Optimized deep learning for genre classification via improved moth flame algorithm

Kumaraswamy, Balachandra

doi:10.1007/s11042-022-12254-y

Cited by 11 publications

(5 citation statements)

References 44 publications

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“…The dataset for analysis was downloaded from Reference 43. Accordingly, the performance of the adopted approach was measured over extant models such as ensemble classifiers + WOA, 44 ensemble classifiers + SLnO, 38 ensemble classifiers + SA‐SLnO, 45 and ensemble classifiers + IMFO 34 regarding certain positive and negative measures. Here, the performance analysis was performed for varied learning percentage (LP) that ranges from 50, 60, 70, and 80.…”

Section: Resultsmentioning

confidence: 99%

“…The updated distances 33 of positive matrix

{R}_N

and

{H}_N

is shown in Equations () and (), wherein,

One

indicates identity matrix 34

H:= R+{\mu}_w\left(\frac{B}{RH}- One\right){H}^T

H:= H+{\mu}_h{R}^T\left(\frac{B}{RH}- One\right)

…”

Section: Extraction Of Proposed Featuresmentioning

confidence: 99%

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Optimal classification of music genres based on acoustic and visual features

Kumaraswamy¹

2022

Concurrency and Computation

Self Cite

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Summary Generally, music includes numerous genres like jazz, pop and so forth. Every genre encompasses diverse musical instruments. For classifying music manually, the individual should pay attention to the song and choose the genre. It consumes more time and the individual should know diverse genres. This article aims to introduce an innovative music genre classification model by considering 2 phases. Initially, short‐time Fourier transform, pitch, Timbre, MNMF, and proposed LBP and bag of words‐based features are extracted. Subsequently, the extracted features are subjected to ensemble classifiers, where support vector machine, neural network, random forest are deployed. The output attained from ensemble classifiers is then classified using deep belief network (DBN) which classifies the music genres in a precise way. For precise detection, the training of DBN is made optimal via tuning the weights. This optimization is carried out by a Whale Integrated Sea Lion Algorithm. At last, the supremacy of the developed model is examined via the evaluation of extant techniques.

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Section: Resultsmentioning

confidence: 99%

“…The updated distances 33 of positive matrix

{R}_N

and

{H}_N

is shown in Equations () and (), wherein,

One

indicates identity matrix 34

H:= R+{\mu}_w\left(\frac{B}{RH}- One\right){H}^T

H:= H+{\mu}_h{R}^T\left(\frac{B}{RH}- One\right)

…”

Section: Extraction Of Proposed Featuresmentioning

confidence: 99%

Optimal classification of music genres based on acoustic and visual features

Kumaraswamy¹

2022

Concurrency and Computation

Self Cite

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“…Kumaraswamy and Poonacha (2021) used the self-adaptive sea lion optimization algorithm to train a neural network that receives a set of features as input. Similarly, Kumaraswamy (2022) uses an improved moth flame optimization algorithm for weight learning of the neural network for the MGC task. Our work differs from these approaches by focusing on the architecture design of the CNN neural network for the MGC task rather than tuning parameters using an optimization algorithm.…”

Section: Related Workmentioning

confidence: 99%

Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection

Singh

Biswas

2023

Expert Systems

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Convolutional neural networks (CNNs) have succeeded in various domains, including music information retrieval (MIR). Music genre classification (MGC) is one such task in the MIR that has gained attention over the years because of the massive increase in online music content. Accurate indexing and automatic classification of these large volumes of music content require high computational resources, which pose a significant challenge to building a lightweight system. CNNs are a popular deep learningbased choice for building systems for MGC. However, finding an optimal CNN architecture for MGC requires domain knowledge both in CNN architecture design and music. We present MGA-CNN, a genetic algorithm-based approach with a novel stochastic hyperparameter selection for finding an optimal lightweight CNN-based architecture for the MGC task. The proposed approach is unique in automating the CNN architecture design for the MGC task. MGA-CNN is evaluated on three widely used music datasets and compared with seven peer rivals, which include three automatic CNN architecture design approaches and four manually designed popular CNN architectures. The experimental results show that MGA-CNN surpasses the peer approaches in terms of classification accuracy, parameter numbers, and execution time. The optimal architectures generated by MGA-CNN also achieve classification accuracy comparable to the manually designed CNN architectures while spending fewer computing resources.

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“…Accurate results on the GTZAN dataset were obtained. Balachandra [12] improved the moth algorithm IMOF and successfully applied it to the task of music genre classification. He achieved good classification results by optimizing the weights of a deep belief network (DBN) and performing classification.…”

Section: Introductionmentioning

confidence: 99%

Music Genre Classification Based on VMD-IWOA-XGBOOST

Gan,

Huang,

Shao

et al. 2024

Mathematics

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Music genre classification is significant to users and digital platforms. To enhance the classification accuracy, this study proposes a hybrid model based on VMD-IWOA-XGBOOST for music genre classification. First, the audio signals are transformed into numerical or symbolic data, and the crucial features are selected using the maximal information coefficient (MIC) method. Second, an improved whale optimization algorithm (IWOA) is proposed for parameter optimization. Third, the inner patterns of these selected features are extracted by IWOA-optimized variational mode decomposition (VMD). Lastly, all features are put into the IWOA-optimized extreme gradient boosting (XGBOOST) classifier. To verify the effectiveness of the proposed model, two open music datasets are used, i.e., GTZAN and Bangla. The experimental results illustrate that the proposed hybrid model achieves better performance than the other models in terms of five evaluation criteria.

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Optimized deep learning for genre classification via improved moth flame algorithm

Cited by 11 publications

References 44 publications

Optimal classification of music genres based on acoustic and visual features

Optimal classification of music genres based on acoustic and visual features

Lightweight convolutional neural network architecture design for music genre classification using evolutionary stochastic hyperparameter selection

Music Genre Classification Based on VMD-IWOA-XGBOOST

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