This paper proposes a system to automatically identify the raga and raga cycle of a song by analyzing note frequencies. It calculates the coefficient of variance of note frequencies to measure similarity between songs. If the coefficient of variance is between 0-1, the songs are from the same raga cycle and are similar. The system was tested on songs and accurately identified whether songs were from the same or different raga cycles based on their coefficient of variance.
This was my Technical Article paper presented on Symposium on Vision-Smart India 2017 held at CSI-MCKVIE Student Chapter and MCKV Institute of Engineering
This was my Technical Article paper presented on Symposium on Vision-Smart India 2017 held at CSI-MCKVIE Student Chapter and MCKV Institute of Engineering
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
The integrated set of functions written in Matlab, dedicate to the extraction of audio tones of musical options connected
to timbre, tonality, rhythm or type. A study on feature analysis in today’s atmosphere, most of the musical information retrieval
algorithmic programs square measure matter based mostly algorithm so we have a tendency that cannot able to build a
classification of musical instruments. In most of the retrieval system, the classification is often done on the premise of term
frequencies and use of snippets in any documents. We have a tendency to gift MIR tool case, associate degree for recognition of
classical instruments, using machine learning techniques to select and evaluate features extracted from a number of different
feature schemes was described by Deng et al. The performance of Instrument recognition was checked using with different
feature selection and algorithms.
Performance Comparison of Musical Instrument Family Classification Using Soft...Waqas Tariq
Nowadays, it appears essential to design automatic and efficacious classification algorithm for the musical instruments. Automatic classification of musical instruments is made by extracting relevant features from the audio samples, afterward classification algorithm is used (using these extracted features) to identify into which of a set of classes, the sound sample is possible to fit. The aim of this paper is to demonstrate the viability of soft set for audio signal classification. A dataset of 104 (single monophonic notes) pieces of Traditional Pakistani musical instruments were designed. Feature extraction is done using two feature sets namely perception based and mel-frequency cepstral coefficients (MFCCs). In a while, two different classification techniques are applied for classification task, which are soft set (comparison table) and fuzzy soft set (similarity measurement). Experimental results show that both classifiers can perform well on numerical data. However, soft set achieved accuracy up to 94.26% with best generated dataset. Consequently, these promising results provide new possibilities for soft set in classifying musical instrument sounds. Based on the analysis of the results, this study offers a new view on automatic instrument classification
DETECTING AND LOCATING PLAGIARISM OF MUSIC MELODIES BY PATH EXPLORATION OVER ...csandit
To the best of our knowledge, the issues of automatic detection of music plagiarism have never
been addressed before. This paper presents the design of an Automatic Music Melody
Plagiarism Detection (AMMPD) method to detect and locate the possible plagiarism in music
melodies. The key contribution of the work is an algorithm proposed to address the challenging
issues encountered in the AMMPD problem, including (1) the inexact matching of noisy and
inaccurate pitches of music audio and (2) the fast detection and positioning of similar
subsegments between suspicious music audio. The major novelty of the proposed method is that
we address the above two issues in temporal domain by means of a novel path finding approach
on a binarized 2-D bit mask in spatial domain. In fact, the proposed AMMPD method can not
only identify the similar pieces inside two suspicious music melodies, but also retrieve music
audio of similar melodies from a music database given a humming or singing query.
Experiments have been conducted to assess the overall performance and examine the effects of
various parameters introduced in the proposed method.
A Software Design Document
On
Honey Beats
(Music Application)
Submitted in partial fulfillment of the requirements
for the award of the degree of
Bachelor of Technology
in
Computer Science and Engineering
Submitted by
Kartik (1719210142)
Abhinav Soni (1719210012)
(G-2020-125)
Under the supervision of
Mr.Ganesh Prasad Pal
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
G.L. BAJAJ INSTITUTE OF TECHNOLOGY & MANAGEMENT
Affiliated to
DR. APJ ABDUL KALAM TECHNICAL UNIVERSITY, LUCKNOW
2020-2021
Abstract
In today’s extremely developed world, each minute, individuals round the globe
specific themselves via numerous platforms on the net. And in every minute, an
enormous quantity of unstructured information is generated. This information is
within the style of text that is gathered from forums and social media websites.
