Advances in Mathematical
Sciences and Applications
Vol. 27, No. 1 (2018), pp. 241–262

GAKKOTOSHOTOKYO JAPANA COMPARISON OF CLASSIFIERS FOR MUSICAL
GENRES CLASSIFICATION AND MUSIC EMOTIONRECOGNITION

Beatriz C. Flamia de Azevedo ∗Federal University of Technology of Paraná - Brazil, Mathematics Department
1640, Alberto Carazzai, 86300-000, Cornélio Procópio(E-mail: beatrizazevedo@alunos.utfpr.edu.br)andGlaucia Maria Bressan †Federal University of Technology of Paraná - Brazil, Mathematics Department
1640, Alberto Carazzai, 86300-000, Cornélio Procópio(E-mail: glauciabressan@utfpr.edu.br)

Abstract.
The automatic classification of music into genres is one of the most popular tasks in the music information retrieval research area and it can be framed as apattern recognition problem. The goal of this paper is to investigate and to apply different classification methods in order to automatically classify Latin musical genres andpredominant emotions associated to them. In order to do this automatic classificationtask, musical physical attributes (sound beats, timbre and frequency) are considered as
input of the classification methods and the performances of classification algorithms areanalyzed. The classifiers considered in this paper are based on data mining and statisticaltechniques: Decision Trees, Random Forest, k-Nearest Neighbor (kNN), Support Vector
Machine (SVM) and Artificial Neural Network (ANN). Each Latin genre is associated with
its predominant emotion, obtained from the literature. Thus, the proposed methodology
can be easily used by music therapists in health treatments.
∗

Financial support Fundação Araucária
corresponding author: glauciabressan@utfpr.edu.br
Communicated by Editors; Received May 5, 2018.
2010 AMS Subject Classification: 68T10; 68Q01; 62-07; 68R10.
Keywords: Latin Musical Genres, Music Emotions, Classification Methods, Pattern Recognition.
†

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1

Introduction

Music is more than leisure activity; it is part of human development. Listening to music
provides benefits for the body, mainly for the brain. People reaction when listening tomusic and the emotion power of music have been researched for different areas as medicine,
psychology, mathematics, neuroscience. The scientists are interested in observe the music
effects in the brain and body reaction to musical effects. The results of these researches
are very positive and they demonstrate that music can induces changes in mind andconsequently it improves humans life quality. This fact then has favored the use of musicas a tool for the different diseases treatment, such as cardiovascular and respiratory [56],speech and memory [17, 23, 24], brain’s disorder such as autism, schizophrenia, depression,anxiety [18, 23, 31, 34] and others.
Music is present in our life since early age. Nakata and Trehub (2004) report thatsinging is more effective than speech at holding an infant’s attention [35], while Trappe(2012) affirms that music could be considered the first sensory impression for the child,since the music brings benefits during the pregnant and it has influence in the babies
development [37, 56].
The study of music and music therapy needs to be multidisciplinary as well as theoretically and scientifically pluralistic [22]. To determine the effect of each music will
provoke in a specific person is not trivial. Music emotions are influenced by many factors
such as age, cultural and personality for example. These characteristics make different
people listen to the same music and distinct emotions are stimulated. Thus, therapists
had difficulty selecting the best music for specific patients. For example, to select a music
for a depressed person, who needs to have happiness and positive feeling stimulated, or fora schizophrenic during a crisis, it is not a simple task, considering personal multi factorsthat involve musical emotions.
In this context, an automatic system that evaluates musical attributes in order toclassify music, considering the predominant emotion of each music or genre, without
human intervention, could be an important tool to help in music therapy. This process
could be done analyzing the physical attributes of music.
Physical attributes of music have connection with emotions that are stimulated in
the listener. Some studies that investigated the relation between emotion and music
attributes concluded that emotions can be influenced by such attribute, as time, timbral,harmony and loudness [25] and the attributes related to rhythm and harmony are closer tohuman experience [12, 16]. Aucouturier and Pachet (2003) aim that music that belong to
the same genre share similarities among themselves, which differentiate them from othergenres [3]. While Lin et. al (2009) affirm that genre and emotion provide complementary
descriptions of music content and often correlate with each other. Their study showsthe existence of a correlation between genre and emotions. In addiction, they observed
improvement in the music emotion classification results when the information about thegenre are available [30].
In this paper, we investigate and apply different classification methods in order to automatically classify Latin musical genres and predominant emotions associated to them.
Musical physical attributes (sound beats, timbre and frequency) are considered as inputof the classification methods. Aiming to evaluate classifiers, the performances of classi-

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fication algorithms are analyzed and compared. The classifiers considered in this paper
are: (i) Decision Trees - using CART and C4.5 algorithms, (ii) Random Forest, (iii) kNearest Neighbor (kNN) - using IBK algorithm, (iv) Support Vector Machine (SVM) using Sequential Minimal Optimization (SMO) and (v) Artificial Neural Network (ANN)- using Multilayer Perceptron. The database used is named Latin Music Database [49],from Silla-Jr et al.(2008), which present real numerical features of Latin music. Each
Latin genre is associated with its predominant emotion obtained in the literature andmainly in the work [46], which investigate the emotion of the Latin Music Database.
This paper is organized as follow: Section 2 describes some researches about music
therapy and music classification. Section 3 describes the classifiers used in this paper
for musical genres classification and music emotion recognition. The evaluation of theclassification task is presented in Section 4 and the database as well as the study involving
Latin genre emotions are described in Section 5. In Section 6 the results obtained fromclassification methods considered in this work are presented. Finally, Section 7 commentson the conclusion obtained from the classification results.

