Identifying dimensions for performing an emotional analysis of classical music. Further, experiments to determine features that matter for emotional classification and clustering of Classical Music.

1. CS6998: Computational Approaches to Emotional Speech, Fall 2009
EMOTIONAL ANALYSIS OF CARNATIC MUSIC:
A MACHINE LEARNING APPROACH.
Ravi Kiran Holur Vijay Arthi Ramachandran
Abstract 1.1 Outline
Carnatic music or South Indian Classical We will start off by saying a few words about
Music is a traditional form of music, which the type of music we would be working with:
originated from and was popularized by the Carnatic music. We will then move on to
South Indian states. Our goal was to create a describe the problem we are trying to address
framework for performing emotion based and some of the underlying assumptions.
Music Information Retrieval of Carnatic Next, we will take a look at some past
music. As part of this, we chose to explore a research in the field of emotional analysis of
couple of the problems which are part of the music and explore how we can adapt this for
larger framework. In the first problem, we try our analysis. After this, we will start
to predict the magnitude of the various investigating one of the core problems -
emotional dimensions the song might induce precisely representing the emotions associated
on the listener. We use an expert system with a song. We do this by introducing the
approach, wherein we assume that the novel concept of emotion dimensions. With
emotions identified by an expert listening to the results of cluster analysis, we see that the
the song would be the same as those emotion dimension-based approach has some
experienced by the actual listener. Thus, the validity in the context of Carnatic music.
expert's annotation is considered to be the Later, we examine, in detail, the process of
gold standard. The subsequent problem would preparing the corpus for annotation by experts
be to cluster emotionally similar songs using and also re-examine the results of annotation
various Clustering techniques available. By with emotion dimensions, including the inter-
analyzing these clusters, we can suggest songs rater agreements. This stage poses a challenge
with similar emotional profile. in itself due to the need to communicate with
experts, who are Artists, about our
1. Introduction requirements in a precise manner. Also, we
Carnatic music, South Indian Classical Music had to make the process as simple as possible
is a very structured style of music. Every piece for the Experts to annotate the corpus. Once
is associated with a certain raaga or melody the corpus is annotated, we then move on to
which is, in turn, associated with a set of training classifiers for predicting the values
emotions. However, many pieces are for each of the emotion dimensions. Here, we
associated with multiple emotions rather a will explore segmentation of songs and also
single emotion. In this paper, we are extracting various features for the
evaluating the use of 10 dimensions to classification task. We analyze the relative
represent the emotions in Carnatic music. importance of each feature set for predicting
Music similarity is a problem explored in the emotional dimensions. We then explore
other genres of music with applications such ways to cluster songs based on their emotional
as music database searching. Here, we are similarity using Clustering techniques.
using emotional features to evaluate similarity Finally, we conclude by looking at the results
in songs. achieved and commenting on the possible
improvements in future.
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2. Emotional Analysis of Carnatic Music: A Machine Learning Approach
uses samples from a custom-created corpus.
1.2. Related Research Here the samples are selected keeping the
There have been various attempts for Raaga constant and varying the type of music
exploring ways to predict the emotions like Sitar, Sarod, Vocal (male and female).
induced by music using computational
techniques. 1.3. Carnatic Music
Yang, Lin, Su and Chen (2008) as well as Carnatic music is a form of classical music
Han, Rho, Dannenberg and Hwang (2009) prevalent in the Southern part of India. It has
explore regression-based approaches for mythological roots and its history dates back
predicting emotions induced by music. They to ancient times, with it evolving over the
consider 12 and 11 emotions respectively, centuries. The form of music is very
derived from Juslin’s theory theory and are structured and stylized with precise
distributed over a 2 dimensional space that combinations of notes allowed in each
uses Thayer’s emotion model. Each instance composition.
corresponds to a point in this 2-dimensional
space, the coordinates of which are predicted 1.3.1. Raagas and Rasas
using regression approaches. Han et al use One of the fundamental concepts of Carnatic
music samples from an online songs database. music is that of a Raaga. Every composition is
The samples are labeled according to a set to a particular raaga. The raaga of a song
taxonomy used by the database and these are characterizes its melody. Each piece in a
later transformed into labels for the 11 certain raaga uses a certain set of notes. There
emotions they used. They pick 15 songs for are also rules which govern how the notes
each of the 11 emotions they considered. interact with each other and which ones are
Trohidis, Tsoumakas, Kalliris and Vlahavas prominent in the piece. Another element of a
(2008) and Wieczorkowska, Synak and Ras raaga is the rasa or emotion.
