IRJET- Music Genre Classification using SVM
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This document presents a technique for classifying music genres using support vector machines (SVM). Tempogram features are extracted from music clips to represent rhythmic characteristics. SVM classifiers are trained on these features from different genres to learn the optimal boundaries between genres. The system is tested on unlabeled music clips and achieves an accuracy of 95% at classifying songs into genres such as pop, classic, and rock. The technique demonstrates that SVM is effective for automatic music genre classification when used with tempogram features.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1058
Music Genre Classification using SVM
R. Thiruvengatanadhan1
1Assistant Professor/Lecturer (on Deputation), Department of Computer Science and Engineering,
Annamalai University, Annamalainagar, Tamil Nadu, India
----------------------------------------------------------------------***---------------------------------------------------------------------
Abstract :- Automatic music genre classification is veryuseful
in music indexing. Tempogram is one of the feature extraction
method uses in classification of musical genre that is based
temporal structure of music signals. Searchingandorganizing
are the main characteristics of the music genre classification
system these days. This paper describes a new technique that
uses support vector machines to classify songs. Supportvector
machines classify audio into their respective classes by
learning from training data. The proposed feature extraction
and classification models results in better accuracy in music
genre classification.
Key Words: Feature Extraction, Tempogram and Support
vector machines (SVM).
1. INTRODUCTION
There are numerous studies that are investigatedinthefield
of digital music and how it would be possible to enhance
user’s experience. A lot of untagged music files are being
archived, while somecontain assumedorfalsetags.However
automatic genre classificationisnotaneasytask considering
music evolving within short periods. In addition to this, the
advancement in digital signal processing and data mining
techniques has led to intensive study on music signal
analysis like , content-based music retrieval, music genre
classification, duet analysis, Musical transcription , Musical
Information retrieval and musical instrumentdetectionand
classification. Musical Instrumentdetectiontechniqueshave
many potential applications such as detecting and analyzing
solo passages, audio and videoretrieval,music transcription,
playlist generation, acoustic environment classification,
video scene analysis and annotation etc [1]
Advanced music databases are continuously achieving
reputation in relations to specialized archives and private
sound collections. Due to improvements in internet services
and network bandwidth there is also an increase in number
of people involving with the audio libraries. But with large
music database the warehouses require an exhausting and
time consuming work, particularly when categorizing audio
genre manually. Music has also been divided into Genres
and sub genres not only on the basis on music butalsoonthe
lyrics as well [2]. This makes classification harder. To make
things more complicate the definition of music genre may
have very well changed over time [3]. For instance, rock
songs that were made fifty years ago are different from the
rock songs we have today.
2. ACOUSTIC FEATURES FOR AUDIO CLASSIFICATION
An important objective of extracting the features is to
compress the speech signal to a vector that is representative
of the meaningful information it is trying to characterize. In
these works, musicfeatures namelyTempogramfeatures are
extracted.
2.1 Tempogram
An element which gives shape to the music in temporal
dimension is the rhythm. Rhythmic feature arranges sounds
and silences in time. A predominatedpulsecalledbeat which
serves as basis for temporal structure of music is induced
[242]. Tempogram captures the local tempo and beat
characteristics of musicsignals.TheFouriertempograms are
used in the research work.
Fig -1: Novelty Curve Computations.
Human perceives rhythm as a regular pattern of pulses as a
result of moments of musical stress. Abrupt changes in
loudness, timbre and harmonic causes the occurrences of
musical accents [4]. In instruments like piano, percussion
instruments and guitar, occurs a sudden change in signal
energy accompanied by very sharp attacks. A novelty curve
is based on this observation and is computed for extracting
meaningful information regarding note onset e.g. pieces of
songs which are dominated by instruments [5]. In the pre-
processing, stage short segmented frames have been
extracted and windowed. The novelty curve computed, as
described above, indicatespeakswhichrepresentnoteonset
values [6]. A hamming window function is applied to avoid
boundary problems as smoothing [7]. Novelty curve
computation is shown in Fig. 1. The Fourier tempogram is
calculated. Tempo related to musical context is a measure of
beats per minute. Finally,the histogramiscomputedforeach
frame resulting in 12 dimensional feature vectors.