Such information is termed as massive information. User opinions square measure
associated with a vary of topics like politics, latest gadgets and merchandise.
Social media, public sector, private sector, IOT(Internet Of things).
In this document it is explained the development of a project that has been
presented in order to finish the degree in computer engineering. The content
begins with an introduction where are explained the motivations to undertake this
Music Application (Honey Beats) project , by providing them with an IT solution .
From the research of current solutions that have similarities with the
objectives of this project, it has been set a plan to follow in the development of this
project where there have been distributed the tasks to develop over a temporal
framework and there have been evaluated its costs and the impact that the project
has in the different dimensions of the sustainability. This document presents the first
version of the project that have been developed, but over the time there will be
added new features to extend the functionalities and utilities that the solution will
provide to their users.
Acknowledgement
The merciful guidance bestowed to us by the almighty made us stick out this project to
a successful end. We humbly pray with sincere heart for his guidance to continue
forever.
We would like to show our greatest appreciation to , Mr.Ganesh Prasad Pal
project guide at GLBITM, Gr. Noida. We always feel motivated and encouraged every
time by his valuable advice and constant inspiration; without his encouragement and
guidance this project would not have materialized.
Words are inadequate in offering our thanks to the Head of Department of Computer
Science & Engineering at G.L. BAJAJ INSTITUTE OF TECHNOLOGY &
MANAGEMENT for his encouragement and cooperation in carrying out this project
work. The guidance and
Indian Classical Dance Mudra Classification Using HOG Features and SVM Classi...IJECEIAES
Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.
Towards Web-Scale Analysis of Musical Structure David De Roure
SALAMI (Structural Analysis of Large Amounts of Music Information) is an ambitious computational musicology project which applies a computational approach to the huge volume of digital recordings now available from such sources as the Internet Archive. It aims to deliver a very substantive corpus of musical analyses in a common framework for use by music scholars, students and beyond, and to establish a web-based methodology and tooling which will enable others to add to this in the future. In its first phase the project has conducted a significant exercise in ground truth collection with 1000 recordings analysed by music students and shortly to be published as open Linked Data.
J. S. Downie, D. De Roure, K. Page.Towards Web-Scale Analysis of Musical Stru...MusicNet
J. Stephen Downie (Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign), David De Roure (Oxford e-Research Centre, University of Oxford) and Kevin Page (Oxford e-Research Centre, University of Oxford).
Music Linked Data Workshop, 12 May 2011, JISC, London.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Knn a machine learning approach to recognize a musical instrumentIJARIIT
The integrated set of functions written in Matlab, dedicate to the extraction of audio tones of musical options connected
to timbre, tonality, rhythm or type. A study on feature analysis in today’s atmosphere, most of the musical information retrieval
algorithmic programs square measure matter based mostly algorithm so we have a tendency that cannot able to build a
classification of musical instruments. In most of the retrieval system, the classification is often done on the premise of term
frequencies and use of snippets in any documents. We have a tendency to gift MIR tool case, associate degree for recognition of
classical instruments, using machine learning techniques to select and evaluate features extracted from a number of different
feature schemes was described by Deng et al. The performance of Instrument recognition was checked using with different
feature selection and algorithms.