2

Related Works

As described before, music is associated with emotion and it can induces reaction andfeelings. Music can not cure a disease, but it can help to relax, to calm and to softnegative effects of various symptoms and diseases, making the healing process faster.
Several publications indicate significant improvements in cure progression even in moreadvanced stages [17, 23, 54].
Many researchers have been studying the effect of music in humans’ memories. The
autobiographical memories count among the most poignant experiences associated withmusic [24]. The musical memory is considered as independent part from other memory
systems [23]. Jacobsen et. al (2015) and Gagnon et. al (2009) studied the influence ofmusic in Alzheimer’s disease, and indicate that musical memory and the music emotion
are preserved and significant improvements has been noticed with the music therapy.
Partanen et. al (2013), investigate the formation and retention of neural representations induced by exposure to melodies during the fetal period [37]. Tabarro et. al (2010)
explore the use of the music during the labor and on the newborn when submitted tothe same melodies heard by their own mothers during pregnancy [53]. In both studies,
positive effects were noticed for both mothers and babies.
Thompson (2013) presents a study of music therapy involving women with breast
cancer, which Decision Tree was used to provide a framework for determining applicableclinical interventions to meet the needs of participants [54]. Cabredo et. al (2012) use anelectroencephalograph to record the reaction of people’s brain while they listen music. The
authors used an implementation of linear regression and the algorithm C4.5 to constructa Decision Tree to build the emotion models for each music emotions [9]. Zhang et. al(2016), propose an automatic emotion recognition system for the music by extracting
different features from the music and machine learning method learning from common
knowledge on emotional state of the trained data. The Random Forest classifier is chosen
to ensemble all these features together and predict the emotion categories of the music [59].

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In Chathuranga et. al (2014), the authors present an ensemble approach for the problem
of automatic music genre classification using SVM and pattern recognition, considering
audio signal [11].
Considering authors that investigate ANN, Mokhsin et. al (2014) detected emotion
features in Malay popular music using ANN by extracting audio timbre features fromboth vocal and instrumental sound clips [33]; while Gerrero-Turrubiates (2014), proposeda music recognition system based on harmonic modification and ANN, this classificationis performed by a feed-forward neural network or Multilayer Perceptron [19].
Others interesting approaches are described in Kothe et. al (2016) and Azarloo et. al(2012), in these papers the authors proposed machine learning models as kNN, ANN and
SVM to detect and distinguish instruments considering different musical genres, basedon music features [5, 27]. Chandwadkar and Sutaone (2013), in their work, classified
four instruments considering piano, acoustic guitar, xylophone and violin. These instruments are identified using various music features and classifiers, Decision trees, k-Nearest
Neighbour classifier, Multilayer Perceptron, Sequential Minimal Optimization Algorithm(SMO) and multi class classifier (meta classifier) [10].
In this paper, we propose an automatic classification of Latin music genres and theirpredominant emotion, applying data mining and statistical technique. In this sense, themain contribution of this paper is to propose a methodology capable of determining thecorrespondent music genre of each music, evaluating multiple music attributes relation tosound beats, timbre and frequency. Furthermore, the predominant emotion of each genre
is considered in this paper and the emotion is associated to the genre (output class of theclassifier). The proposed methodology can be easily used by music therapists helping tochoose the best music for each patient or health treatment.

3

Classifiers

In this section, algorithms of the classification methods employed to classify the Latin
Music Database are described. These algorithms are the more recent techniques presented
in the literature and they have different construction criteria. All of them are available infree software WEKA∗ , developed by the University of Waikato, New Zealand. Thus, it ispossible to compare the performance of different classification algorithms, since they are
implemented using the same platform.
The use of different classifiers allow us to compare methodologies. The algorithms
present different ways to extract information from database and to transform it into
knowledge. For this reason, depending on the metric used by an algorithm, the results
can be different.
In order to compare the performance of different classification methods, “Black Box”
and “White Box” methods were chosen to classify Latin musical genres database. The
Black Box methods can be viewed in terms of its inputs and outputs, without any knowledge of its internal workings. The opposite of them, is the White Box, which is a system
where the inner components are available for inspection. White Box algorithms reveal thestructure, allowing users to assemble algorithms from algorithm building blocks. Among∗(Waikato Environment of Knowledge Analysis - http://www.cs.waikato.ac.nz)

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them, in this paper, Artificial Neural Networks, k-Nearest Neighbor and Support Vector
Machines are Black Box methods, in the sense of users can not read the acquired knowledge in a comprehensible way, and Decision Tree and Random Forest are White Box
methods. The latter ones generate understandable rules. So, users can understand eachsplit, see the impact of that split and even compare it to alternative splits [29].

