Music Genre Classification: A Review of Deep-Learning and Traditional Machine-Learning Approaches

Ndou, Ndiatenda; Ajoodha, Ritesh; Jadhav, Ashwini

doi:10.1109/iemtronics52119.2021.9422487

Cited by 41 publications

(18 citation statements)

References 8 publications

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“…In [21], a tutorial on MIR is provided that is especially useful to newcomers in the field. [13], [25] reviews MRS. Music Genre Classification (MGC) is reviewed in [26]. Drum transcription is reviewed in [27], focusing on non-negative matrix factorization and recurrent neural network architectures.…”

Section: B Related Surveysmentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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The discipline of Deep Learning has been recognized for its strong computational tools, which have been extensively used in data and signal processing, with innumerable promising results. Among the many commercial applications of Deep Learning, Music Signal Processing has received an increasing amount of attention over the last decade. This work reviews the most recent developments in Deep Learning in Music signal processing. Two main applications that are discussed are Music Information Retrieval, which spans a plethora of applications, and Music Generation, which can fit a range of musical styles. After a review of both topics, several emerging directions are identified for future research.

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Section: B Related Surveysmentioning

confidence: 99%

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

et al. 2023

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“…Subsequently, when deep learning techniques were more widely used and models based on these techniques proved very effective in solving many problems, the number of approaches proposed for identifying musical genres using deep learning models grew, leading to greater popularity. The domain of machine learning is still expanding [ 8 ]. The analysis of these studies demonstrates that although deep learning approaches have really improved classification accuracy in comparison to prior methods, they still fall short of an optimal genre categorization system.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the configuration of deep learning models for music genre classification

2024

Heliyon

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“…[1][2][3] One of the most prevalent ways to differentiate music works is by genre. 4 Certain researchers deployed handcraft features for classifying music owing to higher flexibility. Nevertheless, the feature extraction procedure was challenging, inefficient, and complex, also, the handcrafted feature's quality affect the classification quality.…”

Section: Introductionmentioning

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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Music Genre Classification: A Review of Deep-Learning and Traditional Machine-Learning Approaches

Cited by 41 publications

References 8 publications

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Music Deep Learning: Deep Learning Methods for Music Signal Processing—A Review of the State-of-the-Art

Optimizing the configuration of deep learning models for music genre classification

Optimal classification of music genres based on acoustic and visual features

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