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Created at 6am, Apr 5
Ms-RAGArtificial Intelligence
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Analyzing Musical Characteristics of National Anthems in Relation to Global Indices
Contract ID
s3e8DDi3xtIHhmPt3mUHF7AXmnp6eCVruAFJc_JljYc
File Type
PDF
Entry Count
47
Embed. Model
jina_embeddings_v2_base_en
Index Type
hnsw

S M Rakib HasanDept. of CSEBRAC UniversityDhaka, Bangladeshsm.rakib.hasan@g.bracu.ac.bdAakar DhakalDept. of CSEBRAC UniversityDhaka, Bangladeshaakar.dhakal@g.bracu.ac.bdMs. Ayesha SiddiquaDept. of ECENitte Meenakshi ITBengaluru, Indiaayesha.siddiqua@nmit.ac.inMohammad Mominur RahmanDivision of ICTCollege of S and EHamad Bin Khalifa UniversityDoha, Qatarmora28982@hbku.edu.qaMd Maidul IslamDepartment of ECEGreen University of BangladeshDhaka, Bangladeshengr.maidul.eee@gmail.comMohammed Arfat Raihan ChowdhuryCollege of S and EHamad Bin Khalifa UniversityDoha, Qatararfatmarine27@gmail.comS M Masfequier Rahman SwapnoDept. of CSEBUBTDhaka, Bangladeshmasfequier.cse.bubt@gmail.comSM Nuruzzaman NobelDept. of CSEBUBTDhaka, Bangladeshsmnuruzzaman712@gmail.comAbstract—Music plays a huge part in shaping peoples’ psychology and behavioral patterns. This paper investigates the connection between national anthems and different global indices with computational music analysis and statistical correlation analysis. We analyze national anthem musical data to determinewhether certain musical characteristics are associated with peace, happiness, suicide rate, crime rate, etc. To achieve this, we collect national anthems from 169 countries and use computational music analysis techniques to extract pitch, tempo, beat, and other pertinent audio features. We then compare these musical characteristics with data on different global indices to ascertain whether a significant correlation exists. Our findings indicate that there may be a correlation between the musical characteristics of national anthems and the indices we investigated. The implications of our findings for music psychology and policymakers interested in promoting social well-being are discussed. This paper emphasizes the potential of musical data analysis in social research and offers a novel perspective on the relationship between music and social indices. The source code and data are made open-access for reproducibility and future researchendeavors. It can be accessed at http://bit.ly/na code Index Terms—cognitive psychology, computational music analysis,human behavior, global indicators

A. Data Collection In this study, we have collected two distinct categories of data. The initial category is national anthems. We compiled as midi files the national anthems of 171 countries from Kaggle. Our subsequent set of data included the World Peace Index, the World Suicide Rate Index, the World Crime Index, the World Happiness Index, and the World Human Development Index. All of the information was obtained from the website www.worldpopulationreview.com.
id: 610bab86525221ffb4beeae01d09042b - page: 4
B. Data Preprocessing Our midi dataset was preprocessed so that any empty files were removed. Additionally, multiple files existed for the same country. After preprocessing, we were left with midi files containing the national anthems of 166 countries. The index data contained a great deal of information deemed superfluous for our investigation. After removing these, we had the country index scores and ranks. The number of nations varied according to the index. Finally, we eliminate the countries that are not present in both datasets and retain the data for the countries that are unique to each dataset. C. Computational Music Analysis
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We chose a few musical characteristics that distinguish one song from another. Utilizing Python libraries Music21 and pretty-midi, the features were extracted. Music21 is a Python library for music theory and analysis. It provides a wide range of features for extracting musical features, such as pitch, rhythm, and harmony. It also allows to the creation of data frames and databases of musical data and visualization of musical characteristics. We have used this to extract harmonic, melodic, and rhythmic analysis of the national anthems by converting them to midi stream objects. The numerical notations of the musical features allowed us to computationally analyze the anthems. Also, it is a great tool for visualizing musical characteristics. We can directly distinguish among the music by just looking at the plots generated. that we have used to collect PrettyMIDI is another tool numerical data about the instrument, tempo, and dynamics of the music. This provides us insights into the
id: a3cbf2c9dbfba316692feacd65d6f968 - page: 4
Our extracted chosen features are described as such: Melodic Contour: This refers to the structure or pattern of a melody over time, specifically how its pitch raises and declines. It is one of the primary ways a melody creates a sense of tension and release, and it can convey emotions such as happiness, sorrow, and exhilaration. Pitch: The pitch of a sound is its perceived highness or lowness. The frequency of sound waves determines the pitch in music, with higher frequencies corresponding to higher pitches and lower frequencies corresponding to lower pitches. Pitch is an essential element of melody and harmony, and it can be used to generate contrast, tension, and resolution in musical compositions.
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curl -X POST "https://search.dria.co/hnsw/search" \
-H "x-api-key: <YOUR_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"rerank": true, "top_n": 10, "contract_id": "s3e8DDi3xtIHhmPt3mUHF7AXmnp6eCVruAFJc_JljYc", "query": "What is alexanDRIA library?"}'
        
# Query

curl -X POST "https://search.dria.co/hnsw/query" \
-H "x-api-key: <YOUR_API_KEY>" \
-H "Content-Type: application/json" \
-d '{"vector": [0.123, 0.5236], "top_n": 10, "contract_id": "s3e8DDi3xtIHhmPt3mUHF7AXmnp6eCVruAFJc_JljYc", "level": 2}'