3.1

Decision Trees

Decision tree is a very useful data mining technique to extract information of a data set.
This process is done by a TDIDT (Top-Down Induction Decision Tree) algorithm [45],which induces a tree structure by splitting data into subgroups on divide and conquer
method [28]. A Decision Tree consists of a data set, partitioned into groups known as
nodes. The top node is called root node, which is selected using some attribute selection
measure or splitting criterion. Under the root node are the internal nodes, originatingfrom the division of the data set; they constitute the tree branches. At the end of eachbranch is the terminal node, designed leaves, which represent the most appropriated classfor the rule. One rule is composed by each terminal node, plus the internal nodes thatbelongs to one specific branch and the root node. The rules are describe by a IF-THENmodel (“IF attribute w is y1 AND attribute x is y2 THEN the class is z”). These rules
are used to classify unknown data at the inference process.
In order to construct the Decision Tree models, in this paper we employed the mostcommons algorithms to induce trees: CART (Classification and Regression Tree) [8] andC4.5 [41]. These algorithms construct the tree in two phases: growing and pruning. The
growing is a recursive process that establishes the structure of the Decision Tree according
to some splitting criterion, such as Information Gain, Gini Index, Twoing, and Gain Ratio.
The mathematical formulation of C4.5 and CART can be seen in [45].
CART algorithm is characterized by the fact that it constructs binary trees, namelyeach internal node has exactly two outgoing edges [45]. The splitting criteria is named
Twoing, that search for two classes that will make up together more than 50% of the data[55]. While C4.5 algorithm uses Gain Ratio as splitting criteria. The element with highest
Gain Ratio is taken as the root node and data set is split based on the root element values[38].
The second phase, pruning, is responsible to reduce the complexity of the tree. Itmeans reducing size of the tree that are too large and deep. The problem of noise andoverfitting reduces the efficiency and accuracy of data. The overfitting happens when thetree lost the ability of generalizing to instances not present during the training process.
By increasing the number of nodes, the training error usually decreases while at somepoint the generalization error becomes worse [45].
Pruning methods typically use statistical measures to remove the least reliable branches[39]. As a consequence it optimize the computational operation, eliminate overfitting, andimprove the classification of unknown data. The pruning techniques varies according to
the Decision Tree algorithm, Cost-Complexity Pruning, Error Based Pruning, Reduced
Error Pruning and Minimum Error Pruning are some examples that can be used to prune
trees. Their mathematical formulation can be seen in [45].
CART uses the Cost-Complexity to prune the tree, this method proceeds in two stages:

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in the first stage, a sequence of trees T0 , T1 , ..., Tk is built on the training data where T0
is the original tree before pruning and Tk is the root tree and in the second stage, one ofthese trees is chosen as the pruned tree, based on its generalization error estimation [45].
The strategy used by C4.5 named Error Based Pruning it is an evolution of other method
called Pessimistic Pruning. C4.5 equates the predicted error rate at a leaf with this upper
limit, on the argument that the tree has been constructed to minimize the observed error
rate [42]. More details about the prune methodology and the algorithm used to prune
could be seen in [45, 39].

3.2

Random Forests

Random Forests or Random Decision Forests are an ensemble learning method for classification that construct a multitude of Decision Trees at training time and outputting theclass that is the mode of the classes of the individual trees. Due to their power, versatility,and ease of use, Random Forest are quickly becoming one of the most popular machinelearning methods [28]. Random Forest classifiers incorporate the principles of bagging(multiple drawing of samples with replacement) and random feature selection (finding
the most discriminating features from a randomly selected subset of features) for learning
the Decision Trees [26].
A Random Forest consists of multiple Decision Tree and predicts the class that is
obtained by voting on the predictions made by the individual tree (ensemble classifier) [26].
An ensemble combines a series of n learned models (or base classifiers), M1 , M2 , ..., Mn
with the aim of creating an improved composite classification model, M∗ . A given dataset, D, is used to create n training sets, D1 , D2 , ..., Dn , where Di (1 ≤ i ≤ n − 1) is used
to generate classifier Mi . Given a new data tuple to classify, the base classifiers each voteby returning a class prediction. [20].
In order to grow ensembles, often random vector are generated that govern the growthof each tree in the ensemble. Bagging [6], Random split [14] and Random subspace [15] aresome examples of split selectors. According to Breiman (2001) the common element in allof these procedures is that for the kth tree, a random vector Θk is generated, independentof the past random vectors Θ1 , ..., Θk−1 , but with the same distribution; and a tree isgrown using the training set and Θk , resulting in a classifier h(x, Θk ) where x is an input
vector. After a large number of trees is generated, each tree casts a unit vote for the mostpopular class at input x [7].
The accuracy of a Random Forest depends on the strength of the individual classifiers
and a measure of the dependence between them. The ideal is to maintain the strength ofindividual classifiers without increasing their correlation. Random Forest are insensitive tothe number of attributes selected for consideration at each split [20]. The generalization
error for a forest converges as long as the number of tree in the forest is large. Thus,
Random Forest are less prone to overfitting [20, 28].

3.3

Support Vector Machines

A Support Vector Machine (SVM) is a binary classification method that divides the given
data into two groups in the best possible way by using hyperplanes. This method is based