Performance Comparison of Musical Instrument Family Classification Using Soft...Waqas Tariq
Nowadays, it appears essential to design automatic and efficacious classification algorithm for the musical instruments. Automatic classification of musical instruments is made by extracting relevant features from the audio samples, afterward classification algorithm is used (using these extracted features) to identify into which of a set of classes, the sound sample is possible to fit. The aim of this paper is to demonstrate the viability of soft set for audio signal classification. A dataset of 104 (single monophonic notes) pieces of Traditional Pakistani musical instruments were designed. Feature extraction is done using two feature sets namely perception based and mel-frequency cepstral coefficients (MFCCs). In a while, two different classification techniques are applied for classification task, which are soft set (comparison table) and fuzzy soft set (similarity measurement). Experimental results show that both classifiers can perform well on numerical data. However, soft set achieved accuracy up to 94.26% with best generated dataset. Consequently, these promising results provide new possibilities for soft set in classifying musical instrument sounds. Based on the analysis of the results, this study offers a new view on automatic instrument classification
DETECTING AND LOCATING PLAGIARISM OF MUSIC MELODIES BY PATH EXPLORATION OVER ...csandit
To the best of our knowledge, the issues of automatic detection of music plagiarism have never
been addressed before. This paper presents the design of an Automatic Music Melody
Plagiarism Detection (AMMPD) method to detect and locate the possible plagiarism in music
melodies. The key contribution of the work is an algorithm proposed to address the challenging
issues encountered in the AMMPD problem, including (1) the inexact matching of noisy and
inaccurate pitches of music audio and (2) the fast detection and positioning of similar
subsegments between suspicious music audio. The major novelty of the proposed method is that
we address the above two issues in temporal domain by means of a novel path finding approach
on a binarized 2-D bit mask in spatial domain. In fact, the proposed AMMPD method can not
only identify the similar pieces inside two suspicious music melodies, but also retrieve music
audio of similar melodies from a music database given a humming or singing query.
Experiments have been conducted to assess the overall performance and examine the effects of
various parameters introduced in the proposed method.
A Software Design Document
On
Honey Beats
(Music Application)
Submitted in partial fulfillment of the requirements
for the award of the degree of
Bachelor of Technology
in
Computer Science and Engineering
Submitted by
Kartik (1719210142)
Abhinav Soni (1719210012)
(G-2020-125)
Under the supervision of
Mr.Ganesh Prasad Pal
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
G.L. BAJAJ INSTITUTE OF TECHNOLOGY & MANAGEMENT
Affiliated to
DR. APJ ABDUL KALAM TECHNICAL UNIVERSITY, LUCKNOW
2020-2021
Abstract
In today’s extremely developed world, each minute, individuals round the globe
specific themselves via numerous platforms on the net. And in every minute, an
enormous quantity of unstructured information is generated. This information is
within the style of text that is gathered from forums and social media websites.
Such information is termed as massive information. User opinions square measure
associated with a vary of topics like politics, latest gadgets and merchandise.
Social media, public sector, private sector, IOT(Internet Of things).
In this document it is explained the development of a project that has been
presented in order to finish the degree in computer engineering. The content
begins with an introduction where are explained the motivations to undertake this
Music Application (Honey Beats) project , by providing them with an IT solution .
From the research of current solutions that have similarities with the
objectives of this project, it has been set a plan to follow in the development of this
project where there have been distributed the tasks to develop over a temporal
framework and there have been evaluated its costs and the impact that the project
has in the different dimensions of the sustainability. This document presents the first
version of the project that have been developed, but over the time there will be
added new features to extend the functionalities and utilities that the solution will
provide to their users.
Acknowledgement
The merciful guidance bestowed to us by the almighty made us stick out this project to
a successful end. We humbly pray with sincere heart for his guidance to continue
forever.
We would like to show our greatest appreciation to , Mr.Ganesh Prasad Pal
project guide at GLBITM, Gr. Noida. We always feel motivated and encouraged every
time by his valuable advice and constant inspiration; without his encouragement and
guidance this project would not have materialized.
