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Enhanced capsule neural network with advanced triangulation topology aggregation optimizer for music genre
Linlin Jiang1, Lei Yang2, Shakiba Azimi3,4
1Music Academy, Baicheng Normal University, Baicheng, 137000, Jilin, China.
This study introduces a novel music genre classification method using Capsule Neural Network (CapsNet) optimized by the Triangulation Topology Aggregation Optimizer (ATTAO). The proposed approach demonstrates superior performance in music genre recognition across benchmark datasets.
Area of Science:
- Computer Science
- Signal Processing
- Machine Learning
Background:
- Music genre classification is complex due to intricate signal characteristics.
- Existing methods face challenges in capturing nuanced musical features.
Purpose of the Study:
- To present an innovative method for music genre classification.
- To enhance classification accuracy using Capsule Neural Networks (CapsNet).
Main Methods:
- Utilized Capsule Neural Network (CapsNet) for music genre classification.
- Optimized CapsNet parameters with an advanced Triangulation Topology Aggregation Optimizer (ATTAO).
- Applied the method to GTZAN and Ballroom datasets for evaluation.
Main Results:
- The proposed CapsNet-ATTAO method outperformed several state-of-the-art techniques.
- Demonstrated superior efficacy and resilience in music genre recognition tasks.
- Preserved spatial and hierarchical information effectively through CapsNet and ATTAO.
Conclusions:
- The CapsNet-ATTAO approach offers a highly effective solution for music genre classification.
- The method shows significant improvements over existing techniques on benchmark datasets.
- Highlights the potential of CapsNet and ATTAO in audio signal analysis.
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