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on a structural risk minimization method to reduce the error rather than the empiricalrisk minimization method used in traditional statistical learning theory [29]. Without
any knowledge of the mapping, the SVM finds the optimal hyperplane by using the dot
product functions in feature space using kernel functions. The solution of the optimal
hyperplane can be written as a combination of a few input points that are called support
vectors [30].
Training a SVM requires the solution of a very large quadratic programming optimization problems. The Sequential Minimal Optimization (SMO) [40] is an analytical form tosolve the problem of quadratic optimization programming.
The SMO decomposes the overall quadratic programming in quadratic sub-problems,
using Osuna’s Theorem [36] to ensure converge, without any extra matrix storage or using
numerical quadratic programming optimization steps. SMO chooses to solve the smallest
possible optimization problem at every step. For the standard SVM quadratic problem,the smallest possible optimization problem involves two Lagrange multipliers, because theLagrange multipliers must obey a linear equality constraint. At every step, SMO chooses
two Lagrange multipliers to jointly optimize, finds the optimal values for these multipliers,
and updates the SVM to reflect the new optimal values [40].
SMO works fastest for linear SVMs and sparse data sets, as well as binary data andnon-linear data [40]. This algorithm are able to work with missing values are replaced
globally, nominal attributes are transformed into binary ones, and attributes are normalized by default [58]. The advantage of SMO lies in the fact that solving for two Lagrange
multipliers can be done analytically. SMO requires no extra matrix storage at all. Thus,
every large SVM training problems can fit inside of the memory of an ordinary personal
computer or workstation, it is less susceptible to numerical precision problems [40]. The
amount of memory required for SMO is linear, which allows SMO to handle very large
training sets.

3.4

k-Nearest Neighbors

According to [20], the k-Nearest Neighboor (kNN) classifiers are based on learning by
analogy, that is, by comparing a given test tuples with training tuples that are similar toit. The training tuples are described by n attributes. Each tuple represents a point in an
n-dimensional space. In this way, all the training tuples are stored in an n-dimensional
pattern space. When given an unknown tuple, a k-Nearest Neighboor classifier searches
the pattern space for the k training tuples that are closest to the unknown tuple. Thesek training tuples are the k “nearest neighbors” of the unknown tuple. The closenessis defined in terms of a distance metric, such as Euclidean distance. The Euclidean
distance between two points of tuples, which is commonly used in kNN algorithm could
be described by: considering
X1 = (x11 , x12 , ..., x1n ) and X2 = (x21 , x22 , ..., x2n ), thenvu nuX
dist(X1 , X2 ) = t(x1i − x2i )2 .
i=1

In other words, for each numeric attribute, we take the difference between the corre-248
sponding values of that attribute in tuple X1 and in tuple X2 , square this difference, andaccumulate it. The square root is taken of the total accumulative distance count [20].
Specify the value o k, is not a trivial task. The value of k determines how well themodel will generalize to future data [28]. So, choosing a large k reduces the impact orvariance caused by noisy data, but can bias the learner such that it runs the risk of ignoringsmall, but important patterns. On the other hand, using a single nearest neighbor allows
noisy data or outliers, to unduly influence the classification of examples. One common
practice is to set k equal to the square root of the number of training examples, but
normally k is set somewhere between 3 and 10 [28]. For this reason some authors suggest
test k from 1 to 10 to find the best k.
The Based Instance Algorithm (IBK) is based on instances (instance based algorithm)[1]. This algorithm use the technique of “Nearest Neighbor” to classify each new instance.
This methodology is known by lazy learner since training consist only of storing the given
training examples alongside the class label [26].
In the IBK, the value of k can also be determined automatically using leave-one-out
Cross-Validation, subject to an upper limit given by the specified value. Predictions from
more than one neighbor can be weighted according to their distance from the test instance,
and two different formulas are implemented for converting the distance into a weight. The
number of training instances kept by the classifier can be restricted by setting the window
size option. As new training instances are added, the oldest ones are removed to maintainthe number of training instances at this size [58].

3.5

Artificial Neural Network

An Artificial Neural Network (ANN) models the relationship between a set of input signals
and an output signal using a model derived from our understanding of how a biologicalbrain responds to stimuli from sensory inputs. ANNs are best applied to problems where
the input data and output data are well-understood or at least fairly simple, yet theprocess that relates the input to output is extremely complex [28].
A neural network performs pattern recognition by first undergoing a training session
during which the network is repeatedly presented with a set of input patterns along withthe category to which each particular pattern belongs. Later, the network is presented
with a new pattern that has not been seen before, but which belongs to the same population of patterns used to train the network. The network is able to identify the classof that particular pattern because of the information it has extracted from the training
data. Pattern recognition performed by a neural network is statistical in nature, with thepatterns being represented by points in a multidimensional decision space. The decision
space is divided into regions, each one of which is associated with a class. The decisionboundaries are determined by the training process. The construction of these boundaries
is made statistical by the inherent variability that exists within and between classes [21].
When the classes are linearly separable, we could use a simple perceptron. However
when the problem is not linearly separable, the simple perceptron algorithm will fail togenerate a separating hyperplane (in this two-dimensional instance space a hyperplane isjust a straing line), in this case Multilayer Perceptron are used. Multilayer Perceptrons
are usually trained by minimizing the squared error of the network’s output, essentially249
treating it as an estimate of the class probability [58].
According to Haykin (2009), a popular method for the training of Multilayer Perceptrons is the Backpropagation algorithm, which includes the least-mean-square algorithm
as a special case. The training proceeds in two phases: forward and backward, which aredescribed below.
• In the forward phase, the synaptic weights of the network are fixed and the input
signal is propagated through the network, layer by layer, until it reaches the output.
Thus, in this phase, changes are confined to the activation potentials and outputs of
the neurons in the network. A pattern is presented to the units in the input layer,
and from this layer, the units calculate their response that is produced in the output
layer.
• In the backward phase, an error signal is produced by comparing the output of the
network with a desired response. The resulting error signal is propagated throughthe network, again layer by layer, but this time the propagation is performed in
the backward direction. In this second phase, successive adjustments are made to
the synaptic weights of the network. Calculation of the adjustments for the output
layer is straightforward, but it is much more challenging for the hidden layers. The
network learns the rules necessary to work with the knowledge, by adjusting theweights and to minimize the error between the actual data and the calculated by
the network.
Since Backpropagation requires a known, desired output for each input value, it isa supervised learning method. It calculates the error contribution of each neuron after
a batch of data is processed. This is used by an enveloping optimization algorithm toadjust the weight of each neuron, completing the learning process for that case [21]. It
calculates the gradient of the loss function. This function maps values of one or morevariables onto a real number intuitively representing some cost associated with thosevalues. The loss function then calculates the difference between the network output andits expected output. It is used in the gradient descent optimization algorithm. The error is
calculated at the output and distributed back through the network layers. After training,
the network can be used as a tool to classify new data. For this, it should only be used inprogressive mode (feed-forward),i.e., new entries are inserted, processed in intermediate
layers and the results are presented in the output layer, but without the backpropagationof the error. More details about ANN theory and Backpropagation algorithm can be seenin [21].

