Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Oktoechos Classification in Liturgical Music Using
Musical Texture Features
Rajeev Rajan *, Amlu Anna Joshy, and Varsha Shiburaj
College of Engineering, Trivandrum, Thiruvananthapuram
APJ Abdul Kalam Technological University, Kerala, India*rajeev@cet.ac.in

Abstract. A distinguishing feature of the music repertoire of the Syrian tradition is the system of classifying melodies into eight tunes, called ’oktoec̄hos’.
It inspired many traditions, such as Greek and Indian liturgical music. In oktoec̄hos tradition, liturgical hymns are sung in eight modes or eight colours(known as eight ’niram’, regionally). In this paper, the automatic oktoec̄hos genre
classification is addressed using musical texture features (MTF), i-vectors andMel-spectrograms through deep learning strategies. The performance of the proposed approaches is evaluated using a newly created corpus of liturgical musicin Malayalam. Long-short term memory (LSTM)-based experiment reports theaverage classification accuracy of 83.76%, with a significant margin over otherframeworks. The experiments demonstrate the potential of LSTM in learning
temporal information through MTF in recognizing eight modes in oktoec̄hos system.
Keywords: liturgy, colour, timbral, deep learning.

1

Introduction

Oktoec̄hos classification in liturgical music (music using in worship) is addressed using
deep learning frameworks in the paper. Music plays a vital role in liturgy because music itself is a language that goes beyond even cultures and races. The vast diversity offorms, styles, and functions in the music used for worship makes it challenging to categorize liturgical music. Musical roles have been distributed in different ways in different rites. Indian orthodox church has imbibed this music system into its liturgy throughits relationship with the orthodox church in Syria (Antiochian liturgy). A distinguishing feature of the music repertoire of the Syrian tradition is the system of classifying
melodies into eight tunes [15]. This musical tradition is transferred to Indian orthodox
liturgical music through centuries with hymns in the Malayalam1 . Most of the hymns
used for various feasts and occasions are musically composed under eight tunes. The
system of singing the same text in eight different melodies in an eight-week cycle isreferred to as the’oktoec̄hos’ [15].
1https://en.wikipedia.org/wiki/Malayalam

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

1.1

Oktoec̄hosWestern Syriac music is based on the classical tradition prescribed in ’Bethgazzo’2 .
In oktoec̄hos tradition, liturgical hymns are sung in eight modes, similar to the Greek
liturgy. They are a group of eight adaptable melody types, known as eight ’colours’ or’niram’ [27]. None of the Syriac melodies may cover eight notes in an octave. It may
often cover three or four or five notes. There is a similarity in Syrian/Indian liturgical(Malankara) hymnal music and rāga3 of Indian art music. But they cannot be taken
in equal level because the rāga classification of Indian art music is incomparable in its
scientific systematisation. Each rāga has a particular mode and temperament. Oktoec̄hos
can be compared to rāga in the sense that they are also creating passion or rasa during
singing [27]. In Indian art music, a hymn in a rāga can be sung or played in anotherrāga. The same principle is applied in the oktoec̄hos system that most of the liturgicalhymns can be sung in all the eight tunes.
Oktoec̄hos is considered as a cyclic system because it is performed in a cycle ofeight weeks with two colours in a week. Each colour begins with evening prayer of
Sunday. If the first colour is used in the evening, the same is continued for the restof the day. From Monday evening onwards the fifth colour is used. On Tuesday, it isagain switched on to the first colour and so on. The next Sunday begins with the second
colour. It is continued in the order 1-5; 2-6; 3-7; 4-8; till to the fourth Sunday and on
the fifth Sunday onwards the order becomes 5-1; 6-2; 7-3; 8-4.
1.2

Related Work

Although there has been significant work in music genre classification, the proposed
task of liturgical music genre classification is first of its kind. Melodic features [23] andlocal features [28] have been employed well for genre classification task. Researchers
used both generative and discriminative models [12, 24] for music classification. Musical texture features are recently used in meter classification works [22, 21, 19]. Music
genre classification is addressed using feature fusion in [20]. A model capable of learning distinctive rhythmic structures of different music genres using unsupervised learning is proposed in [16]. In contrast with the standard approaches, model-based distances
between time series can take into account the structure of the songs by modelling the
dynamics of the parameter sequence [7]. More recent deep learning approaches process
spectrograms for the task of music genre classification [17, 3]. Regarding multimodal
approaches found in the literature, most of them combine audio and song lyrics [11]through a fusion framework. The proposed task is similar to music genre classification,
but shares the textual content across modes is one of the specific traits of the oktoec̄hos
genre system. The aim of the work is to explore the ability of LSTM to capture the longrange dependency in learning temporal patterns.
The rest of the paper is organized as follows; Section 2 describes the proposedsystem followed by the performance evaluation in Section 3. The analysis of results isgiven in Section 4. Finally, the paper is concluded in Section 5.
2
3

