Automatic Music Genre Classification using Convolution Neural Network

Vishnupriya, S.; Meenakshi, K.

doi:10.1109/iccci.2018.8441340

Cited by 69 publications

(32 citation statements)

References 4 publications

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“…Currently, deep learning techniques are emerging as an alternative to handcrafted feature engineering due to automatic feature extraction, as introduced in [18]. These techniques perform automatic feature extraction for the music classification tasks [19][20] [21].…”

Section: Related Workmentioning

confidence: 99%

A Globally Regularized Joint Neural Architecture for Music Classification

et al. 2020

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Section: Related Workmentioning

confidence: 99%

A Globally Regularized Joint Neural Architecture for Music Classification

et al. 2020

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“…Vishnpryaet.al., classifies the music into various genres by extracting the feature vector and Mel Frequency Cepstral Coefficient (MFCC) is used as a feature vector for sound sample. [12]…”

Section: Related Workmentioning

confidence: 99%

Music Genre Classification using Lyric Mining Based ontf-Idf

K*,

SafaM.,

et al. 2020

IJRTE

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With the advancement in the internet technologies, music domain has flourished with better access to various music libraries. In the present times, we are able to access music files over the internet with ease. Nowadays, the lyrics sets are categorised into different genres which cater to various listener moods. Users prefer to listen to music that best suits their mood. Thus considering the need for such classification, research works are being carried out to develop methodologies that can distinguish the music based on individual mood. In this research, instead of using the traditional method of audio feature analysis, we propose to develop a system which analyses the lyrics dataset of the songs based on the features extracted from the training phase and we can predict the mood of the song that is presented to the system at the validation stage. The proposed system is considering five moods containing one hundred songs each, for the validation purpose. The system is capable of predicting the mood of the song based on the analysis of the lyric text.

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“…To classify a large digital music resource library, if manual labeling is used, it will consume a lot of manpower and time, and the labeling results are more subjective, and the labeling standards cannot be completely unified, which is limited by different professionals who label music. erefore, the automatic classification of music [5][6][7] has gradually become a research hotspot for researchers. e automatic classification of music genres can effectively solve the problem of high cost and time-consuming human labeling.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

2021

Scientific Programming

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The teaching of ideological and political theory courses and daily ideological and political education are two important parts of education for college students. With the iterative update of information technology, the individualized development of students, and the reform and innovation of ideological and political education, higher goals and requirements have been put forward for ideological and political education. Some universities have developed new paths in the teaching model, but they have not considered the evaluation module and paid little attention to their own development. They only paid attention to the fact that it injected fresh blood into the reform of education model and ideological education but ignored the improvement of their own quality. Therefore, with these limitations, the learning effect is not satisfactory. Keeping in view these issues, this article defines the concept of deep learning and ideological and political education of college students as the starting point and then analyzes the new precise and personalized concepts, new forms of intelligent teaching and evaluation, and new models of intelligent learning that deep learning brings to college students’ ideological and political education. This is a new path of intelligent linkage with the subject, object, and mediator. It can deepen the reform of the education and teaching mode of individualization, accuracy, interactivity, and vividness of college students’ ideological and political education and improve the evaluation and management of college students’ ideological and political education. The experimental results of the study showed the effectiveness of the proposed study.

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Automatic Music Genre Classification using Convolution Neural Network

Cited by 69 publications

References 4 publications

A Globally Regularized Joint Neural Architecture for Music Classification

A Globally Regularized Joint Neural Architecture for Music Classification

Music Genre Classification using Lyric Mining Based ontf-Idf

Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

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