4

Evaluation of Classification Methods

Evaluating the performance of a classification is a fundamental aspect of machine learning.
In this work, Cross-Validation, Receiver Operating Characteristic and Confusion Matrix
are applied as efficient indicators for assessing the quality of the analysis results.
In s-fold Cross-Validation, the data is randomly split into s mutually exclusive subsets
of approximately equal size. An inducer is trained and tested s times; each time it is tested

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on one of the n folds and trained using the remaining s − 1 folds. The advantage of thismethod is that it matters less how the data gets divided. Every data point gets to be ina Test Set exactly once, and gets to be in a training set s − 1 times. The variance of theresulting estimate is reduced as s is increased. Therefore, the basic idea is that some ofthe data is removed before training begins. Then, when training is done, the data thatwas removed can be used to test the performance of the learned model on “new” data.
The Confusion Matrix is used as an indication of the properties of a classification
rule. It contains the number of elements that have been correctly or incorrectly classified for each class. The main diagonal presents the number of observations that havebeen correctly classified for each class. The off-diagonal elements present the number ofobservations that have been incorrectly classified [45].
The Confusion Matrix M (Ci , Cj ), for i, j = 1, ..., k, classes provides an effective measure of the classification model performance, since it shows the number of elements correctly classified and predict, for each class. Let T be a set of examples and h the hypothesis. The Confusion Matrix of h is given by:X

M (Ci , Cj ) =k h(x) = Cj k∀(x,y)∈T :y=ci

where Ci is the true class and the Cj is the predict class. Table 1 illustrates the Confusion
Matrix.

Class
True C1
True C2..
.

Table 1: Confusion Matrix
Predict C1 Predict C2 . . .
M (C1 , C1 ) M (C1 , C2 ) . . .
M (C2 , C1 ) M (C2 , C2 ) . . .
..
..
.
.

Predict Ck
M (C1 , Ck )M (C2 , Ck )..
.

True Ck

M (Ck , C1 )M (Ck , Ck )M (Ck , C2 )...

Receiver Operator Characteristic (ROC) curve, or ROC curve, is commonly used topresent results for decision problems in machine learning. It examines the tradeoff between
the detection of true positives, while avoiding the false positives. Curves are defined ona plot with the proportion of true positives on the vertical axis, and the proportionof false positives on the horizontal axis. These values are equivalent to sensitivity and
1−specificity, respectively. The closer the curve is to the perfect classifier, the better it is
at identifying positive values. This can be measured using a statistic known as the area
under the ROC curve, which measures the total area under the ROC curve. The area
ranges from 0.5 (for a classifier with no predictive value), to 1.0 (for a perfect classifier).
The points comprising ROC curves indicate the true positive rate at varying false
positive thresholds. To create the curves, a classifier’s predictions are sorted by themodel’s estimated probability of the positive class, with the largest values first. Beginningat the origin, each prediction’s impact on the true positive rate and false positive rate
will result in a curve tracing vertically (for a correct prediction), or horizontally (for an
incorrect prediction), according to [28].

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5

Database and Musical Emotions

In this section, the music database is described, as well as the procedure to collect thenumerical input attributes. Besides, the musicology of the musical genres considered inthis paper is presented in order to enable the association with the emotions prevalent ineach one of them.

5.1

Latin Music DatabaseIn this paper, we used a set of the available music in Latin Music Database, from [49], a3000 musical recordings database from the ten different genres: Tango, Salsa, Forró, Axé,
Bachata, Bolero, Merengue, Gaúcha, Sertanejo and Pagode. This database is organized
in a matrix, where the row are called “instance” and contains the numerical information
about the attributes and it corresponds to one genre; and column represents 30 input
attributes, that represent the attributes extracted from the music; these attributes are
better described in Table 2. The MARSYAS framework was employed for feature extraction [57]. The attributes employed in this paper comprise short-time Fourier transform,
Mel frequency cepstral coefficients (MFCC), beat and pitch related features, inter-onset
interval histogram coefficients, rhythm histograms and statistical spectrum descriptors.
More details can be seen in [50]. The attributes can be split into three groups: thebeat-related attributes (attributes 1 to 6) include the relative amplitudes and the beats
per minute. Timbral texture attributes (attributes 7 to 25) account for the means andvariance of the spectral centroid, rolloff, flux, the time zero domain crossings, the first five
MFCCs and low energy. Pitch-related attributes (attributes 26 to 30) include the maximum periods and amplitudes of the pitch peaks in the pitch histograms. The attribute
vector is outlined in Table 2.