3. CLASSIFICATION MODEL
3.1 Support Vector Machine
A machine learning technique which is based on the
principle of structure risk minimization is support vector
machines. It has numerousapplicationsinthearea ofpattern](https://image.slidesharecdn.com/irjet-v5i10197-181103055152/85/IRJET-Music-Genre-Classification-using-SVM-1-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1059
recognition [8]. SVM constructs linear model based upon
support vectors in order to estimate decision function. If the
training data are linearly separable, then SVM finds the
optimal hyper plane that separates the data without error
[9].
Fig. 2 shows an example of a non-linear mapping of SVM to
construct an optimal hyper plane of separation. SVM maps
the input patterns through a non-linear mapping intohigher
dimension feature space. For linearlyseparabledata,a linear
SVM is used to classify the data sets [10]. The patterns lying
on the margins which are maximized are the support
vectors.
Fig -2: Example for SVM Kernel Function Φ(x) Maps 2-
Dimensional Input Space to Higher 3-Dimensional Feature
Space. (a) Nonlinear Problem. (b) Linear Problem.
The support vectors are the (transformed) trainingpatterns
and are equally close to hyperplane of separation. The
support vectors are the training samples that define the
optimal hyperplane and are the most difficult patterns to
classify [11]. Informally speaking, theyarethe patternsmost
informative of the classification task. The kernel function
generates the inner products to construct machines with
different types of non-linear decision surfaces in the input
space [12].
4. IMPLEMENTATION
4.1 Dataset Collection
The music data is collected from music channels using a TV
tuner card. A total dataset of 100 different songsisrecorded,
which is sampled at 22 kHz and encoded by 16-bit. In order
to make training results statisticallysignificant,trainingdata
should be sufficient and cover various genres of music.
4.2 Feature Extraction
In this work fixed length frames with duration of 20 ms and
50 percentages overlap (i.e., 10 ms) are used. An input wav
file is given to the feature extractiontechniques.Tempogram
12 dimensional feature values will be calculated for the
given wav file. The above process is continued for 100
number of wav files.
4.3 Classification
When the featureextractionprocessisdonethemusicshould
be classified. We select 75 music samples as training data
including 25 classic music, 25 pop music and 25 rock music.
The rest 25 samples are used as a test set. For the SVM-1
which is used to classify music into pop and classic used for
training. For the SVM-2 which is used to classify classic and
rock are used for training. Table 1 shows Performance of
music classification in different SVM kernel function.
Table -1: Performance of music genre classification in
different SVM kernel function.
SVM Kernels Performance
Polynomial 89%
Gaussian 95%
Sigmoidal 88%
Chart -1: Performance of music classification for different
duration of music clips
The performance of SVM for different duration as shown in
Chart 1 shows that when the duration were increased from
10 to 20 there was no considerable increase in the
performance.
5. CONCLUSION
In this paper, we have proposed an automatic music genre
classification system using SVM. Tempogram is calculated
as features to characterize audio content. SVM learning
algorithm has been used for the classification of genre
classes of music by learning from training data. Two
nonlinear support vector machine classifiers are developed
to obtain the optimal class boundaries between classic and
pop, pop and rock by learning from training data.
Experimental results show that the proposed audio support
vector machine learning method has good performance in
musical genre classification scheme is very effective and the
accuracy rate is 95%.
REFERENCES
[1] C. Joder, S. Essid, G. Richard, and S. Member, “Temporal
Integration for Audio Classification With Application to
Musical InstrumentClassification,”IEEETransactionson
Speech and Audio Processing, vol. 17, no. 1, pp. 174–
186, 2009.](https://image.slidesharecdn.com/irjet-v5i10197-181103055152/85/IRJET-Music-Genre-Classification-using-SVM-2-320.jpg)
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1060
[2] Serwach, M., & Stasiak, B., GA-based parameterization
and feature selection for automatic music genre
recognition. In Proceedings of 2016 17th International
Conference Computational Problems of Electrical
Engineering, CPEE 2016.
