]; responses to music with four dif-
ferent emotional characters [17]; and four emotional responses to music able to in-
duce emotions related to four quadrants in the model of valence-arousal: joy, tension,
sadness, and peacefulness [18]. Most research used classic statistical analysis tools [9]
[11] [12] [13] [14] [15], while others used machine learning tools such as k-means,
naïve Bayes and artificial neural networks [16], probabilistic neural network [17], and
least squares support vector machine [18].
The field of human-computer interaction (HCI) has not yet fully explored ECG as
a measure for recognizing conditions of stress and emotions elicited by music. But
examples in the literature include the following: A system for reducing human effort
and improving the practicality of HCI was developed, comprising a music player that
selects songs depending on the emotion of the user. The system recognized four emo-
tions (happy, angry, sad and neutral) by means of facial expression, using convolu-
tional neural networks [19]. Another system for playing notes on a musical score was
developed, controlled by eye-tracker and head movements on fixing the gaze on a
point on the score [20]. Elsewhere, a structure was created for multimodal control of
musical performance and sonic interaction, through explicit interaction with tangible
objects (pucks), and implicit interaction by measuring brain activity (electroenceph-
alography-EEG) and ECG [21]. In other studies, physiological measurements were
used to perform sound synthesis and control of tempo in a digital musical instrument,
while a system was developed to detect emotion using EEG, pulse, and blood pres-
sure, recommending color and music according to emotional states of users of a self-
driving vehicle, classifying emotions by support vector machines [22].
However, since implicit interaction interfaces (e.g. that use physiological meas-
urements) are still emerging, research is needed on such interfaces in HCI systems, to
determine to what extent they might enhance human bandwidth in control or interac-
tion [21]. The use of physiological signals to generate music automatically was thus
proposed in the field of physiological computing [23]. Although physiological re-
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sponse elicited by music has been assessed using such signals as ECG and HRV [11]
[16], most systems do not incorporate each subject’s own perception. Most considered
a two-dimensional emotion space [17]. Research carrying out the classification of
emotions has generally used music previously categorized according to the emotion it
is able to produce (emotional music). Emotions associated with sadness, peacefulness,
and happiness have commonly been considered, together with emotions related to
tension such as threat, scary, or fear [14] [17] [18] [24]. There is room for improve-
ment, as classification rates oscillate between 60 and 90% [18] [22] [25] [26] in the
best cases.
1.1 Our contribution
Given the above history, recognizing that ECG signals are seldom used for emotion
recognition and that very little research has associated heart and emotions using ma-
chine learning [27], we have constructed a system to recognize or classify emotional
responses to music and nature sounds, from ECG signals and using a single percep-
tion scale.
Our hypothesis is that emotional responses to such sounds, measured by a single
scale, can be recognized using only ECG signals classified by machine learning tech-
niques. For future applications to be able to take decisions aimed at reducing stress in
subjects based on their emotional response, it is clearly important that machines can
recognize states of stress. Further studies are also required regarding pleasant and
unpleasant emotions elicited by music, an area of research “still in its infancy" [3] by
which sounds and their influence could enhance the quality of human life. Similarly,
physiology-based interfaces in HCI have also been suggested as a means of improv-
ing single- and multi-user experiences [21].
2 Methodology
2.1 Data acquisition and experimental procedure
Data acquisition required the participation of 23 healthy adult volunteers - 9 females
and 14 males, with a mean age of 25.5 yrs (SD=6.8) - in an experimental auditive
procedure. All gave informed consent. The ethical committee of the Universidad del
Cauca approved the informed consent and the experimental procedures in their entire-
ty. Subjects were assessed through an audiometry exam, and were fully instructed
about the procedures of the experimental phase. For the presentation of auditive stim-
uli, subjects were in a supine position, equipped with noise-cancelling headphones
(Fig. 1.) and instructed to keep their eyes closed. Their heart electrical activity was
then recorded using an ECG acquisition system (OpenBCI - Cyton Biosensing Board
(8-channels) [28]); the electrode position in the ECG corresponded to that of the lead
II. ECG was recorded with a sample rate of 250 Hz. Each subject was tested individu-
ally.
