Abstract:
"The historical musicology of Sri Lankan music binds with Sri Lankan's rich culture. The roots of
Sri Lankan music began with Folk music around 1400 AD. Sri Lankan music considerably
influenced Buddhist traditions with the development of Kolam and puppetry/ Nurthi music. Music
is considered to be a universal language in the modern era. Whatever the language, music is
considered mixed with emotions where listeners would stick to listen. Sinhala music tends to
represent emotional representation to its listeners based on the melody and lyrics. Russell's
circumplex model has been used in this research to classify songs into Happy / Calm and Sad
emotions. The study uses JAudio to extract music information from the songs. Machine Learning
techniques like Random Forest, SMO, Naive Bayes, Decision Tree and logistic regression
algorithms were used in doing the classification. NLP was conducted using different word
embedding techniques like TFIDF, Word2Vec and using pre-trained BERT models to get the
classification.
SMO performed well with all extracted features from JAudio, with an accuracy of 75.92%. Reliefbased attribute selection increased the SMO accuracy to 79.01%. Correlation-based attribute
selection had 76.54% accuracy with SMO. The BERT model trained on top of xlm-roberta-based
gained the highest accuracy of 61.22% in categorizing emotional labels using the lyrical data.
The best performance was recorded with SMO with Relief-based attribute selection for music
information with Machine Learning. The best NLP technique was the BERT model trained using
transformers on top of the xlm-roberta-based model."