Bethgazzo is a Syriac liturgical book that contains a collection of Syriac chants and melodies.
rāga is the fundamental melodic framework for both Carnatic and Hindusthani traditions58

Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

2

System Description

2.1

Feature Extraction

It has already been proven that timbral and rhythmic features are useful in genre classification task [1]. In our experiment, we extracted timbral and rhythmic features as musical texture features. Timbral features, namely Mel-frequency cepstral features (MFCC)
and low-level timbral feature-set (TLF ), are computed in the front-end. Spectral centroid, spectral roll-off, spectral flux, and spectral entropy [13] are extracted as low-leveltimbral feature set. Besides, features namely tempo, pulse clarity, event density [10] arecomputed as rhythmic cues (RF ). Event density represents the number of events perunit time in the music piece. It is a measure that captures how easily ”listeners can perceive the underlying rhythmic or metrical pulsation of music” [10]. This feature plays
an important role in musical genre recognition, in particular, allowing a finer discrimination between genres that present similar average tempo, but that differ in the degreeof emergence of the main pulsation over the rhythmic texture [10]. The distribution ofpulse clarity for the corpus is shown in Fig. 1. It can be seen that the pulse clarity distribution for niram 1, niram 2 and niram 3 is different from the rest. Low-level timbral
features and rhythmic features are computed using MIRToolbox 4 .

Fig. 1: Distribution of pulse clarity for the colours

Given the success of using i-vectors for speaker and music processing tasks [29, 6],
we use the i-vector framework in the proposed task for performance comparison. The
i-vector-based statistical feature has been employed well in the task of music genre
classification [4]. In i-vector system [5], the high dimensional GMM super vector space(generated from concatenating the mean values of GMM) is mapped to a low dimensional space called total variability space. The target utterance GMM is adapted froma universal background model (UBM) using eigenvoice adaption. The target GMM super vector can be viewed as a shifted version of UBM. Formally, a target GMM super
vector M can be written as:
M = m + Tw(1)
4

https://www.jyu.fi/hytk/fi/laitokset/mutku/en/research/materials/

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

where m represents the UBM super vector, T is a low dimensional rectangular total
variability (TV) matrix, and w is termed as i-vector. Using training data, the UBM andTV matrix is modeled by expectation maximization. 100 dimensional i-vectors (iMFCC )are computed for each song from MFCC using Alize tool kit [2].
In the final phase, visual representation of audio files, spectrograms are utilized
for the proposed task. Since Mel-spectrogram has already been utilized well for music
genre classification tasks [25, 8], we also experimented with mel-spectrogram-CNN
framework for the proposed task. Mel-spectrogram can be seen as the spectrogramsmoothed, with high precision in the low frequencies and low precision in the highfrequencies. Mel-spectrogram is computed with frame size of 40 ms and hop size of 10
ms using 128 bins.
2.2

Classification Scheme

We experimented with four classifiers, namely, SVM, DNN, CNN and LSTM. DNN isbased on six hidden layered network, which uses 64, 128, 256, 512, 1024, 2048 nodes
in successive layers with a dropout of 0.25. The network is trained with the batch size
is 32 for 150 epochs by AdaMax optimization algorithm. Relu and softmax have beenchosen for hidden and output layers, respectively.

Table 1: LSTM architecture used for the experiment
Sl no. Output Size
Description
1
2
3
4
5

(45,64)(46, 64)(1024)(1024)(8)

LSTM, 64 hidden units
Dropout (0.25)
LSTM, 1024 hidden units
Dropout (0.25)
Dense (8 hidden units)

The proposed CNN has six convolution layers, followed by max-pooling. We use
filters with a very small 3×3 receptive fields, with a fixed stride of one and increase thenumber of filters for the layer by a factor of 2 after every layer. Global max-pooling is
adopted in the final max-pooling layer, which is then fed to a fully connected layer. The
training is done with 100 epochs by optimizing the categorical cross-entropy between
predictions and targets using Adam optimizer, with a learning rate of 0.001.
LSTM architecture shown in Table 1 effectively utilized to track the temporal pattern embedded in the modes of the music. LSTM-RNNs can capture long-range temporal dependencies by overcoming the vanishing gradient problem in conventional RNNs[26]. RNN tap inherent temporal pattern embedded within the frame-wise computed
MTF. Deep learning schemes and SVM are implemented using and Keras-TensorFlow
and LibSVM, respectively.

