Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Deniable Authentication Encryption for Privacy Protection using Blockchain
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Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
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Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
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Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks
Volume-3 | Issue-1
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1
An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4
Volume - 4 | Issue - 3 | september 2022
Published
25 August, 2022
The ability of music to spread joy and excitement across lives, makes it widely acknowledged as the human race's universal language. The phrase "music genre" is frequently used to group several musical styles together as following a shared custom or set of guidelines. According to their unique preferences, people now make playlists based on particular musical genres. Due to the determination and extraction of appropriate audio elements, music genre identification is regarded as a challenging task. Music information retrieval, which extracts meaningful information from music, is one of several real - world applications of machine learning. The objective of this paper is to efficiently categorise songs into various genres based on their attributes using various machine learning approaches. To enhance the outcomes, appropriate feature engineering and data pre-processing techniques have been performed. Finally, using suitable performance assessment measures, the output from each model has been compared. Compared to other machine learning algorithms, Random Forest along with efficient feature selection and hyperparameter tuning has produced better results in classifying music genres.
KeywordsFeature selection hyperparameter tuning music genre classification Music Information Retrieval (MIR)
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