(2006) explore multi-label based classification
approaches for predicting emotions induced Carnatic music theory tells that there are nine
by music. Trohidis et al. (2008) consider 6 rasas or emotions conveyed by the music
emotions which are derived from Tellegan- style. Certain raagas are associated with
Watson-Clark model of mood. Each instance particular rasas. For example, Hamsadhwani is
can correspond to more than one emotion in associated with joy while Kaanada is
this approach. They use music samples from a associated with love
custom-created corpus. Here the samples are (http://www.karnatik.com/rasas.shtml). Often,
selected based on different genres. Experts bhakthi, or devotion, is considered another
were asked to annotate the corpus with rasa. The nine rasas, used in Carnatic Music
emotional labels in this case. Also, they theory are shown in Table 1.
consider 30 sec segments of each sample for
the feature extraction task. avarasa Meaning
In his work, Chordia, (2007) tries to shringara romance/beauty
empirically establish the relationship between
Raagas and emotions induced on the listeners hasya comic/happiness
in Hindustani or North Indian Classical Music. karuna pathetic/sad
He considers 12 discrete emotions, 4 of which rudra angry
are among the traditional emotions used in vira heroic
classical theory. Each instance can correspond bhayanaka ridden by fear
to more than one of these 12 emotions. He bibhatsa disgust
adbhuta surprise/wonder
2

3. Emotional Analysis of Carnatic Music: A Machine Learning Approach
shanta peaceful evoke a multitude of emotions in the listener
Table 1: Emotional components in Carnatic Music and hence cannot be represented using a single
label. For example, the song “Enna Tavam” in
2. Problem Definition raga Kapi, is a devotional song praising a
Our aim is to build a framework that Hindu God. Since it is also about a God who
accomplishes two tasks: in the form of a child, there are strong notes of
• Predict the emotions induced on a listener affection and love conveyed by the song.
when the listener listens to the song. There is also a tinge of the melancholy of a
• Given a song that the listener likes, retrieve mother whose son is growing up fast and
a set of songs that might induce emotions proving to be beyond her comprehension. In
similar to that of the given song. such cases, it is hard to label the song as
We explore an expert system approach “happy” when there are many other emotions
wherein the knowledge obtained from experts involved. In our quest for alternative
in the field of Carnatic music serves as the representations, we came up with a novel way
ground truth for various tasks in our system, to interpret the emotional state of a listener by
including the information about emotions using a mixture of the classical emotions
induced by a particular song. defined by Carnatic music theory.
In order to address the problem described In this model, the emotional state experienced
above, there are several sub-problems to by a listener is represented using a mixture of
consider, the important ones being: the 10 Rasas, as defined by the Classical
• How do we represent and annotate the music theory. Thus, the emotional state of a
emotions induced by a song? listener can be visualized as a point in a 10-
• How do we prepare a corpus of Carnatic dimensional Euclidian space, where each
songs and get it annotated by experts? How dimension corresponds to a rasa. To further
do we design a method which makes it easy illustrate the model, we could look at a few
for the experts to annotate? examples below:
• What features from audio signal might be • Happy(1) = Sringara (Romantic) + Shantha
relevant for classifying the emotions (Peaceful)
induced? A song in this state is romantic but either
• How do we cluster emotionally similar elements of soothing peace and calm in it.
songs together? How good/pure are these One example from the Carnatic music
clusters? repertoire is “Mohamahinaen” in raga
Karaharapriya.
3. Representation of Emotions • Happy(2) = Hasya (Joy) + Bhakthi
For solving the problem at hand, it was crucial (Devotion)
to come up with a system for representing “Vathapi Ganapathim” in Hamsadhwani is a
emotions conveyed by Carnatic music. popular song that is joyful and energetic.
Initially, we experimented with the two Yet it is devotional, praising Ganesha.
dimensional Thayer’s model and a multi-class, • Excitement(1) = Bhakthi (Devotional) +
single-label approach to emotion Rudra (Angry) + Adhbhuta (Wonder) +
representation as used in Yang et al. (2008) Veera (Powerful)
and Han et al. (2009). The results of this “Bho Shambho” in raaga Revathi is a song
experiment were not promising. One of the that is powerful and intensely devotional,
reasons for the failure might be due to the fact praising the God of destruction and
that compositions in Carnatic music tend to portraying his omniscience. The power also
3

4. Emotional Analysis of Carnatic Music: A Machine Learning Approach
translates into anger occasionally, showing often slower exploration of the melody
the dance of God and wondering at His leading into the main song. The experts were
power. asked to annotate each of these song segments
with ratings for each of the 10 Rasas.