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Table 2: Overall classification accuracy for the experiments
Sl.No Feature
Method Accr.(%)
1 MFCC+ TLF + RF
SVM
42.65
2 MFCC + TLF + RF
DNN
48.70
3 iMFCC + TLF + RF
DNN
50.00
4 Mel-spectrogram
CNN
52.60
5
MFCC + TLF + RF LSTM 83.76

3

Performance Evaluation

3.1

Database

A database is created in a studio environment and it consists of eight nirams (colours),
with 384 audio tracks with duration 25 to 45 sec per file. No accompaniments were there
in the audio files. A total of 15 professional singers in the age group 12 to 50, were participated in the data recording and the whole session was recorded at 44.1kHz. All thesingers were very much familiar with the singing modes in ’oktoec̄hos’. Malayalam
hymns were collected from the liturgical book of Indian Orthodox church. The recordings were made niram by niram in successive sessions using a high-quality microphone,A few audio files can be accessed at https://sites.google.com/view/audiosamples-2020/.
During experimentation, 60% files of the dataset are used for training, 10% is used forvalidation and the rest for testing.
3.2

Experimental set-up

MFCCs (39 dim comprising 13 dim MFCC, its delta and delta-delta features), timbral(TLF , 4 dim) rhythmic (RF , 3 dim) are frame-wise computed with a frame width of
40 ms and hop size of 10 ms and fused in feature-level to obtain 46-dimensional MTF.
In the i-vector experimental phase, 100-dimensional i-vectors are computed using 128
mixture GMM from MFCC using Alize tool-kit [2]. UBM model is trained using features derived from the auxiliary database comprising audio file other than the files in
the corpus. Auxiliary database, comprising 300 audio files (duration 25-35ms) of liturgical music category, is prepared in a studio environment. The songs from the training
data are used for modelling the total variability matrix T by Eigen voice adaption. In
the fusion scheme, track level aggregated timbral (TLF ) and rhythmic (RF ) features
are concatenated with track-level computed i-vectors. Following the evaluation method
widely used in the MIR tasks, we computed the precision and recall and the F1 measure
as basic evaluation metrics for the performance.

4

Results and Analysis

The results are tabulated in Table 2. As per the table, the average classification accuracyof 42.66%, 48.70%, 50.00%, 52.60% and 83.76% are reported for SVM, DNN, i-vector

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100

Accuracy (%)

CNN

LSTM

80
60
40
20

1

2

3

4

5

6

Number of Layers

Fig. 2: Accuracy with number of layers for CNN and LSTM

framework, Mel-spectrogram-CNN and LSTM, respectively. It is worth noting that the
LSTM outperforms other approaches with a significant margin. It is reasonable to saythat time pattern capturing scheme is needed in order to recover more relevant information from temporal embedded musical traits [7]. Experiments show that the LSTM
approach is promising for the given task, improving on the case where the dynamics are
not taken into account, and a stationary characterization of the sequences is employed.
LSTM utilized musical textural features to capture song dynamics effectively to perform oktoec̄hos classification. It is shown in [4] that the important music elements canbe captured by i-vectors and may potentially benefit to the classification of music signal. A possible cause of the low value of accuracy in the given experimental set-up may
potentially due to the inability to capture the rhythmic-temporal dynamics well with thegiven UBM framework. Besides, aggregation of musical texture features to track-level
might have deteriorated the performance.
The performance with varying the number of layers of the network is shown in Fig.
2. For the CNN framework, the result improved, as the number of layers increased up
to six and then saturated due to overfitting. It is due to the fact that as n increases,
the model grows in-depth, and the upper layers find efficient feature representations
that are invariant to small perturbations leading to better model generalization. The
authors [14] emphasize the need for more training data in the visual representationbased approaches for the genre classification task. It is stated that CNN needs a large
size of data to achieve better results since it is not successful enough for less data [9].
An elegant solution to this problem is data augmentation, by which deformations toa collection of annotated training samples results in additional training data. During
LSTM approach, maximum accuracy is obtained for two layers as seen in lower-panein Fig. 2. The proposed experiment validates the claim that temporal information has
effectively been learned by MTF-LSTM framework. The experimental insights in [18]show that the performance of the system depends on the temporal architecture, which
is basically designed by considering the musical domain knowledge.
The normalized confusion metrics of LSTM is plotted in Fig. 3. Class-wise classification accuracy of all nirams are greater than 70% for LSTM. Niram 5 and niram 7
report accuracy greater than 90%. Class wise accuracy can be examined from the bar
plot given in Fig. 4 from all phases. The significant improvement in class-wise accuracy of nirams 1, 3, 7, and 8 over CNN based framework can be seen from the plot. The
performance can potentially be improved using data augmentation and proper choiceof architecture. The performance metrics precision, recall and F1 score for all the five62

Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Fig. 3: Normalized Confusion Matrices for MTF-LSTM

approaches are given in Table 3. The average F1 measure of 0.43, 0.50, 0.50, 0.52, 0.84
are reported for SVM, DNN, i-vector-DNN, CNN and LSTM, respectively. The highvalues of precision, recall and F1 score show the significance of LSTM for the proposed task. Fig. 5 visualizes the output vectors produced by the snippets for the last

SVM

DNN

i MFCC+TLF+ RF-DNN

CNN

LSTM

Accuracy (%)

100
80
60
40
20
0
Nir-1

Nir-2

Nir-3

Nir-4
Nir-5
Niram Class

Nir-6

Nir-7

Nir-8

Fig. 4: Class-wise performance for entire phases of the experiments

dense layer of the trained LSTM network using t-SNE. Note that there is good clustering (as represented with colour) and a general separation of different classes for LSTM.
It is important to note the effectiveness of LSTM in the proposed task without using any
modelling data or augmentation data as that of i-vector or CNN methodologies. Since
the results show the promise of temporal pattern learning, other frameworks have to beexperimented to investigate the potential of the proposed approach.

5

Conclusion

Oktoec̄hos classification is addressed in this paper. The performance of the proposed approaches is evaluated using a newly created corpus of Liturgical music in Malayalam.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Table 3: Precision (P), recall (R), and F1 measure

R

F1

P

R

F1

0.82
0.79
0.82
0.78
0.90
0.77
0.95
0.87

P

0.80 0.84
0.74 0.85
0.93 0.74
0.88 0.70
0.86 0.95
0.75 0.79
0.95 0.95
0.85 0.89

0.84

F1

0.45
0.59
0.47
0.63
0.60
0.59
0.44
0.40

0.85 0.84

R

0.42 0.50
0.52 0.68
0.70 0.35
0.69 0.58
0.54 0.68
0.55 0.63
0.47 0.42
0.44 0.37

0.52

P

0.51
0.52
0.45
0.39
0.42
0.56
0.57
0.55

0.54 0.53

F1

0.48 0.56
0.46 0.61
0.54 0.39
0.46 0.33
0.40 0.44
0.52 0.61
0.59 0.56
0.60 0.50

0.50

R
0.44
0.40
0.34
0.61
0.50
0.57
0.41
0.71

0.50 0.50

P
0.35 0.58
0.36 0.45
0.32 0.37
0.71 0.53
0.53 0.47
0.62 0.53
0.50 0.35
0.80 0.63

0.50

F1
0.39
0.41
0.31
0.55
0.48
0.53
0.35
0.44
0.52 0.48

R

Niram-1 0.55 0.30
Niram-2 0.40 0.42
Niram-3 0.25 0.42
Niram-4 0.64 0.47
Niram-5 0.62 0.40
Niram-6 0.43 0.68
Niram-7 0.33 0.37
Niram-8 0.54 0.37
0.43

P

SL.No Colour MFCC+TLF +RF -SVM MFCC+TLF +RF -DNN iMFCC +TLF +RF -DNN Mel-spectrogram-CNN MFCC+ TLF +RF - LSTM
1
2
3
4
5
6
7
8
0.47 0.43

Macro

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Fig. 5: tSN E plot from LSTM

The evaluation shows the potential of MTF-LSTM framework in Oktoec̄hos classification with an average classification accuracy of 83.76%. Since the Greek liturgy and
Gregorian chant also share similar musical traits with Syrian tradition, the musicological insights observed can potentially be applied to those traditions as well.

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