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Once the subjects were prepared, music and nature sounds were played randomly
to them. The 11 music tracks comprised instrumental song sections; ten nature tracks
made up the test. At the end of each track, the subjects opened their eyes and assessed
their perception (by means of a graphical user interface projected on the ceiling above
them) on a scale of 1 to 9, with low figures representing a negative response and
higher figures reflecting a more positive reaction (these responses were then grouped
in binary form – as “Positive” or “Negative” to carry out the subsequent classifica-
tion). After subjects recorded their perception, a 15-second period of silence was in-
corporated before the next track. Volume was normalized across all tracks and each
track comprised 12 seconds. Music included Colombian music from the Pacific coast,
Latin Jazz, Merengue, Rock in Spanish, Classical, Bachata, Popular music, Vallenato,
Reggaeton, and Ballads in Spanish. These genres were chosen given the experiment
cultural context in Southwestern Colombia. The 21 tracks were played a single time,
in a random manner, to each subject.
Fig. 1. Diagram of experimental procedure
2.2 Signal processing
Captured signals (n=483) were conditioned by applying a third-order one-dimensional
median filter and after that, filtered signals were subtracted from the original signals
to perform baseline wander correction [29]. A data augmentation process was then
carried out using wavelet-based shrinkage filtering [30]. Following the method im-
plemented in [30], three mother wavelets were used to filter the signals - Daubechies
4 (db4), Daubechies 6 (db6), and Symlets 8 (sym8). The outcome of this process was
1932 signals. Signals were then segmented by means of a Hamming Window; each
signal was divided into three segments of six seconds each, with 50% overlapping
[31]. After that, feature extraction was conducted, where maximum, mean, variance,
skewness, kurtosis, energy, entropy, and Katz and Higuchi (k=3) fractal dimensions
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of the signals were extracted; these features were computed directly from the signals.
Feature extraction was carried out in Matlab software [32].
2.3 Classification
Having extracted the features, classification processes were implemented using six
artificial intelligence algorithms: k-nearest neighbors (kNN), lightGBM, neural net-
work, random forest, and XGBoost (Table 1). These were designed to discriminate
between the binary perception responses: Positive and Negative.
Table 1. Configuration parameters of the implemented artificial intelligence algorithms
Classification
Configuration
algorithms
k-nearest neighbors Number of neighbors: 20, metric: Manhattan, weight: distance
LightGBM Max bin=600, learning rate=0.001, number of leaves=12, boost-
ing=gbdt, number of iterations=7000
Neural network Multi-layer perceptron with backpropagation. Neurons in hidden layers:
90, activation: ReLu, regularization: alpha=0.05, solver: Adam
Random forest Number of trees: 45
XGBoost Learning rate=0.3, max depth=5, min child weight=1, gamma=0, al-
pha=0.1, subsample=1, colsample by tree=1, objective=multi:softprob
2.4 Data, training, test and evaluation of models
Regarding the dataset generated, the rate of positive and negative sentiments were
53% and 47% respectively for music, and 70% and 30% respectively for nature
sounds. The ten folds cross-validation method was utilized for training the algorithms.
Performance was evaluated using the area under the ROC curve (AUC), accuracy,
precision, recall, and F1-score [33]. These metrics allow evaluation of classifier per-
formance working on imbalanced datasets.
3 Results
After training the selected algorithms, performance was evaluated through the AUC,
accuracy, precision, recall, and F1-score metrics. The classification evaluations are
shown according to the type of stimuli considered - music (Fig. 2.) and nature sounds
(Fig. 3.). The outcomes showed accuracy up to 0.86 and AUC 0.91 for music stimuli
(Fig. 2.) and both accuracy and AUC up to 0.92 for nature sounds (Fig. 3.).