Further, we represent the value for each Distribution of Parent Ragas
dimension as a rating between 0 (not present) in Corpus
and 3 (very strongly present), as obtained
from the expert. Thus, we can represent the Chitarambari
emotion conveyed by each song as a 10- Kalyani
dimensional vector, where each dimension can Dharmavathi
take on values between 0 and 3, inclusive. In Gamanashrama
order to test the validity of our representation Pantuvarali
model, we performed a cluster analysis, the Shubhapanthuvarali
results of which can be used to indicate that Varali
Chalanta
the representation chosen was indeed a good
Shankarabharanam
representation of emotions in Carnatic music. Harikhambhoji
Karaharapriya
4. Corpus Natabhairavi
In order to implement the machine learning Suryakantam
approach, we needed a corpus which is Mayamalavagowla
Natakapriya
annotated with ground truth. In our research
Thodi
review, we failed to find any existing corpus
for Carnatic music and hence built our own 0 10 20 30
corpus. We started off by selecting a few
hundred songs from various Carnatic Number of Samples of Parent Raga
compositions sung by various artists. One of Figure 1: Distribution of Parent Ragas in Corpus
the team member, who is well versed in the
theory of Carnatic Music, labeled each of the The rating had to be chosen from {0(not
compositions with coarse-grained emotion present), 1(weakly present), 2(present),
labels consisting of Happy, Sad, Peaceful and 3(strongly present)}. We then created a
Devotional. We then selected 109 songs, Google Docs Spreadsheet which could be
distributed equally over these 4 coarse grained used by the experts for annotation, as shown
classes for our final corpus. in Figure 2.
Next, we had to get the data annotated by The distribution of Raagas across various
experts. We extracted a 30 sec (after initial 30 samples in the corpus can be seen in (Figure
sec) segment from each song, uploaded it to a 1).
publicly accessible repository (esnips.com).
We ignored the first 30 seconds since it is
Figure 2: Annotator Spreadsheet
4

5. Emotional Analysis of Carnatic Music: A Machine Learning Approach
4.1. Agreement between labelers used in conjunction with classical dance to
To check the agreement between the two convey emotions. Table 3 shows the number
labelers, we looked at the Kappa statistic of disagreements in valence for the rasas as
between the labelers. Cohen’s Kappa is seen in each parent raaga.
calculated as
Pr(a) − Pr(e) Melakartha umber of Disagreements of
κ=
1− Pr(e)
Adhbhuta
Sringara
where Pr(a) is the observed percentage
Bhakthi
Karuna
Shanta
agreement and Pr(e) is the probability of
random agreement. In this case, since there are
4 possible ratings for any emotion, Pr(e) = Thodi 3 3 1 1 4
Natakapriya 4 4 2 4 3
0.25. The following table shows the Kappa
Mayamalavagowl 4 2 1 3 1
statistic for each emotion. a
Suryakantam 1 2 1 1 0
Emotion Meaning Kappa Statistic Natabhairavi 5 4 1 2 5
Karaharapriya 11 7 8 12 13
Bhakthi Devotion -0.1809 Harikhambhoji 11 1 11 12 9
Sringara Love 0.1365 Shankarabharana 13 4 18 20 20
Hasya Comedy/laughter 0.9492 m
Raudra Anger 0.9873 Chalanta 2 0 2 3 0
Karuna Sadness 0.3015 Varali 2 3 2 2 3
Bhibhatsa Disgust 1 Shubhapanthuvar 1 1 0 1 0
Bhayanaka Fear 0.9873 ali
Vira Heroism 0.2888 Pantuvarali 1 2 1 2 2
0.6063 Gamanashrama 2 1 0 1 2
Adhbhuta Wonder
Dharmavathi 1 0 0 1 0
Shanta Peace 0.1365
Table 2: Kappa Statistic for each Rasa
Kalyani 6 3 6 8 8
Chitarambari 0 0 1 1 0
The statistic is very high for Hasya, Raudra, Total 67 37 55 74 70
Bhibhatsa and Bhayanaka. This is the case Table 3: Disagreements by parent raga
because those emotions are very rarely present
in Carnatic music and hence both labelers 5. Experiments and Analysis
typically labeled the samples as 0. The 5.1. Classification Task
remaining emotions show significant Once we had the annotated corpus, the next
disagreement. Each rater seems to perceive step was to train classifiers for predicting the
different levels of the emotion in the samples. ratings for each of the dimensions or rasas.
