Feature Selection for Musical Genre Classification Using a Genetic Algorithm

Girsang, Abba Suganda; Manalu, Andi Setiadi; Huang, Ko-Wei

doi:10.25046/aj040221

Cited by 11 publications

(6 citation statements)

References 36 publications

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“…The intermediate frequency f ( m ) of each triangular filter is distributed at equal distances and intervals on the Mel frequency axis and widens with the increase of m on the frequency axis. The frequency response of triangular filter is defined as [ 17 ]

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Section: Research On Music Style Classification Based On Deep Learningmentioning

confidence: 99%

Research on Music Style Classification Based on Deep Learning

Wang

Sohail

2022

Computational and Mathematical Methods in Medicine

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Music style is one of the important labels for music classification, and the current music style classification methods extract features such as rhythm and timbre of music and use classifiers to achieve classification. The classification accuracy is not only affected by the classifier but also limited by the effect of music feature extraction, which leads to poor classification accuracy and stability. In response to the abovementioned defects, a deep-learning-based music style classification method will be studied. The music signal is framed using filters and Hamming windows, and the MFCC coefficient features of music are extracted by discrete Fourier transform. A convolutional recurrent neural network structure combining CNN and RNN is designed and trained to determine the parameters to achieve music style classification. Analysis of the simulation experimental data shows that the classification accuracy of the studied classification method is at least 93.3%, and the classification time overhead is significantly reduced, the classification results are stable, and the results are reliable.

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Section: Research On Music Style Classification Based On Deep Learningmentioning

confidence: 99%

Research on Music Style Classification Based on Deep Learning

Wang

Sohail

2022

Computational and Mathematical Methods in Medicine

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“…Although Apache Spark (developed by UC Berkeley, USA) may provide better running time, our basic objective was to design an optimized model using machine learning algorithms. The confusion matrix in terms of accuracy, precision, recall, F-measure, G-mean, and (Area Under Curve) AUC is calculated for the baselines classifiers such as SVM-GA [32], MSVM [33], MULTICLASS (Multiple Class) [34], and ACOSVM (Ant Colony Optimization using Support Vector Machine) [35]. All these baselines have proved their performance against the number of algorithms in the literature.…”

Section: Resultsmentioning

confidence: 99%

Smart Agriculture Cloud Using AI Based Techniques

et al. 2021

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This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

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“…A recent study [18] examined the selection of frequency-domain features and low-level features using a genetic algorithm. Comparative analysis is performed with different classification algorithms, such as Naïve Bayes (NB) and K-nearest neighbor (KNN), and SVM.…”

Section: Related Workmentioning

confidence: 99%

Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

Almazaydeh¹,

Atiewi²,

Tawil³

et al. 2022

Computers, Materials &Amp; Continua

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Genres are one of the key features that categorize music based on specific series of patterns. However, the Arabic music content on the web is poorly defined into its genres, making the automatic classification of Arabic audio genres challenging. For this reason, in this research, our objective is first to construct a well-annotated dataset of five of the most well-known Arabic music genres, which are: Eastern Takht, Rai, Muwashshah, the poem, and Mawwal, and finally present a comprehensive empirical comparison of deep Convolutional Neural Networks (CNNs) architectures on Arabic music genres classification. In this work, to utilize CNNs to develop a practical classification system, the audio data is transformed into a visual representation (spectrogram) using Short Time Fast Fourier Transformation (STFT), then several audio features are extracted using Mel Frequency Cepstral Coefficients (MFCC). Performance evaluation of classifiers is measured with the accuracy score, time to build, and Matthew's correlation coefficient (MCC). The concluded results demonstrated that AlexNet is considered among the topperforming five CNNs classifiers studied: LeNet5, AlexNet, VGG, ResNet-50, and LSTM-CNN, with an overall accuracy of 96%.

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Feature Selection for Musical Genre Classification Using a Genetic Algorithm

Cited by 11 publications

References 36 publications

Research on Music Style Classification Based on Deep Learning

Research on Music Style Classification Based on Deep Learning

Smart Agriculture Cloud Using AI Based Techniques

Arabic Music Genre Classification Using Deep Convolutional Neural Networks (CNNs)

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