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Table 2: Description of the attribute vector
Attributes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

5.2

Description
Relative amplitude of the first histogram peak
Relative amplitude of the second histogram peak
Ratio between the amplitudes of the second peak and the first peak
Period of the first peak in bpm
Period of the second peak in bpm
Overall histogram sum (beat strength)
Spectral centroid mean
Spectral rolloff mean
Spectral flow mean
Zero crossing rate mean
Standard deviation for spectral centroid
Standard deviation for spectral rolloff
Standard deviation for spectral flow
Standard deviation for zero crossing rate
Low energy
First MCFF mean
Second MCFF mean
Third MCFF mean
Fourth MCFF mean
Fifth MCFF mean
Standard deviation for first MFCC
Standard deviation for second MFCC
Standard deviation for third MFCC
Standard deviation for fourth MFCC
Standard deviation for fifth MFCC
The overall sum of the histogram (pitch strength)
Period of the maximum peak of the unfolded histogram
Range of the maximum peak of the folded histogram
Period of the maximum peak of the folded histogram
Pitch interval between the two most prominent peaks of the folded histogram

Musicology

In this subsection, a literature review that associates Latin musical genres with respective
predominant emotion is presented. Specific books and publications about Latin music
was consulted in order to find characteristics of each genre considered in this paper [2, 4,
13, 32, 43, 44, 46, 47, 48, 51, 52].
Tango: This musical genre has melancholic characteristics. The lyrics usually present
an unmatched love as central theme, referring to the past, to the lost youth, then producing sadder emotions, combining drama, passion, sensuality and aggressiveness [51, 52].

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Bachata: It is a genre that deals with love and suffering. Its origins refer to ruraland poor communities in the Dominican Republic. Love, lack of love and disappointment
are frequently sung in Bachata. José Manuel Calderón, considered the pioneer of Bachatamusic, affirms that this musical genre refers to “feeling”, capable of rising not only bitterness and disaffection, but also a range of emotions, which become a true message: “TheBachata sings love” [48].
Bolero: It is considered a romantic genre. It involves subjects like love among humanbeings, betrayal and unmatched love. Commonly the lyrics present a central theme thattalk about a problem or an amorous intention [2, 52].
Merengue: It is considered the national dance of the Dominican Republic, Merengue
rised in the Creole culture. It is a simple and engaging dance with sexual connotations, inwhich dancers interweave during the choreography. The lyrics usually talk about subjects
like love and passion, in joyful and seductive way [4, 52].
Salsa: This genre instigates the idea of a music with “taste” [44]. It is dancing genre.
Normally the lyrics talk about a seducer man, characterized by the pleasure and sexyexpressions [52].
Forró: It started with the Brazilian musician Luiz Gonzaga, after his rhythm “Baião”.
Gonzaga wanted “to sing the Brazilian Northeast”, talking about the man who left his
land due to drought and the financial difficulties to live large urban centers [47].
Pagode: Pagode is a kind of samba, a popular Brazilian genre. The lyrics talk about
daily activities. It is common in poor or traditional “samba communities” in Rio deJaneiro. Popularly, the Pagode is dancing and singing in joyful cluster of people [32].
Sertanejo: It is a common genre in countryside of Brazil, popular sung by duets ortrios of musicians with strong connection of countryside. Its lyrics talk about arduous lifeof country people, as rural work, love suffered, or when the countryman is forced to moveto a city, leaving his roots [43].
Gaúcha: This genre is popular in the South of Brazil with strong influence of Italians
and Germans rhythms. It contemplates joy and theatricality, describing the country life
[13].
Axé: This genre appeared in the state of Bahia, in Brazil, during the carnival party.
It is considered a mix of African and Brazilian rhythms characterized by a cheerful,
contagious, enthusiastic and very popular in Brazilian Carnival [32].
In Santos and Silla-Jr (2015), musical genres that compose the Latin Music Database
was analyzed by professionally trained Brazilian musicians who indicate the predominantemotion perceived in each music [46]. In addiction, after a large literature review, theemotions associated to the musical genres are determined and the results are presented
in Table 3.

6

Results

In order to classify the Latin musical genres, the performances of the classifiers describedin Section 3 were investigated, by executing them for the same database.
The Latin Music Database, is compose by 30 input attributes, described in Table
2, and the 31th column contains the outputs, which are Latin genres. There are 3000

254

Table 3: Emotions associated to musical genres
Genre
EmotionGenre
EmotionTango
Deception
Forró
Welcoming
BachataLove
PagodeHappiness
BoleroRomantic Sertanejo
Sadness
Merengue
PassionGaúchaJoyfulSalsa
Seduction
Axé
Enthusiastic

