Deep Convolutional Neural Network for musical genre classification via new Self Adaptive Sea Lion Optimization

Kumaraswamy, Balachandra; Poonacha, P.G.

doi:10.1016/j.asoc.2021.107446

Cited by 22 publications

(5 citation statements)

References 36 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%

Optimal classification of music genres based on acoustic and visual features

Kumaraswamy¹

2022

Concurrency and Computation

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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%

Optimal classification of music genres based on acoustic and visual features

Kumaraswamy¹

2022

Concurrency and Computation

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“…BAG demonstrates higher accuracy in classifying music genres compared to the other four classifiers. The paper introduces a technique for categorizing music genres using a Convolutional Neural Network (CNN) and the Self Adaptive Sea Lion Optimization (SA-SLnO) algorithm [ 20 ]. This approach involves first extracting the non-negative matrix factorization (NMF) features, pitch features, and STFT features from the signal.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing the configuration of deep learning models for music genre classification

2024

Heliyon

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“…However, these approaches only optimize the weights of the neural networks. 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.…”

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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Deep Convolutional Neural Network for musical genre classification via new Self Adaptive Sea Lion Optimization

Cited by 22 publications

References 36 publications

Optimal classification of music genres based on acoustic and visual features

Optimal classification of music genres based on acoustic and visual features

Optimizing the configuration of deep learning models for music genre classification

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

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