Our reasoning behind this is that different We chose to treat each of the dimensions as
labelers have different perceptions of independent of each other and hence we had
emotions. For instance Sringara can refer to to train different classifiers for each of the
romantic love but often also refers to parental dimensions. Further, looking at the results of
love, friendship or beauty. As a result of expert annotation, we decided to filter out
numerous interpretations, the consistency some dimensions since they were rated as
between raters decreases. Since only some of absent for most of the instances. We trained
these emotions are used commonly in songs to classifiers for four different dimensions:
convey emotions, we restricted to our Bhakthi, Sringara, Karuna, Shantha. The task
experiments to the following emotions: was a multi-class (4) classification problem
bhakthi, sringara, karuna, vira, adhbuta and since each of the Rasas could have discrete
shanta. The remaining Rasas were usually
5

6. Emotional Analysis of Carnatic Music: A Machine Learning Approach
ratings of 0, 1, 2, or 3. Features. SVM Ripper
Accuracy (%) Accuracy (%)
Dynamics. 36.4 34.5
5.1.1 Classification Methodology Rhythm – Attack 37.4 30
We used WEKA (Hall et al., 2009) for our Rhythm – Tempo 34.6 34.5
experiments. For each of the dimension, we Rhythm – All 33.6 34.5
experimented with SVM and Ripper Pitch 43 29
classifiers. For choosing SVM model, we Timbre – MFCCs 37.4 35.5
varied the "C" parameter from 1x10-4 to (Mean, SD)
Timbre – Mel 37.3 40
1x104.
Spectrum
Timbre – Others 45.8 34
5.1.2 Features Used (Mean, SD)
We used MIRToolBox within Matlab Timbre – All 40 36.5
environment (Lartillot et al., 2007) for (Mean, SD)
extracting the features from Audio signals. Tonal (Mean, SD) 41 43
All 37.4 40
Keeping in line with previous research in this Table 5: Dimension – Sringara
field, we experimented with the following set Baseline: 34.6% (Major Value = 2)
of features.
• Dynamics – RMS Energy. Features SVM Ripper
• Rhythm – Fluctuation, Tempo, Attack Time. Accuracy (%) Accuracy (%)
Dynamics 60 57
• Pitch – Peak, Centroid calculated from Rhythm – Attack 57.9 57.9
Chromagram. Rhythm – Tempo 57.9 55
• Timbre – Spectral, Centroid, Skewness, Rhythm – All 57.9 57
Spread, Brightness, Flatness, Roughness, Pitch 57.9 57
Irregularity, MFCCs, Zero crossing, Spectral Timbre – MFCCs 57.9 56
Timbre – Mel 57.9 53
flux.
Spectrum
• Tonal – Key clarity, Key strengths (12). Timbre – All 57.9 50.5
Tonal (Mean, SD) 57.9 59
5.1.3 Classification Results All 57.9 51
The results reported below are the accuracy Table 6: Dimension – Karuna
Baseline: 57.9% (Major Value = 0)
value averages obtained using 10-fold cross
validation technique. Features SVM Ripper
Accuracy (%) Accuracy (%)
Features SVM Ripper Dynamics 42.9 36.4
Accuracy (%) Accuracy (%) Rhythm – Attack 42.9 37.4
Dynamics 71 67 Rhythm – Tempo 49.6 43
Rhythm – Attack 71 68 Rhythm – All 42 54.2
Rhythm – Tempo 71 71 Pitch (Mean, SD) 47 50.5
Rhythm – All 71 71 Timbre – MFCCs 43 38
Pitch 71 68 Timbre – Mel 43 44
Timbre – MFCCs Spectrum (Mean,
71 64
SD)
Timbre – Mel 71 63 Timbre – All 44 39
Spectrum
Tonal 43 40
Timbre – All 70 64.5 All 44 39
Tonal 71 62 Table 7: Dimension – Shanta
All 70 60 Baseline: 42.9% (Major Value = 1)
Table 4 : Dimension – Bhakthi
Baseline: 71% (Major Value = 3)
6

7. Emotional Analysis of Carnatic Music: A Machine Learning Approach
As we can see from the evaluation results, the • The clustering gives us a soft or fuzzy
importance of each feature set and classifier approach for representing the emotions
varies by the emotional dimension under conveyed by a song. Using these clusters,
consideration. The best feature sets for each of we can retrieve songs that are emotionally
the emotional dimensions are highlighted in similar to the given song. Without this
green. The best performing feature sets for approach, emotional similarity would be
each of the dimensions are in logical restricted to single labels like Happy, Sad
agreement with those defined by the Carnatic etc. Hence, given a happy song, we could
Music theory. only retrieve other Happy songs. But with
this approach, we can locate emotionally
5.1.4 Analysis of results similar songs in the 10-dimensional space
As we can see, the results are equal to or using any similarity metrics like Cosine
better than baseline for all the emotional similarity, Euclidian distance, Manhattan
dimensions considered. But for Bhakthi, the Distance, among others.
