Neural Network Music Genre Classification

Pelchat, Nikki; Gelowitz, Craig M.

doi:10.1109/cjece.2020.2970144

Cited by 57 publications

(14 citation statements)

References 20 publications

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“…Besides, the rule of music theory was introduced to confine the genre of generated music [ 8 ]. Pelchat and Gelowitz input the images of spectrograms generated from the time slices of songs into a neural network to classify the songs into their respective musical styles [ 9 ]. Yan trained the network weights by the T-S-based cognitive neural network and improved the genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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“…The neural network had six convolutional layers followed by a fully connected layer and then a Softmax function at the end in order to calculate the probability of each genre detected and a one-hot array of genre classifications. The results showed an accuracy of 85% on the given test data (Pelchat, Gelowitz, 2020).…”

Section: Literature Reviewmentioning

confidence: 82%

Development of a mobile application for flag identification based on artificial neural networks

Zatezalo

Đunđer

2022

Zb. Veleuč. Rij. (Online)

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Strojno učenje te posebno pripadajuće potpodručje poznato kao duboko učenje pokreću novedisruptivne tehnologije i nedavna postignuća u različitim industrijama. Ovaj rad koristi do određenerazine istu tehnologiju pri izradi mobilne aplikacije za edukacijske svrhe, a koja omogućuje identifikacijunepoznatih zastava pomoću umjetne inteligencije. Kako bi aplikacija bila funkcionalna i efikasna,potrebno je bilo kreirati vlastiti podatkovni skup i obraditi ga na razne načine kako bi se osiguralazbirka podataka koja odražava podatke iz stvarnoga svijeta. Ova zbirka bi se potom koristila kaopodatkovni ulaz za razvijanje modela, koji se razvija unutar okruženja TensorFlow. Nakon što je modelrazvijen, implementiran je kao dio mobilne aplikacije programirane Flutterom. Funkcionalnost mobilneaplikacije i točnost modela ispitana je na brojci od preko 100 novih slika koje model prethodno nijevidio niti je nad njima bio treniran. Rezultat ovog ispitivanja mogao bi se smatrati odrazom okvirnespremnosti i primjenjivosti ove aplikacije u stvarnom svijetu.

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“…CNN is also found to be an effective algorithm in the identification of music genres also. To classify the songs into their respective genre CNN is also on spectrograms extracted from the songs (3) . MFCC-based CNN and RNN (Recurrent Neural Network) are used to determine the music style where 93.3% classification accuracy is achieved (4) .…”

Section: Related Workmentioning

confidence: 99%

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Bora¹,

Barman²,

Patowary³

et al. 2023

IJST

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Objectives: A melody is made up of several musical notes or pitches that are joined together to form one whole. This experiment aims to develop four models based on the Mel-frequency Cepstral Coefficients (MFCC) to classify the melodies played on harmonium corresponding to five different class of Assamese folk Music. Methods: The melodies of five different categories of Assamese folk songs are selected for classification. With the help of expert musicians, these melodies are played in harmonium and audio samples are recorded in the same acoustic environment. 20 MFCC's are extracted from each of the samples and classification of the melodies is done using four supervised learning techniques-Decision Tree Classifier, Linear Discriminant Analysis (LDA), Random Forest Classifier, and Support Vector Machine (SVM). Findings: The performance of the fitted models are evaluated using different evaluation techniques and presented. A maximum of 94.17% average accuracy score is achieved under Support Vector Machine. The average accuracy scores of Decision Tree Classifier, Linear Discriminant Analysis (LDA), and Random Forest Classifier are 73.58%, 85.58%, and 86.11% respectively. The models are developed based on 250 samples (50 from each type). However, increasing the training sample size, there is a possibility to improve the performances of the other three models also. Novelty: The developed approach for identifying the melodies is based on computational techniques. This work will certainly provide a basis for conducting further computational studies in folk music for any community.

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Neural Network Music Genre Classification

Cited by 57 publications

References 20 publications

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Development of a mobile application for flag identification based on artificial neural networks

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

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