[3] Dijk, L. Van., Radboud Universiteit Nijmegen
Bachelorthesis Information Science Finding musical
genre similarity using machine learning techniques, 1–
25, 2014.
[4] Mi Tian, Gy ¨ orgy Fazekas, Dawn A. A. Black, Mark
Sandler, On the use of the tempogram to describe audio
content and its Application to music structural
segmentation, IEEE International Conference on
Acoustics, Speech and Signal Processing(ICASSP),2015.
[5] Venkatesh Kulkarni, Towards Automatic Audio
Segmentation of Indian Carnatic Music, Master Thesis,
Friedrich Alexander University, 2014.
[6] Eronen, A. and Klapuri, A., “Music Tempo Estimation
with k-NN regression,” IEEE Transactions on Audio,
Speech and Language Processing, vol. 18, no. 1, pp. 50-
57, 2010.
[7] Gui, Wenming & Sun, Yao & Tao, Yuting & Li, Yanping &
Meng, Lun & Zhang, Jinglan., A Novel Tempogram
Generating Algorithm Based on Matching Pursuit.
Applied Sciences, 2018.
[8] Chungsoo Lim Mokpo, Yeon-Woo Lee, and Joon-Hyuk
Chang, “New Techniques for Improving the practicality
of a SVM-Based Speech/Music Classifier,” IEEE
International Conference on Acoustics, Speech and
Signal Processing, pp. 1657-1660, 2012.
[9] Hongchen Jiang, JunmeiBai, Shuwu Zhang, and Bo Xu,
“SVM-Based Audio Scene Classification,” IEEE
International Conference Natural Language Processing
and Knowledge Engineering, Wuhan, China, pp. 131-
136, October 2005.
[10] Lim and Chang, “Enhancing Support Vector Machine-
Based Speech/Music Classification using Conditional
Maximum a Posteriori Criterion,”Signal Processing,IET,
vol. 6, no. 4, pp. 335-340, 2012.
[11] Md. Al Mehedi Hasan and Shamim Ahmad. predSucc-
Site: Lysine Succinylation Sites PredictioninProteinsby
using Support Vector Machine and Resolving Data
Imbalance Issue. International Journal of Computer
Applications 182(15):8-13, September 2018.
[12] Hend Ab. ELLaban, A A Ewees and Elsaeed E
AbdElrazek. A Real-Time System for Facial Expression
Recognition using Support Vector Machines and k-
Nearest Neighbor Classifier. International Journal of
Computer Applications 159(8):23-29, February 2017.](https://image.slidesharecdn.com/irjet-v5i10197-181103055152/85/IRJET-Music-Genre-Classification-using-SVM-3-320.jpg)
IRJET- Music Genre Classification using SVM
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1058 Music Genre Classification using SVM R. Thiruvengatanadhan1 1Assistant Professor/Lecturer (on Deputation), Department of Computer Science and Engineering, Annamalai University, Annamalainagar, Tamil Nadu, India ----------------------------------------------------------------------***--------------------------------------------------------------------- Abstract :- Automatic music genre classification is veryuseful in music indexing. Tempogram is one of the feature extraction method uses in classification of musical genre that is based temporal structure of music signals. Searchingandorganizing are the main characteristics of the music genre classification system these days. This paper describes a new technique that uses support vector machines to classify songs. Supportvector machines classify audio into their respective classes by learning from training data. The proposed feature extraction and classification models results in better accuracy in music genre classification. Key Words: Feature Extraction, Tempogram and Support vector machines (SVM). 1. INTRODUCTION There are numerous studies that are investigatedinthefield of digital music and how it would be possible to enhance user’s experience. A lot of untagged music files are being archived, while somecontain assumedorfalsetags.However automatic genre classificationisnotaneasytask considering music evolving within short periods. In addition to this, the advancement in digital signal processing and data mining techniques has led to intensive study on music signal analysis like , content-based music retrieval, music genre classification, duet analysis, Musical transcription , Musical Information retrieval and musical instrumentdetectionand classification. Musical Instrumentdetectiontechniqueshave many potential applications such as detecting and analyzing solo passages, audio and videoretrieval,music transcription, playlist generation, acoustic environment classification, video scene analysis and annotation etc [1] Advanced music databases are continuously achieving reputation in relations