results were not any better than the baseline.
Two of the important factors responsible for 5.2.1 Clustering and Evaluation
the low accuracies might be: In order to evaluate the purity of each cluster,
• Sparse data in terms of number of annotated we need a metric that tells us how emotionally
samples that were available (109 in total). similar the given songs are to each other. We
• Limitations of the song segmentation explored a qualitative approach for evaluating
technique used during feature extraction. the goodness or purity of the cluster. This
This was basically 30 second segment after approach consists of associating each song
initial 30 seconds. with the Melakartha Raaga it corresponds to.
Once we have this, we could obtain the
5.2. Clustering information about emotional similarity of
Once we have the values or ratings for each of Raagas using Carnatic Music theory. Thus, by
the Rasas or dimensions, we can try clustering analyzing the Raaga distribution within each
together songs based on their emotional cluster, we can qualitatively comment
similarity. The motivation for this arises from regarding the cluster's purity or goodness.
our hypothesis that the emotional state of a
listener can be visualized as a point in 10- For the clustering task, we represented each
dimensional Euclidean space. Therefore, input instance (song) as a 10-dimensional
emotionally similar songs would occur close vector and we ran the EM algorithm in weka
to each other in this 10-dimensional space. with the following parameters:
The clustering task would basically help us “weka.clusterers.EM -I 100 -N -1 -M 1.0E-6 -
with two things: S 100”. We analyzed each person’s labels
• If the songs clustered together are indeed separately because of low rater agreement. We
similar with respect to emotions induced on had initially tried to correlate the clusters to
the listener (cluster purity is high), it would the raga labels but due to lack of enough
verify our hypothesis that the 10 dimensions samples from many of the ragas (many had
are indeed good for capturing the emotions only 1 sample present in the corpus), we
corresponding to the song. decided to use its parent raga.
7

8. Emotional Analysis of Carnatic Music: A Machine Learning Approach
Every raga in Carnatic music is either a parent structures. Those derived from the same raga
raga or a derived raga. Each derived raga is are more similar to each other that any two
derived from one of 72 main ragas, the parent random ragas.
ragas. Derived ragas share the same notes as
their parent raga as well as certain melodic
Number of samples per cluster (Rater 1)
25
Number of samples
20
15
10
5
0
Parent Raga Count of Cluster 0 Count of Cluster 1
Figure 2 - Raaga distribution across clusters
5.2.2 Analysis of clustering results found in Cluster 0 tend to be associated with
We notice (Figure 3) that the distribution of sadness and peace. We can see Karaharapriya
parent raga instances for each cluster is is split between the two clusters. Several ragas
different. Since the cluster size is fixed to two, in the corpus were derived from
we can hypothesize that the two clusters might Karaharapriya. One such raga is Kannada,
logically correlate to roughly Happy and Sad which is associated with joyous emotions.
emotions. This might be possible only if the Others such as Abheri are much sadder and
10-dimensional representation chosen is
dimensional melancholic. Hence we see mixed clusters in
indeed a good way to capture the emotions Karaharapriya.
conveyed by the song. Looking at the
distribution of the Raagas across clusters In figure 4, we can see how the different
(Figure 3), we can notice that the Raagas emotions contribute towards each cluster.
corresponding to Happy or Positive emotions Cluster 1 (in red) shows an absence of Karuna
tend to co-occur predominantly in the same
occur and much higher levels of Sringara while
cluster, and those corresponding to the Cluster 0 (in blue) is sadder.
negative emotions tend to co co-occur
predominantly in the other (different) cluster.
