Study and analysis of feature based automatic music genre classification using Gaussian mixture model

Kaur, Chandanpreet; Kumar, Ravi

doi:10.1109/icici.2017.8365395

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…One of the first works was that developed in [37], where K-nearest neighbor (KNN) was used. Other works have been based on other computational models, such as Gaussian mixture models (GMMs) [15,16], hidden Markov models [19], linear discriminant analysis [5], support vector machines [10,23], artificial neural networks [22], and convolutional neural networks [7,20,28].…”

Section: Related Workmentioning

confidence: 99%

CONEqNet: convolutional music equalizer network

Iriz¹,

Patricio

Berlanga

et al. 2022

Multimed Tools Appl

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The process of parametric equalization of musical pieces seeks to highlight their qualities by cutting and/or stimulating certain frequencies. In this work, we present a neural model capable of equalizing a song according to the musical genre that is being played at a given moment. It is normal that (1) the equalization should adapt throughout the song and not always be the same for the whole song; and (2) songs do not always belong to a specific musical genre and may contain touches of different musical genres. The neural model designed in this work, called CONEqNet (convolutional music equalizer network), takes these aspects into account and proposes a neural model capable of adapting to the different changes that occur throughout a song and with the possibility of mixing nuances of different musical genres. For the training of this model, the well-known GTzan dataset, which provides 1,000 fragments of songs of 30 seconds each, divided into 10 genres, was used. The paper will show proofs of concept of the performance of the neural model.

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Section: Related Workmentioning

confidence: 99%

CONEqNet: convolutional music equalizer network

Iriz¹,

Patricio

Berlanga

et al. 2022

Multimed Tools Appl

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show abstract

“…The GMM model was used in studies related to music data processing and music genre classification [46,51]. Over the last few years and so far, GMM has continued to be used for music genre recognition, indexing, and retrieval of music [52][53][54][55][56][57][58][59][60]. This is because the GMM model is characterized by the parameters related averages and variance of data also allow modeling of data distribution with optional precision.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Some New Results on Automatic Identification of Vietnamese Folk Songs Cheo and Quanho

Thành¹,

Loan²,

Quang³

2020

JCC

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Vietnamese folk songs are very rich in genre and content. Identifying Vietnamese folk tunes will contribute to the storage and search for information about these tunes automatically. The paper will present an overview of the classification of music genres that have been performed in Vietnam and abroad. For two types of very popular folk songs of Vietnam such as Cheo and Quan ho, the paper describes the dataset and GMM (Gaussian Mixture Model) to perform the experiments on identifying some of these folk songs. The GMM used for experiment with 4 sets of parameters containing MFCC (Mel Frequency Cepstral Coefficients), energy, first derivative and second derivative of MFCC and energy, tempo, intensity, and fundamental frequency. The results showed that the parameters added to the MFCCs contributed significantly to the improvement of the identification accuracy with the appropriate values of Gaussian component number M. Our experiments also showed that, on average, the length of the excerpts was only 29.63% of the whole song for Cheo and 38.1% of the whole song for Quan ho, the identification rate was only 3.1% and 2.33% less than the whole song for Cheo and Quan ho respectively.

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“…Traditional feature parameters include pitch, timbre, tempo, spectrogram, Mel-spectrogram, linear prediction coefficients and short-time features. Traditional MGC models include K-nearest neighbor (KNN) [2], support vector machine (SVM) [3] and Gaussian mixture model (GMM) [4]. In 2002, Tzanetakis et al collected music data to form the dataset GTZAN, which contains 1,000 music samples from 10 music genres [5].…”

Section: Introductionmentioning

confidence: 99%

Broadcast Swin Transformer for Music Genre Classification

Duan

2024

HSET

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As a matter of fact, with the rapid development of computation ability as well as machine learning models, music genre classification (MGC) has been widely explored in recent years, which is crucial to the development of modern digital music media platforms. With this in mind, this paper proposes a novel architecture called Broadcast Swin Transformer (BST). To be specific, it adds the Broadcast Mechanism to Swin Transformer, which can effectively convey as well as utilize the low-level information of the spectrogram at multiple scales. According to the analysis, the model has been experimented on Mel-spectrograms extracted from the audio dataset GTZAN with a Top-1 accuracy of 99.0%. At the same time, its excellent performance has also been demonstrated and evaluated in ablation study and comparison with the state-of-the-art methods. In the meantime, the current limitations as well as further prospects are presented as well. Overall, these results shed light on guiding further exploration of music genre classification.

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Study and analysis of feature based automatic music genre classification using Gaussian mixture model

Cited by 9 publications

References 13 publications

CONEqNet: convolutional music equalizer network

CONEqNet: convolutional music equalizer network

Some New Results on Automatic Identification of Vietnamese Folk Songs Cheo and Quanho

Broadcast Swin Transformer for Music Genre Classification

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