to specialized archives and private sound collections. Due to improvements in internet services and network bandwidth there is also an increase in number of people involving with the audio libraries. But with large music database the warehouses require an exhausting and time consuming work, particularly when categorizing audio genre manually. Music has also been divided into Genres and sub genres not only on the basis on music butalsoonthe lyrics as well [2]. This makes classification harder. To make things more complicate the definition of music genre may have very well changed over time [3]. For instance, rock songs that were made fifty years ago are different from the rock songs we have today. 2. ACOUSTIC FEATURES FOR AUDIO CLASSIFICATION An important objective of extracting the features is to compress the speech signal to a vector that is representative of the meaningful information it is trying to characterize. In these works, musicfeatures namelyTempogramfeatures are extracted. 2.1 Tempogram An element which gives shape to the music in temporal dimension is the rhythm. Rhythmic feature arranges sounds and silences in time. A predominatedpulsecalledbeat which serves as basis for temporal structure of music is induced [242]. Tempogram captures the local tempo and beat characteristics of musicsignals.TheFouriertempograms are used in the research work. Fig -1: Novelty Curve Computations. Human perceives rhythm as a regular pattern of pulses as a result of moments of musical stress. Abrupt changes in loudness, timbre and harmonic causes the occurrences of musical accents [4]. In instruments like piano, percussion instruments and guitar, occurs a sudden change in signal energy accompanied by very sharp attacks. A novelty curve is based on this observation and is computed for extracting meaningful information regarding note onset e.g. pieces of songs which are dominated by instruments [5]. In the pre- processing, stage short segmented frames have been extracted and windowed. The novelty curve computed, as described above, indicatespeakswhichrepresentnoteonset values [6]. A hamming window function is applied to avoid boundary problems as smoothing [7]. Novelty curve computation is shown in Fig. 1. The Fourier tempogram is calculated. Tempo related to musical context is a measure of beats per minute. Finally,the histogramiscomputedforeach frame resulting in 12 dimensional feature vectors. 3. CLASSIFICATION MODEL 3.1 Support Vector Machine A machine learning technique which is based on the principle of structure risk minimization is support vector machines. It has numerousapplicationsinthearea ofpattern
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1059 recognition [8]. SVM constructs linear model based upon support vectors in order to estimate decision function. If the training data are linearly separable, then SVM finds the optimal hyper plane that separates the data without error [9]. Fig. 2 shows an example of a non-linear mapping of SVM to construct an optimal hyper plane of separation. SVM maps the input patterns through a non-linear mapping intohigher dimension feature space. For linearlyseparabledata,a linear SVM is used to classify the data sets [10]. The patterns lying on the margins which are maximized are the support vectors. Fig -2: Example for SVM Kernel Function Φ(x) Maps 2- Dimensional Input Space to Higher 3-Dimensional Feature Space. (a) Nonlinear Problem. (b) Linear Problem. The support vectors are the (transformed) trainingpatterns and are equally close to hyperplane of separation. The support vectors are the training samples that define the optimal hyperplane and are the most difficult patterns to classify [11]. Informally speaking, theyarethe patternsmost informative of the classification task. The kernel function generates the inner products to construct machines with different types of non-linear decision surfaces in the input space [12]. 4. IMPLEMENTATION 4.1 Dataset Collection The music data is collected from music channels using a TV tuner card. A total dataset of 100 different songsisrecorded, which is sampled at 22 kHz and encoded by 16-bit. In order to make training results statisticallysignificant,trainingdata should be sufficient and cover various genres of music. 