Words are inadequate in offering our thanks to the Head of Department of Computer
Science & Engineering at G.L. BAJAJ INSTITUTE OF TECHNOLOGY &
MANAGEMENT for his encouragement and cooperation in carrying out this project
work. The guidance and
Indian Classical Dance Mudra Classification Using HOG Features and SVM Classi...IJECEIAES
Digital understanding of Indian classical dance is least studied work, though it has been a part of Indian Culture from around 200BC. This work explores the possibilities of recognizing classical dance mudras in various dance forms in India. The images of hand mudras of various classical dances are collected form the internet and a database is created for this job. Histogram of oriented (HOG) features of hand mudras input the classifier. Support vector machine (SVM) classifies the HOG features into mudras as text messages. The mudra recognition frequency (MRF) is calculated for each mudra using graphical user interface (GUI) developed from the model. Popular feature vectors such as SIFT, SURF, LBP and HAAR are tested against HOG for precision and swiftness. This work helps new learners and dance enthusiastic people to learn and understand dance forms and related information on their mobile devices.
Towards Web-Scale Analysis of Musical Structure David De Roure
SALAMI (Structural Analysis of Large Amounts of Music Information) is an ambitious computational musicology project which applies a computational approach to the huge volume of digital recordings now available from such sources as the Internet Archive. It aims to deliver a very substantive corpus of musical analyses in a common framework for use by music scholars, students and beyond, and to establish a web-based methodology and tooling which will enable others to add to this in the future. In its first phase the project has conducted a significant exercise in ground truth collection with 1000 recordings analysed by music students and shortly to be published as open Linked Data.
J. S. Downie, D. De Roure, K. Page.Towards Web-Scale Analysis of Musical Stru...MusicNet
J. Stephen Downie (Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign), David De Roure (Oxford e-Research Centre, University of Oxford) and Kevin Page (Oxford e-Research Centre, University of Oxford).
Music Linked Data Workshop, 12 May 2011, JISC, London.
Computational Approaches for Melodic Description in Indian Art Music CorporaSankalp Gulati
Presentation for my PhD defense, Music Technology Group, Barcelona, Spain.
Resources: http://compmusic.upf.edu/node/304
Short abstract:
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify melodic patterns, compare them both within and across music pieces, and finally, characterize the specific melodic context of IAM, the rāgas. All these analyses are done using data-driven methodologies on sizable curated music corpora. Our work paves the way for addressing several interesting research problems in the field of music information research, as well as developing novel applications in the context of music discovery and music pedagogy.
Similar to Modeling of Song Pattern Similarity using Coefficient of Variance (20)
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
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Madeline Smith, The Glasgow School of Art
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
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Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
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Modeling of Song Pattern Similarity using Coefficient of Variance
1. Modeling of Song Pattern Similarity
using Coefficient of Variance
Presented by
Gobinda Karmakar
Faculty Advisor: Prof. Sudipta Chakrabarty
18th February 2017 Techno India Salt Lake 1
2. This paper proposes a system that identifies the raga and raga cycle automatically from
particular song music has been proposed. The music origin or raga forms are the main
theme of Indian music and it is the sequence of several notes structures into a
composition in a way, which is pleasant to listening. The two song pattern similarity
identification is achieved by identifying the notes and their fundamental frequencies of
each notes of that particular song and then finds out the coefficient of variance of that
song. To establish the work consider some songs as the test data and find out the Raga
pattern similarities among them. If the coefficient of variance is between 0 and 1, it
indicates the two songs are from the same raga cycle and almost alike, otherwise they are
from two different raga cycles and their patterns are different. The primary aim behind
this paper is that it can be used as a good basis for the song pattern similarity matching
concept is the field of Musical Pattern Recognition. The focus of this study is to explore the
efficiency of Statistical Method to search for an optimum combination of frequencies of
different note structures of different songs to find the similarities in the field of Speech
processing in Quality Music Metric.
