Turkish Music Emotion
Donated on 8/14/2023
There are four different classes of music emotions in the dataset: happy, sad, angry, and relax.
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
Classification
Feature Type
Real, Integer
# Instances
400
# Features
50
Dataset Information
Additional Information
The dataset is designed as a discrete model, and there are four classes in the dataset: happy, sad, angry, relax. To prepare the dataset, verbal and non-verbal music are selected from different genres of Turkish music. A total of 100 music pieces are determined for each class in the database to have an equal number of samples in each class. There are 400 samples in the original dataset as 30 seconds from each sample. Number of Data in Each class Relax 100 Happy 100 Sad 100 Angry 100
Has Missing Values?
No
Introductory Paper
By M. Er, Ibrahim Berkan Aydilek. 2019
Published in International Journal of Computational Intelligence Systems
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Class | Target | Categorical | no | ||
| _RMSenergy_Mean | Feature | Continuous | no | ||
| _Lowenergy_Mean | Feature | Continuous | no | ||
| _Fluctuation_Mean | Feature | Continuous | no | ||
| _Tempo_Mean | Feature | Continuous | no | ||
| _MFCC_Mean_1 | Feature | Continuous | no | ||
| _MFCC_Mean_2 | Feature | Continuous | no | ||
| _MFCC_Mean_3 | Feature | Continuous | no | ||
| _MFCC_Mean_4 | Feature | Continuous | no | ||
| _MFCC_Mean_5 | Feature | Continuous | no |
0 to 10 of 51
Additional Variable Information
Features such as Mel Frequency Cepstral Coefficients (MFCCs), Tempo, Chromagram, Spectral and Harmonic features have been extracted to analyze the emotional content in music signals. MIR toolbox is used for feature extraction.
Class Labels
relax, happy, sad, angry
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset turkish_music_emotion = fetch_ucirepo(id=862) # data (as pandas dataframes) X = turkish_music_emotion.data.features y = turkish_music_emotion.data.targets # metadata print(turkish_music_emotion.metadata) # variable information print(turkish_music_emotion.variables)
Er,Mehmet Bilal. (2023). Turkish Music Emotion. UCI Machine Learning Repository. https://doi.org/10.24432/C5JG93.
@misc{misc_turkish_music_emotion_862,
author = {Er,Mehmet Bilal},
title = {{Turkish Music Emotion}},
year = {2023},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5JG93}
}Keywords
Creators
Mehmet Bilal Er
bilal.er@harran.edu.tr
Harran University
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.