4.2 Feature Extraction In this work fixed length frames with duration of 20 ms and 50 percentages overlap (i.e., 10 ms) are used. An input wav file is given to the feature extractiontechniques.Tempogram 12 dimensional feature values will be calculated for the given wav file. The above process is continued for 100 number of wav files. 4.3 Classification When the featureextractionprocessisdonethemusicshould be classified. We select 75 music samples as training data including 25 classic music, 25 pop music and 25 rock music. The rest 25 samples are used as a test set. For the SVM-1 which is used to classify music into pop and classic used for training. For the SVM-2 which is used to classify classic and rock are used for training. Table 1 shows Performance of music classification in different SVM kernel function. Table -1: Performance of music genre classification in different SVM kernel function. SVM Kernels Performance Polynomial 89% Gaussian 95% Sigmoidal 88% Chart -1: Performance of music classification for different duration of music clips The performance of SVM for different duration as shown in Chart 1 shows that when the duration were increased from 10 to 20 there was no considerable increase in the performance. 5. CONCLUSION In this paper, we have proposed an automatic music genre classification system using SVM. Tempogram is calculated as features to characterize audio content. SVM learning algorithm has been used for the classification of genre classes of music by learning from training data. Two nonlinear support vector machine classifiers are developed to obtain the optimal class boundaries between classic and pop, pop and rock by learning from training data. Experimental results show that the proposed audio support vector machine learning method has good performance in musical genre classification scheme is very effective and the accuracy rate is 95%. REFERENCES [1] C. Joder, S. Essid, G. Richard, and S. Member, “Temporal Integration for Audio Classification With Application to Musical InstrumentClassification,”IEEETransactionson Speech and Audio Processing, vol. 17, no. 1, pp. 174– 186, 2009.
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1060 [2] Serwach, M., & Stasiak, B., GA-based parameterization and feature selection for automatic music genre recognition. In Proceedings of 2016 17th International Conference Computational Problems of Electrical Engineering, CPEE 2016. [3] Dijk, L. Van., Radboud Universiteit Nijmegen Bachelorthesis Information Science Finding musical genre similarity using machine learning techniques, 1– 25, 2014. [4] Mi Tian, Gy ¨ orgy Fazekas, Dawn A. A. Black, Mark Sandler, On the use of the tempogram to describe audio content and its Application to music structural segmentation, IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP),2015. [5] Venkatesh Kulkarni, Towards Automatic Audio Segmentation of Indian Carnatic Music, Master Thesis, Friedrich Alexander University, 2014. [6] Eronen, A. and Klapuri, A., “Music Tempo Estimation with k-NN regression,” IEEE Transactions on Audio, Speech and Language Processing, vol. 18, no. 1, pp. 50- 57, 2010. [7] Gui, Wenming & Sun, Yao & Tao, Yuting & Li, Yanping & Meng, Lun & Zhang, Jinglan., A Novel Tempogram Generating Algorithm Based on Matching Pursuit. Applied Sciences, 2018. [8] Chungsoo Lim Mokpo, Yeon-Woo Lee, and Joon-Hyuk Chang, “New Techniques for Improving the practicality of a SVM-Based Speech/Music Classifier,” IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1657-1660, 2012. [9] Hongchen Jiang, JunmeiBai, Shuwu Zhang, and Bo Xu, “SVM-Based Audio Scene Classification,” IEEE International Conference Natural Language Processing and Knowledge Engineering, Wuhan, China, pp. 131- 136, October 2005. [10] Lim and Chang, “Enhancing Support Vector Machine- Based Speech/Music Classification using Conditional Maximum a Posteriori Criterion,”Signal Processing,IET, vol. 6, no. 4, pp. 335-340, 2012. [11] Md. Al Mehedi Hasan and Shamim Ahmad. predSucc- Site: Lysine Succinylation Sites PredictioninProteinsby using Support Vector Machine and Resolving Data Imbalance Issue. International Journal of Computer Applications 182(15):8-13, September 2018. [12] Hend Ab. ELLaban, A A Ewees and Elsaeed E AbdElrazek. A Real-Time System for Facial Expression Recognition using Support Vector Machines and k- Nearest Neighbor Classifier. International Journal of Computer Applications 159(8):23-29, February 2017.