instances (rows of the matrix), being 10 instances of each one of the ten genres.
In order to compare the numerical and categorical data results, firstly the attributes
were categorized according to tercile. Then, each input attribute is divided in 3 categories(low, medium and high), with 1000 values in each category.
The considered measures of models validation are: Test Set and Cross-Validation.
For the Test Set, the database is partitioned in training set and Test Set. The training
set is composed by 80% of full database (2400 instances) which is used to generated themodels; and the Test Set, composed by 20% remaining data, is used to measure the abilityof generalization of the generated models for unknown data. For the Cross-Validation, we
consider n=30 as described in the Section 4. Cross-Validation uses the full data, however
there is replacements of data subsets to test, during the testing process.
The results obtained from classifiers execution, considering numerical data, are presented in Table 4 and 5 present the results obtained from categorical data. The column
named “accuracy”, in Tables 4 and 5, is defined as the number of instance correctly classified divided by the instances total. Accuracy values are provided by Confusion Matrices,since the main diagonal indicates the number of genres correctly classified. As describedin the Section 4, the higher the values are concentrated in main diagonal, the better theclassifier is.
The area under the ROC curve are also used as evaluation measure. As describe in
Section 4, the closer the curve is to perfect classifier, the better it is at identifying positive
values. Thus the closer to 1, the better the classification is.
Table 4: Numerical data results
Test Set
Cross-Validation
Classifier
Accuracy ROC Accuracy ROC
C4.5 (pruning)
62.33 0.811 61.03 0.797
C4.5 (no pruning)
62.00 0.811 60.47 0.798
CART (pruning)
62.83 0.868 64.20 0.869
CART (no pruning)
60.17 0.804 63.10 0.812
Random Forest
78.00 0.972 80.33 0.972
Multilayer Perceptron 74.33 0.951 76.23 0.956
SMO
72.33 0.933 74.37 0.936
IBK (k=6)60.33 0.882 63.17 0.889

255

Table 5: Categorical data results
Test Set
Cross-Validation
Classifier
Accuracy ROC Accuracy ROC
C4.5 (pruning)
56.83 0.783 56.93 0.779
C4.5 (no pruning)
55.00 0.776 55.87 0.779
CART (pruning)
58.00 0.863 60.43 0.857
CART (no pruning)
57.50 0.773 57.60 0.785
Random Forest
73.83 0.955 73.37 0.972
Multilayer Perceptron 71.00 0.934 69.40 0.936
SMO
68.17 0.921 69.77 0.927
IBK (k=6)64.50 0.921 68.90 0.932

As described in Section 3, Decision Trees models could be constructed using or not thepruning methods. The pruning process optimizes the computational operation, eliminates
the overfitting and improves the classification of unknown data. In Table 6, measures ofcomplexity of each Decision Tree model generated is presented. The complexity of thetrees can be indicated by the number of leaves and the size of the tree.

Table 6: Measures of complexity of the Decision Trees algorithms
Algorithms
Test Set Cross-Validation
Numerical Data Leaves Size Leaves
Size
CART(pruning)
58
115
67
133
CART(no pruning)
239
585
341
681
C4.5 (pruning)
301
601
364
727
C4.5 (no pruning)
310
619
379
757
Categorical Data Leaves Size Leaves
Size
CART (pruning)
55
109
109
217
CART (no pruning) 385
769
485
969
C4.5 (pruning)
368
735
475
949
C4.5 (no pruning)
433
865
547
1093
Considering the models constructed using the kNN algorithm, different values for k
were tested, varying k from 0 to 10. The best results are provided using Euclidean
distance, with k = 6, which is nearly to the square root of the number of the attributes(30 attributes), as suggested in the literature.
As we can notice in Tables 4 and 5, the best accuracy and the best area under the
ROC curve are presented by the models generated using the Random Forest. For thisreason, Tables 7 to 10 present the confusion matrices for the Random Forest models. The
Tables 7 and 9 correspond to Test Set, thus, the matrices must contain 600 data samples.
And the Tables 8 and 10 consist of the results from Cross-Validation, which contains the3000 the full dataset.

256

Table 7: Confusion Matrix using Test Set and numerical dataGenrea b c d e f g h i j
Tango =a 60 0 0 0 0 0 0 0 0 0
Bachata =b 0 58 0 0 2 0 0 0 0 0
Bolero =c 2 1 43 0 3 3 2 3 3 0
Merengue =d 0 2 0 53 1 0 1 0 2 1
Salsa =e 0 3 0 3 44 2 2 1 2 3Forró =f 0 0 4 1 5 40 7 2 1 0
Pagode =g 0 0 5 1 0 1 46 3 1 3
Sertanejo =h 0 0 5 0 1 4 1 42 2 5Gaúcha =i 1 0 4 1 4 4 2 0 41 3
Axé
=j 0 2 0 2 3 2 4 1 5 41

Table 8: Confusion Matrix using Cross-Validation and numerical dataGenrea b c d e f g h i j
Tango =a 290 0 10 0 0 0 0 0 0 0
Bachata =b 0 283 2 4 7 1 0 0 3 0
Bolero =c 8 1 239 0 9 5 11 23 4 0
Merengue =d 0 7 0 277 2 3 3 1 4 3Salsa =e 0 2 10 6 243 11 5 4 17 2Forró =f 0 5 15 8 16 182 22 19 19 14
Pagode =g 0 0 8 1 6 9 249 6 9 12
Sertanejo =h 0 0 31 0 2 17 20 196 10 24
Gaúcha =i 3 2 15 4 10 16 8 13 218 11
Axé=j 0 5 5 10 3 9 7 8 20 233

