An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 1 | march 2023
Published
21 March, 2023
Creating good music is truly a laborious task since it requires lots of effort, extended time, and many instruments. When the tune of music does not sound good after being composed with much toil, then composers must discard it, which is a hectic job. Hence, an easy way to compose the music which will require less time and less effort is required. Genetic algorithm is a possible way of searching the solution to the problem in large dimension search space. Genetic algorithm (GA), a part of soft computing in the field of music composition can be used to solve this issue. This paper proposes the use of GA for composing music and the use of fitness function to select more melodious music. In GA, for music creation, two musical segments will act as parent nodes for creating new music, and by applying genetic operators, there is a change in the music such that breaks are modified between the tunes. Music which sounds pleasing is chosen with the user’s help using fitness function, and if the user is satisfied with the generated tune, then the process of generating the music is terminated; otherwise, the selected musical tune by the fitness function will act as the parent node for the next generation of musical tune. Moreover, this work explains which fitness function to be applied on the specific problem.
KeywordsMusic composition Genetic Algorithm (GA) Fitness functions music notes melody soft computing
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