Content-Based Music Classification by Advanced Features and Progressive Learning

Su, Ja-Hwung; Chin, Chu-Yu; Hong, Tzung-Pei; Su, Jung-Jui

doi:10.1007/978-3-030-14802-7_10

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Su et al [30] proposed a substance grounded harmony classifier titled Progressive-Learning-based Music Classifier (PLMC) was recommended to go for disputes of highlight substance and learning technique. Regarding highlight content, the audio highlights were redesigned as the propelled highlights to upgrade the nature of highlights.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Singh

Jaiswal

2020

SN Appl. Sci.

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Audio classification is the reason for the multimedia gratified examination which is the utmost significant and generally utilized application these days. For huge information bases, programmed classification procedure utilizing Artificial Intelligence (AI) is more viable than the manual classification. Various sorts of AI calculations have proposed in writing like K-Nearest Neighbors Principal Component Analysis, Gaussian Mixture Model, and Hidden Markov Model, etc. By utilizing the above methods, the audio classification can be done with no class related pre-information. However, they require huge training data with no real segregation results. To beat these insufficiencies, this paper proposed a general structure for audio classification. In this paper, another audio classification algorithm utilizing Support Vector Machine (SVM) in view of Whale Optimization Algorithm (WOA) is given where WOA-SVM utilizes the class mark of the info test as the real yield. WOA is utilized for conquering the inconvenience of SVM, for example, high computational multifaceted nature as a result of the explaining of enormous scale quadratic programming in parameter iterative learning methodology. The audio sign has shown up in huge volumes on account of its tendency. With the goal that we have utilized the MapReduce approach which is one of the sorts of big data investigation to play out the classification on the unstructured information. The proposed audio classification algorithm has contrasted with a few existing classification algorithms with demonstrating its productivity and the exactness.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Singh

Jaiswal

2020

SN Appl. Sci.

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“…Hidden Layer Summation Layer Output Layer Computational Intelligence and Neuroscience added or removed without requiring extensive retraining [33]. A PNN is consequently superior to numerous models of neural networks in terms of its learning speed, and it is efective in a wide range of applications [34].…”

Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Samha

Alshammri

Nuagah

et al. 2023

Computational Intelligence and Neuroscience

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Audio classification and retrieval has been recognized as a fascinating field of endeavor for as long as it has existed due to the topic of identifying and choosing the most useful audio attributes. The categorization of audio files is significant not only in the area of multimedia applications but also in the disciplines of medicine, sound analysis, intelligent homes and cities, urban informatics, entertainment, and surveillance. This study introduces a new algorithm called the modified bacterial foraging optimization algorithm (MBFOA), which is based on a method that retrieves and classifies audio data. The goal of this algorithm is to reduce the computational complexity of existing techniques. Along with the combination of the peak estimated signal, the enhanced mel-frequency cepstral coefficient (EMFCC) and the enhanced power normalized cepstral coefficients (EPNCC) are used. These are then optimized using the fitness function utilizing MBFOA. The probabilistic neural network is used to differentiate between a music signal and a spoken signal from an audio source (PNN). It is next necessary to extract and list the characteristics that correspond to the class that was arrived at as a consequence of the categorization. When compared to other approaches that are somewhat similar, MBFOA demonstrates superior levels of sensitivity, specificity, and accuracy.

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“…H (1) H (2) H (3) H (I) H (I) H (1) H (2) H (3) … … collects the tags in the MagnaTagATune data set through an online game called TagATune. In the game, every two players will be asked to listen to the music clips provided by the game, and then the players need to give the category tags of the music clips heard.…”

Section: Data Setmentioning

confidence: 99%

“…At present, music classification mainly includes text classification and classification based on music content. Text classification is mainly based on music metadata information, such as singer, lyrics, songwriter, age, music name, and other labeled text information [3]. is classification method is easy to implement, simple to operate, and fast to retrieve, but the shortcomings are also evident.…”

Section: Introductionmentioning

confidence: 99%

Music Feature Classification Based on Recurrent Neural Networks with Channel Attention Mechanism

Gan

2021

Mobile Information Systems

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With the advancement of multimedia and digital technologies, music resources are rapidly increasing over the Internet, which changed listeners’ habits from hard drives to online music platforms. It has allowed the researchers to use classification technologies for efficient storage, organization, retrieval, and recommendation of music resources. The traditional music classification methods use many artificially designed acoustic features, which require knowledge in the music field. The features of different classification tasks are often not universal. This paper provides a solution to this problem by proposing a novel recurrent neural network method with a channel attention mechanism for music feature classification. The music classification method based on a convolutional neural network ignores the timing characteristics of the audio itself. Therefore, this paper combines convolution structure with the bidirectional recurrent neural network and uses the attention mechanism to assign different attention weights to the output of the recurrent neural network at different times; the weights are assigned for getting a better representation of the overall characteristics of the music. The classification accuracy of the model on the GTZAN data set has increased to 93.1%. The AUC on the multilabel labeling data set MagnaTagATune has reached 92.3%, surpassing other comparison methods. The labeling of different music labels has been analyzed. This method has good labeling ability for most of the labels of music genres. Also, it has good performance on some labels of musical instruments, singing, and emotion categories.

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Content-Based Music Classification by Advanced Features and Progressive Learning

Cited by 5 publications

References 16 publications

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Content‐Based Audio Classification and Retrieving Using Modified Bacterial Foraging Optimization Algorithm

Music Feature Classification Based on Recurrent Neural Networks with Channel Attention Mechanism

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