Comparative Analysis Implementation of Queuing Songs in Players
Using
Audio Clustering Algorithm
Exploring the intersection of audio signal processing,
machine learning, and intelligent media systems.
Journal Paper
My published research in audio processing and machine learning.
Comparative Analysis Implementation of Queuing Songs in Players
Using
Audio Clustering Algorithm
Comparative study of k-means, DBSCAN, and adaptive algorithms for clustering audio data. Analyzes performance, accuracy, and efficiency to determine ideal grouping methods.
Key Contributions
The main contributions and findings of this research.
Comparative benchmarking of three clustering algorithms
Time complexity analysis (O(n log n) vs O(n²))
Evaluation using Precision, Recall, and F-Measure
Practical application in intelligent music queuing systems
Demonstrated superiority of density-based and adaptive methods
Research Objectives
The primary goals and focus areas of this study.
Compare centroid-based, density-based, and adaptive clustering methods
Analyze algorithm performance using evaluation metrics
Study time complexity and asymptotic behavior
Identify the most suitable clustering method for intelligent music queuing systems
Research Approach
The step-by-step methodology used in this research.
Feature Representation
K-Means Implementation
DBSCAN Implementation
Adaptive Clustering
Performance Evaluation
What's Next
Upcoming research directions and planned publications.
Deep Audio Embeddings
ExploringExploring transformer-based models for richer audio feature representations in clustering tasks.
Real-time Adaptive Queuing
PlannedBuilding adaptive queuing systems that learn user preferences in real-time using reinforcement learning.
Cross-modal Analysis
IdeationCombining audio features with lyrical and visual metadata for holistic music understanding.
Edge Deployment
IdeationOptimizing clustering algorithms for resource-constrained mobile and IoT music devices.
Author Team
The researchers and engineers who contributed to this publication.
Lead Researcher & Faculty Advisor
Robotics & ML Engineer
Associate Researcher
Associate Researcher
Research Analyst