This paper is published in Volume-6, Issue-3, 2020
Area
Machine Learning
Author
Madhuvanti Puranik, Janardhana Bhat K., Arpitha A., Deepthi R., Akshay Istev Dias
Org/Univ
Srinivas Institute of Technology, Mangalore, Karnataka, India
Pub. Date
10 June, 2020
Paper ID
V6I3-1472
Publisher
Keywords
Music Emotion, Music Information Retrieval, Music Mood, Attribute Weighting, Class, Local Optimization Naive Bayes, Cloud Storage

Citationsacebook

IEEE
Madhuvanti Puranik, Janardhana Bhat K., Arpitha A., Deepthi R., Akshay Istev Dias. Framework for classification of music based on tempo using Naïve Bayes and cloud storage, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Madhuvanti Puranik, Janardhana Bhat K., Arpitha A., Deepthi R., Akshay Istev Dias (2020). Framework for classification of music based on tempo using Naïve Bayes and cloud storage. International Journal of Advance Research, Ideas and Innovations in Technology, 6(3) www.IJARIIT.com.

MLA
Madhuvanti Puranik, Janardhana Bhat K., Arpitha A., Deepthi R., Akshay Istev Dias. "Framework for classification of music based on tempo using Naïve Bayes and cloud storage." International Journal of Advance Research, Ideas and Innovations in Technology 6.3 (2020). www.IJARIIT.com.

Abstract

Music is considered as one the best form of expression of emotions. The music that people hear is governed by what mood they are in. The characteristics of music such as rhythm, melody, harmony, pitch, and timbre play a major role in human physiology and psychological functions, thus altering their mood. The Naive Bayes classifier is considered a popular classification technique in machine learning. It’s been shown to be very effective on a range of test data sets. However, the strong assumption that each attribute is independent of each other is often violated in real-world problems. In this paper, we have applied locally weighted Naïve Bayes to data stored within cloud to automate the process of classification of music.
Paper PDF