More specifically, Harikhambhoji an and
Shankarabharanam have a large fraction of the
samples in Cluster 1. In general, those ragas
tend to be associated with more positive
emotions such as happiness and joy. The ragas
8

9. Emotional Analysis of Carnatic Music: A Machine Learning Approach
music. We were also able to verify the
appropriateness of this method through the
cluster analysis procedure. Next, we tried
.
training classifiers to predict the values for
each of the emotional dimensions, the results
of which were better than the baseline.
Therefore, we have a framework that could be
efore,
used for:
• Predicting and locating songs according to
ocating
the emotions they induce on the listener.
• Retrieving songs that are emotionally
similar to the given song.
We can attribute the relatively low
improvements over baseline in the
classification task to:
• Sparsity of annotated samples.
• Naive Segmentation technique used (30 sec
after initial 30 sec) during feature extraction
• Conflicts in Expert labeling (low Kappa
scores).
6.1. Future Work
If we were to improve on our work, we could
ur
start by trying to obtain more annotated
samples from the Experts. The definition of
each of the dimension as a Rasa should be
made more explicit in order to decrease
conflicts in labeling between experts and
increase the Kappa score. We would also need
ncrease
to collect the annotation ratings from more
than one expert in order to ensure statistical
and logical consistency of the annotations.
Also, we could explore using a more fine
grained rating scale for each dimension and
dimens
see if it leads to improvement in clustering
and classification accuracies. We need to also
work on better segmentation strategies for
extracting features. For example, we could try
Figure 3: Distribution of Clusters across Rasas
: considering the initial 30 sec, middle 30 sec
and the final 30 sec segments of the given
We can thus qualitatively argue that the
song, rather than just one 30 sec segment.
clusters induced are indeed good/pure.
6. Conclusion 6.2. Acknowledgements
We would like to take this opportunity to
In our work, we explored a novel method for
express our heartfelt gratitude to everyone
representing emotions conveyed by Carnatic
who helped us in our work. Specifically, we
9

10. Emotional Analysis of Carnatic Music: A Machine Learning Approach
would like to thank the following people for music into emotions. In: Proceedings of the
their invaluable suggestions and contributions: 9th International Conference on Music
• Sapthagiri Iyengar, has been practicing Information Retrieval (ISMIR).(2008).
Carnatic music for more than a decade now.
He has also performed in various Carnatic 5. A. Wieczorkowska, P. Synak, and Z.W.
music concerts. We consulted him for Ras. Multi-label classification of emotions in
clarifications related to the Carnatic theory music. In Proceedings of the 2006
and he was also one of the Experts who International Conference on Intelligent
volunteered to annotate the corpus. Many of Information Processing and Web Mining
his invaluable suggestions have been (IIPWM’06), pages 307–315, 2006.
incorporated into our present work.
• Meena Ramachandran is a connoisseur of 6. Chordia, P. and Rae, A. 2008.
Carnatic music. She was one of the experts Understanding Emotion in Raag: An
who volunteered to annotate the corpus. Empirical Study of Listener Responses. In
• Prof. Julia Hirschberg, Bob Coyne, Fadi Computer Music Modeling and Retrieval.
Biadsy and all other members of the Speech Sense of Sounds: 4th international
Lab and CS6998 course for their invaluable Symposium, CMMR 2007, Copenhagen,
suggestions. Denmark, August 27-31, 2007.
7. How does a raga make you feel
7. References (http://www.karnatik.com/rasas.shtml)
1. Y.-H. Yang, Y.-C. Lin, Y.-F. Su, and H.-H.
8. Lartillot, O. & Toiviainen, P. (2007). MIR
Chen, "A regression approach to music
in Matlab (II): A Toolbox for Musical Feature
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Extraction From Audio. International
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(TASLP), vol. 16, no. 2, pp. 448-457, Feb.
Vienna, 2007.
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9. Mark Hall, Eibe Frank, Geoffrey Holmes,
2. Byeong-jun Han, Seungmin Rho, Roger B.
Bernhard Pfahringer, Peter Reutemann, Ian H.
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Music Emotion Recognition using Support Software: An Update; SIGKDD Explorations,
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Sarroff, Jeff Berger and Robert Rowe,
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Recognition System”, Poster presentation.
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Information Retrieval Conference, Kobe,
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4. Trohidis, K., Tsoumakas, G., Kalliris, G.,
Vlahavas, I.: Multilabel classification of
10