18th February 2017 Techno India Salt Lake 2
Abstract
3. Indian Classical Music (ICM)
Carnatic Classical Music (CCM)
Raga
Parent ragas (Melakarta Raga)
Raga Cycles (Chakras)
1. Moon Cycle (Indu)
2. Eyes Cycle (Netra)
3. Fire Cycle (Agni)
4. Scripture Cycle (Veda)
5. Arrow Cycle (Bana)
6. Seasons Cycle (Ritu)
7. Sages Cycle (Risi)
8. Elemental Gods Cycle (Vasu)
9. Universe Cycle (Brahma)
10. Directions Cycle (Disi)
11. Lord Shiva Cycle (Rudra)
12. Sun God Cycle (Aditya)
18th February 2017 Techno India Salt Lake 3
Introduction
4. The workflows of the proposed work are given below:
Step 1: Take a song
Step 2: Run the song through Wave Surfer software which is used in this
experiment to get the pitch value of that song. Firstly “.wav” file is used to
create the pitch file of the song. This will give all the pitches that are used
in the song and the pitch data are saved in the “.f0” format. It consists of
huge number of frequencies of monotonic song and we convert this “.f0”
into “.txt” format.
Step 3: Accepted only those frequency values within 50 to 500.
Step 4: Then the number of occurrences has been calculated of each
frequency ranging from 50 to 500.
18th February 2017 Techno India Salt Lake 4
Proposed Work
5. Step 5: Fix twelve frequencies which have highest occurrence respectively
from the list of frequencies of the .fo file
Step 6: Calculate total frequency by the following formula-
Total frequency = Frequency x Occurrence
Step 7: Calculate Mean =
Here, N=12
Step 8: Calculate Frequency Distance = Total Frequency – Mean
Step 9: Calculate Variance =
Step 10: Calculate Standard Deviation =
18th February 2017 Techno India Salt Lake 5
Proposed Work
6. Step 11: Calculate Coefficient of Variance (CV) =
Step 12: Repeat steps 2 to 12 for the second song.
Step 13: Calculate the difference of the CV (Coefficient of variation) of both
the songs.
Step 14: If (CV Difference > 0 && CV Difference <1)
Printf (“Both songs lie in same song origin and Raga cycle and the
given two songs are alike at a certain limit”)
Else
Printf (“Both songs lie in different song origin or Raga cycle and
the given two songs are not alike at a certain limit”)
Step 15: Exit.
18th February 2017 Techno India Salt Lake 6
Proposed Work
7. 18th February 2017 Techno India Salt Lake 7
Overall Workflows of the Proposed Work
8. 18th February 2017 Techno India Salt Lake 8
Result Set Analysis
Note Frequency Mean Distance from Mean Distance2 Variance Standard
Deviation
Coefficient
of Variance
First 262
239.66667
22.33333333 498.7777778
788.8888889 28.0872 11.7193
Second 265 25.33333333 641.7777778
Third 220 -19.66666667 386.7777778
Fourth 259 19.33333333 373.7777778
Fifth 247 7.333333333 53.77777778
Sixth 222 -17.66666667 312.1111111
Seventh 196 -43.66666667 1906.777778
Eighth 198 -41.66666667 1736.111111
Ninth 250 10.33333333 106.7777778
Tenth 294 54.33333333 2952.111111
Eleventh 218 -21.66666667 469.4444444
Twelveth 245 5.333333333 28.44444444
TABLE I
Coefficient of Variance of Song 1 of song origin or raga Kanakaangi
9. 18th February 2017 Techno India Salt Lake 9
Result Set Analysis
TABLE 2
Coefficient of Variance of Song 2 of song origin or raga Navaneetham
Note Frequency Mean Distance from Mean Distance2 Variance Standard
Deviation
Coefficient
of Variance
First 208
226.916667
-18.916667 357.840278
1731.734 41.614 18.339
Second 220 -6.9166667 47.8402778
Third 186 -40.916667 1674.17361
Fourth 195 -31.916667 1018.67361
Fifth 206 -20.916667 437.506944
Sixth 286 59.083333 3490.84028
Seventh 279 52.083333 2712.67361
Eighth 193 -33.916667 1150.34028
Ninth 190 -36.916667 1362.84028
Tenth 282 55.083333 3034.17361
Eleventh 290 63.083333 3979.50694
Twelveth 188 -38.916667 1514.50694
10. Difference of Coefficient of Variance = 18.339 – 11.7193 = 6.6197
Since the difference of coefficient of variance of the two song pair does
not lie between 0 and 1, therefore, both the songs are not alike and the
song pattern is different of the given two songs.
