Hierarchical mining with complex networks for music genre classification

Salazar, Andrés Eduardo Coca

doi:10.1016/j.dsp.2022.103559

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…Classification with kNN in the initial stage prepares the features used for classification. In this method, it is necessary to determine the k value of the number of nearest neighbors that will be used for the classification process [30]. The value of k is usually an odd number.…”

mentioning

confidence: 99%

The object detection model uses combined extraction with KNN and RF classification

Kurniati,

Manongga,

Sembiring

et al. 2024

IJEECS

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Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1-score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification.

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mentioning

confidence: 99%

The object detection model uses combined extraction with KNN and RF classification

Kurniati,

Manongga,

Sembiring

et al. 2024

IJEECS

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GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

Eduardo

Salazar

2023

2023 International Joint Conference on Neural Networks (IJCNN)

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The implementation of a proposed deep-learning algorithm to classify music genres

Liu

2024

Open Computer Science

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To improve the classification effect of music genres in the digital music era, the article employs deep-learning algorithms to improve the performance of the classification of music genres. An auxiliary (estimated) model is constructed to estimate the amount of unmeasured data in the dual-rate system to enhance the recognition effect of music features. Moreover, a dual-rate output error model to identify such impacts is proposed to eliminate the impact of corrupt data caused by the estimation, which eventually leads to the further improvement of the proposed model called dual-rate multi-innovation forgetting gradient algorithm based on the auxiliary model. In addition, the article employs linear time-varying forgetting factors to improve the stability of the system, advances the recognition effect of music features through enhancement processing, and combines a deep-learning algorithm to construct a classification system of music genres. The result shows that the classification of the music genre system based on a deep-learning algorithm has a good music genre classification effect.

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Hierarchical mining with complex networks for music genre classification

Cited by 5 publications

References 22 publications

The object detection model uses combined extraction with KNN and RF classification

The object detection model uses combined extraction with KNN and RF classification

GLSI Texture Descriptor Based on Complex Networks for Music Genre Classification

The implementation of a proposed deep-learning algorithm to classify music genres

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