Table 9: Confusion Matrix using Test Set and categorical dataGenrea b c d e f g h i j
Tango =a 57 0 3 0 0 0 0 0 0 0
Bachata =b 0 56 0 2 2 0 0 0 0 0
Bolero =c 5 0 38 0 2 2 7 4 2 0
Merengue =d 0 1 0 55 0 0 1 0 2 1
Salsa =e 1 2 2 3 42 1 1 2 2 4Forró =f 1 0 4 1 2 33 12 4 1 2
Pagode =g 0 1 5 1 0 1 42 5 2 3
Sertanejo =h 1 0 6 0 2 1 4 35 0 11
Gaúcha =i 2 0 2 1 2 2 1 2 45 3
Axé
=j 0 3 0 3 4 0 2 5 3 40

257

Table 10: Confusion Matrix using Cross-Validation and categorical dataGenrea b c d e f g h i j
Tango =a 276 0 24 0 0 0 0 0 0 0
Bachata =b 0 277 4 9 5 4 0 0 1 0
Bolero =c 11 4 226 0 12 4 15 20 6 2
Merengue =d 1 13 0 264 4 1 4 1 6 6Salsa =e 1 11 20 14 208 9 9 7 15 6Forró =f 2 7 23 11 18 142 36 24 20 17
Pagode =g 0 2 12 4 8 10 224 11 15 14
Sertanejo =h 1 1 34 1 8 18 21 177 11 28
Gaúcha =i 12 6 15 6 14 9 8 12 201 17
Axé=j 1 8 3 13 11 8 12 17 21 206

7

Conclusion

In this paper, we execute and discuss the results and the performance obtained fromdifferent classifiers for the musical genre classification task and music emotion recognition.
From the obtained results, it is possible to conclude about the classifier that provided thebest classification results and then it can be used by music therapists, who must select a
determined musical genre for a specific patient, by considering the predominant emotionof each genre.
For the classification task, White Box and Black Box classifiers were considered, since
they provide different classification models and results, which make possible to investigate
their performances.
Analyzing the results presented in Tables 4 and 5, the higher accuracy and the higher
area under the ROC curve are provided by Random Forests models. Due to this method
deal with ensemble, it works better using large number of trees, consequently, large data
sets. The Confusion Matrices can confirm this fact, since the higher values are concentrated in the main diagonal, indicating the excellent performance of the model.
The overffiting can be a problem for Decision Trees, but not for Random Forests
classification method, since it consists of multiple Decision Trees and predicts the classthat is obtained by voting on the predictions made by the individual trees. The most
voted genre is elected.
The advantage of using White Box methods, such as Decision Trees and Random
Forest, is the possibility to know the relationship between the input attributes and toobtain more information about the behavior of the data. Besides, the “IF-THEN” rules
set, which is extracted from the classification model, is closer to the human understanding.
Neural networks that use Backpropagation can be seen as Black Box models, since
the ways that network uses to obtain a specific result are unknown. A limitation of use
refers to training time, which tends to be very slow. Thousands of cycles can be needed toreach acceptable levels of error, especially if serial computers are used in the simulation,
since the processing unit must calculate the functions for each unit and its connections
separately, which can be problematic for networks with a large amount of data.

258
There are no rules to define how many units must exist in intermediate layers, howmany layers, or how the connections between these units should be. The processing is slow,
however, for the data set used in this work, the neural network presents the second best
accuracy, especially with numerical data, since it presents a larger amount of information
to do the training process.
Tables 4 and 5 also show that the categorical data presented better results than numerical data when using kNN classifier, with IBK algorithm. This algorithm generated
best results using Euclidean distance, with k = 6. The reason is because kNN classifier
for categorical data uses only 3 classes to partitioning the categories, while for numerical
data, it uses 10 classes (the 10 musical genres). Then, kNN presents higher accuracy for
categorial data.
Table 6 presents the number of leaves and the size of each tree generated by Decision
Tree models. When a prune process is applied, the complexity of the tree is reduced,
consequently, the computational cost is also reduced. The prune process makes the trees
easier to be interpreted without impact in the accuracy of the models. According toTables 4 and 5, CART algorithm with pruning presents higher accuracy and higher area
under ROC curve than other Decision Trees algorithms.
Despite of Decision Trees not present the best accuracy in relation to the other classifiers, the Decision Trees are the clearest models for to human understanding. These
rules can provide important information for the musicotherapists, who will have an extra
knowledge to guide them in the selection of the most appropriated music for the patient.
An example of Decision Tree rule, from the CART model, extracted from Test Set validation, is “IF attribute 6 is greater than or equal to 0.067 AND attribute 13 is less than
0.464 AND attribute 8 is less than 0.276 AND attribute 11 is greater than or equal to0.024 AND attribute 28 is less than 0.121 THEN the genre is Bolero”. Thus, considering
Table 3, the predominant emotion of Bolero genre is Romantic. For CART categoricalmodel, an example of Test Set rule is “IF attribute 13 is low or medium AND attribute
16 is high or medium AND attribute 9 is medium or low AND attribute 6 is high AND
attribute 21 is low AND attribute 18 is low or medium AND attribute 5 is low or high andattribute 28 is medium THEN the genre is Sertanejo”. Thus, the predominant emotion
is Sadness, according to Table 3. The number of the attributes can be checked in Table2.
It is important to highlight that all classification models provided good results, accuracy higher than 60% and the area under ROC curve higher than 0.77. Then, all of them
could be applied to classify new data and generate acceptable results. For this case study,
Random Forest method presented the best results for the classification task, obtaining
0.972 for the area under ROC curve and 80.33% of accuracy for numerical data. Random
Forest is a “White Box” method, as explained above, however, it does not generates a
final tree from which a set of rules could be extracted.

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