18th February 2017 Techno India Salt Lake 10
Result Set Analysis
11. Statistical methods have been used in a number of theoretical and
practical applications in the computer modeling and retrieval of music.
Coefficient of variance is a very useful tool to achieve to measure the
variability of a series of data and it is expressed as a percentage.
In this paper we have presented how similarity between notes structures
of two or more song compositions.
The primary importance of this study is to establish that the two ragas
are almost alike in the same raga cycle and there are some differences
occur in the ragas of two different raga cycles in some music parameters
like, aesthetics, moods, motifs, rhythm, tempo etc.
This contribution focuses that compositions with ragas of one raga cycle
have similar impact on music listeners in the field of Speech Processing.
18th February 2017 Techno India Salt Lake 11
Conclusion
12. Apply this concept to measure the similarity in all the derived song
origins (Raga) in future.
To built different Music Recommendation Systems based on time,
season, genre, human behaviors, human moods etc. in future
In the field of Music Classification and Music Clustering.
Applying this concept in the field of Music therapy.
18th February 2017 Techno India Salt Lake 12
Future Scope
13. Authors are grateful to the Department of Master of Computer Application
(MCA), Techno India, Salt Lake for doing the work and using the
infrastructures of the college and under which this article has been
completed. With great pleasure we mention the name of Prof. Sudipta
Chakrabarty for his remarkable guidance and encouragement. We also
intend to extend our heart-felt gratitude and special thanks to all the faculty
members of our department in our college. We also extend our thanks to
our Team Members for their co-operation during the work.
18th February 2017 Techno India Salt Lake 13
Acknowledgment
14. 1. Debashis De, Samarjit Roy, “Polymorphism in Indian Classical Music: A Pattern Recognition
Approach”, In Proceedings of International Conference on Communications, Devices and Intelligent
Systems (CODIS), IEEE, 2012, pp. 612-615.
2. Debashis De, Samarjit Roy, “Inheritance in Indian Classical Music: An Object-Oriented Analysis and
Pattern Recognition Approach”, In Proceedings of International Conference on Radar, Communication
and Computing (ICRCC), IEEE, 2012, pp. 193-198.
3. Sayanti Chakraborty, Debashis De, “Object Oriented Classification and Pattern Recognition of Indian
Classical Ragas”, In Proceedings of the 1st International Conference on Recent Advances in
Information Technology (RAIT), IEEE, 2012.
4. Sayanti Chakraborty, Debashis De, “Pattern Classification of Indian Classical Ragas based on Object
Oriented Concepts”, In Proceedings of the International Journal of Advanced Computer
5. Samarjit Roy, Sudipta Chakrabarty, Debashis De, "A Framework of Musical Pattern Recognition Using
Petri Nets." Emerging Trends in Computing and Communication. Springer India, 2014. 245-252.
6. Samarjit Roy, Sudipta Chakrabarty, Pradipta Bhakta, Debashis De, “Modelling High Performing Music
Computing using Petri Nets,” Accepted In: International Conference on Control, Instrumentation,
Energy and Communication (CIEC 2014), IEEE.
7. Sudipta Chakrabarty, Debashis De, “Quality Measure Model of Music Rhythm using Genetic
Algorithm”, In Proceedings of International Conference on Radar, Communication and Computing
(ICRCC), IEEE, 2012, pp. 125-130.
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