Rafayet Hossain's repositories
BanglaMusicStylo-A-Stylometric-Dataset-of-Bangla-Music-Lyrics
With the rapid growth of Bangla music industry huge volume of Bangla songs are produced every day. Immense number of producers, lyricists, singers and artists are involved in production of songs from different genres. Among many genres of Bangla music; classical, folk, baul, modern music, Rabindra Sangeet, Nazrul Geeti, film music, rock music and fusion music has gained the highest popularity. Lyricists try to express their feelings and views towards any situation or subject through their writings. Therefore, each lyricist have their own dictionary of thoughts to put on music lyrics. In this paper, we have presented “BanglaMusicStylo”, the very first stylometric dataset of Bangla music lyrics. We have collected 2824 Bangla song lyrics of 211 lyricists in a digital form. All the lyrics are stored in text format for further use. This dataset could be used for stylometric analysis such as authorship attribution, linguistic forensics, gender identification from textual data, Bangla music genre classification, vandalism detection, emotion classification etc. Identifying the significant research opportunities in this area, we have formalized this dataset which could be used for stylometric analysis.
Hospitality-Management-Dashboard
A dashboard using Power BI
English-Music-Lyrics-Dataset-Contains-300-songs
Music has a soothing impact on listener’s mood and emotional states. Apart from the rhythm, sequence, instrumental effects on a song, lyrics could be considered as the most vital element. Lyricists’ mood and affection towards a song while writing could be understand from the lyrics. As lyrics are nothing but a form of text, the elements of fictions such as language tone, language style, diction and voice are well maintained in music lyrics. Understanding the tone of a song both language and emotional tones are essential to develop different interactive applications. Music players, video repositories, video sharing sites could use the understandings to recommend next song to play according to the music interest or mood of the listeners. Understanding the language and emotional tone is thus a challenging task. In this paper, we have investigated the possibilities to use IBM Watson Tone Analyzer, an open-source API service to analyze language and emotional tones from song lyrics. We have extracted the features from a 300 English song dataset using the supported API service and formulated a machine learning methodology to classify the language tone (analytical, confident and tentative) and emotional tone (anger, fear, joy and sadness). For classification purpose, we have applied different classifiers includes Naïve Bayes, decision trees and variations, ensemble classifier such as random forest, sequential minimal optimization and simple logistic regression.
exploratory-data-analysis-population
An Exploratory Data Analysis of Population Data
MagicMirror
An android Application using Unity3D, C#, Vuforia
Music-Data-set-contains-200-song-
We have 200 English song. In the preprocessing part we have remove the verse, chorus and some digits that defines the repetition of that lyrics. Because these verse, chorus and digits are not impactable on title recommendation. We also elaborate the contraction word. After doing that, we have used modified LDA algorithm and from there we determine the percentage of words from a lyrics line. We pick the sentence as the title of the song which sentence contains the greater percentage of word.
Music-Title-Estimation
Music title predictor based on topic modeling algorithm
Naz-Day2-OOPRelation
Gradution Traioning program
rafayetrafi
Config files for my GitHub profile.
RoadAccident-DataAnalysis-Excel
Road Accident Data Analysis
VisualHelp
A real time Object detection and Live Text detection mobile Application for blind people and it successfully give voice notification that help blind people as their third eye.