A machine learning approach to classify sinhala songs based on userratings

A Machine Learning Approach to
Classify Sinhala Songs Based On
User Ratings
Authors:
H.M.T. Paranagama1
M.K.A. Ariyaratne1
S.C.M.D.S. Sirisuriya1
1Department of Computer Science, Faculty of Computing,
General Sir John Kotelawala Defence University,
Ratmalana.
10th International Research Conference of General Sir John Kotelawala Defence University
August 3rd – 4th 2017

1.
Introduction
Background of the problem
2

3
❏ Are all musicians popular?
❏ How does popular musicians differ from the
unpopular ?
❏ Can unpopular musicians become popular?
❏ If so.How?
Problem at hand

Proposed
Solution
A web based tools that uses Multi
Layer Neural Networks to
determine/predict a rating of a given
song.
4

2.
Literature Review
What others have done
5

6
Most of the work related to music
and machine learning were done in :
● Music genre classification
● Music recommendation
Methods used:
● Pattern recognition
● Similarity measurements
● Music information retrieval

3.
Design
High level view of the system
7

Overview of the tool in operation
8
1
2
3
4
5

Architecture of the multi-layer neural network
9

4.
Implementation
Building the system
10

Implementation
Input Features
●Tempo
●Mel-Frequency Cepstral Co-efficient (MFCC)
●Harmonic element
Output Class
●Poor(0)
●Moderate(1)
●Excellent(2)
11

Implementation (Cont...)
Activation functions used
●Sigmoid(on hidden layers)
●Softmax (on Output layer)
12

Technologies used
Libraries
●TensorFlow
●Librosa
●Numpy
●Sklearn
Programming Language
●Python
Web development
●Flask,HTML,CSS,Bootstrap
13

5.
Results
Performance evaluation
14

Change in the accuracy against training epochs
Performance measurements
15

Performance measurements(Cont...)
16
Changes in cost against training epochs

Changes in weights against training
epochs
17

Changes in biases against training
epochs
18

6.
Optimization
Trying to improve the system
19

Tuning Hyperparameters
Parameter
configuration
Training
Epochs
# of Neurons
in hidden
layer 1
# of Neurons
in hidden
layer 2
Learning
rate
Accuracy
1 1000 1000 500 0.05 81%
2 1000 500 500 0.05 80%
3 500 60 60 0.05 85%
4 500 60 60 0.1 85%
20

Performance Optimization
21
➢ Using Clustering(K-Means) to determine the labels
➢ Using pre-stored data for training and testing
purposes rather than real-time extraction

7.
Discussion
Matters to focus on
22

23

Acknowledgements
Special thanks to all the people who made and released
these awesome resources for free:
● Presentation template by SlidesCarnival
● Photographs by Unsplash & Death to the Stock Photo
(license)
Further gratitude is extended to the following individuals
for their advice and guidance:
● MR.Chandrasekara(Provider of the music repository)
● Ms.M.K.A.Ariyaratne(Supervisor of this project)
● Dr.T.G.I.Fernando(pointing out the research problem)
24

References
● https://www.tensorflow.org/
● https://librosa.github.io/librosa/
● https://aqibsaeed.github.io/2016-09-03-urban-sound-
classification-part-1/
25

Thanks!
Any questions?
26
Contact Information:

A machine learning approach to classify sinhala songs based on userratings

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