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Fig. 2. AUC, accuracy, precision, recall, and F1-score for music stimuli
Fig. 3. AUC, accuracy, precision, recall, and F1-score for nature sound stimuli
4 Discussion
From the outcomes it was observed that a single scale of emotion evaluation with a
particular ECG signal lead allowed the selected machine learning algorithms to dis-
criminate valence judgments elicited by music and nature sounds. With music stimuli,
the best AUC performance was achieved with the kNN and neural network algo-
rithms, both attaining 0.91, and accuracy was greatest using lightGBM, with an accu-
racy of 0.86. The best performance with nature sound stimuli was with kNN, with
0.92 AUC and accuracy, while neural network also achieved an AUC of 0.92. In gen-
eral, classification of emotions elicited by nature sounds was better than with music
(Fig. 2. and 3), although this could be influenced by the unbalanced responses to na-
ture sounds. All considered evaluation metrics confirm this higher performance. This
suggests that subjects responded more uniformly to nature sounds than to music. This
behavior may arise from the fact that people generally are exposed to the same com-
mon nature sounds, provoking more or less the same responses. People are exposed to
music however in many different ways in their daily lives. Moreover, personal prefer-
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ences and tastes have a great influence, producing more variability in the responses
between subjects.
Discriminating between relaxed and excited states elicited by a multimedia expo-
sure that incorporated relaxing and exciting music, 90% accuracy was achieved using
GSR, ECG, electrooculogram, EEG, and photoplethysmography [25]. Another study,
classifying three responses to Persian music - happy, peaceful, sad - attained an emo-
tion classification accuracy of 90% [34]. While the above studies mainly classified
responses to music associated with pre-anticipated emotions or emotions defined
previously, in the present work, the emotional responses of subjects to a range of
stimuli was considered, rendering remarkable the high performance in accuracy ob-
tained. Even more remarkable given that the perception of each subject plays an im-
portant role in the classification.
Another study in the field of HCI classified four emotional states (stability, relaxa-
tion, tension, and excitement) from EEG data with a performance of 86% [22]. Simi-
larly, other work classified music-evoked emotions from EEG data, in which 67% for
valence was the best performance [26]. Meanwhile, in [18], for positive and negative
valence, high and low arousal, and four types of emotion (joy, tension, sadness, and
peacefulness) correct classification rates of 83, 73, and 62% were achieved, respec-
tively. Our results correspond closely to the 83% valence classification. Differences
were seen as subjects presented their perception report for each stimulus individually
while in [18] subjects reported their perception after listening to longer sessions of
several minutes in which several stimuli were presented. Additionally, our results
suggest that with only 12 seconds it is possible to produce an emotional response in
subjects. The comparative studies above considered EEG signals while in our experi-
mentation, emotion recognition was performed using ECG signals.
New HCI proposals in physiological computing suggest the use of such physiolog-
ical signals as EEG, ECG or electrodermal activity to automatically generate music
using machine learning techniques. Our system represents an approximation to this
aim [23]. Moreover, this type of system could be applied in the field of education
where background music is expected to improve learning processes [10]. Applications
in music therapy, stress management, the practice of yoga or mindfulness might also
be implemented. New studies may even address the support of therapies to manage
depression using implicit interaction (measuring ECG signals) in HCI systems based
on music stimuli. A great advantage in systems that use ECG is its simplicity for
measurements in comparison with physiological signals such as EEG. For very lite
applications ECG could be measured using only two electrodes [35] [36].
5 Conclusions
A system based on machine learning techniques and ECG signals was designed, to
recognize emotional responses to music and nature sounds, measured by a single scale
of perception. Subjects were found to give more uniform responses to nature sounds
than to music. The kNN algorithm gave the best mean performance. It was possible to
recognize the distinctive perception of subjects using binary evaluation and ECG
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signals. These outcomes encourage new research and applications that take account of
the individual perception of each subject in such a way that an HCI system based on
music perception could operate in a more personalized way with more accuracy.
This study showed that with a short period of exposure to a stimulus it is possible
to elicit emotions in listeners. A recommendation for future work however concerns
the exploration of longer stimuli; another recommendation involves the emotion eval-
uation scale, initially suggesting the inclusion of a neutral possibility in addition to
negative and positive perception; finally, studies using more subjects are recommend-
ed, leading to more robust systems with a better performance.
Acknowledgments. This work was supported by Universidad del Valle, Universidad
del Cauca, and Colciencias (Funding call No. 727 of 2015), Colombia. The sponsors
(did not have direct participation) had no role in the study design, data collection and
analysis, neither decision to publish, nor preparation of the manuscript. We are
especially grateful to Colin McLachlan for suggestions relating to the English text.
Conflict of interest statement. The authors declare that the research was conducted
in the absence of any commercial or financial relationships that could be interpreted
as a potential conflict of interest.
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