Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network

Lima, Raul de Araújo; Sousa, Rômulo César Costa de; Lopes, Hélio; Barbosa, Simone Diniz Junqueira

doi:10.1007/978-3-030-61401-0_49

Cited by 12 publications

(4 citation statements)

References 4 publications

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“…(3) where p j is the relative frequency of class j in n. The lowest value is zero, which signifies a node that contains the elements of the same class [16].…”

Section: Gini Impurity In Decision Treementioning

confidence: 99%

Classification of Kazakh Music Genres Using Machine Learning Techniques

Mimenbayeva,

Bekmagambetova,

Muratova

et al. 2024

sjaitu

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This article analysis a Kazakh Music dataset, which consists of 800 audio tracks equally distributed across 5 different genres. The purpose of this research is to classify music genres by using machine learning algorithms Decision Tree Classifier and Logistic regression. Before the classification, the given data was pre-processed, missing or irrelevant data was removed. The given dataset was analyzed using a correlation matrix and data visualization to identify patterns. To reduce the dimension of the original dataset, the PCA method was used while maintaining variance. Several key studies aimed at analyzing and developing machine learning models applied to the classification of musical genres are reviewed. Cumulative explained variance was also plotted, which showed the maximum proportion (90%) of discrete values generated from multiple individual samples taken along the Gaussian curve. A comparison of the decision tree model to a logistic regression showed that for f1 Score Logistic regression produced the best result for classical music - 82%, Decision tree classification - 75%. For other genres, the harmonic mean between precision and recall for the logistic regression model is equal to zero, which means that this model completely fails to classify the genres Zazz, Kazakh Rock, Kazakh hip hop, Kazakh pop music. Using the Decision tree classifier algorithm, the Zazz and Kazakh pop music genres were not recognized, but Kazakh Rock with an accuracy and completeness of 33%. Overall, the proposed model achieves an accuracy of 60% for the Decision Tree Classifier and 70% for the Logistic regression model on the training and validation sets. For uniform classification, the data were balanced and assessed using the cross-validation method. The approach used in this study may be useful in classifying different music genres based on audio data without relying on human listening.

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“…(3) where p j is the relative frequency of class j in n. The lowest value is zero, which signifies a node that contains the elements of the same class [16].…”

Section: Gini Impurity In Decision Treementioning

confidence: 99%

Classification of Kazakh Music Genres Using Machine Learning Techniques

Mimenbayeva,

Bekmagambetova,

Muratova

et al. 2024

sjaitu

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“…O melhor resultado foi obtido pelo modelo LSTM (50% de acurácia), ao considerar uma ca-mada de Embedding de 100 dimensões e treinamento explorando as técnicas de Dropout e Gradient Clipping. Em [de Araújo Lima et al 2020], foi apresentado um conjunto de dados com cerca de 138 mil canc ¸ões brasileiras e 14 gêneros. Os experimentos com ele realizados exploraram os modelos Support Vector Machine (SVM), RF e LSTM Bidirecional (BiLSTM), cada um associado a diferentes técnicas para a gerac ¸ão dos embeddings.…”

Section: Trabalhos Relacionadosunclassified

Classificação de gêneros a partir de letras de músicas em português

de Oliveira,

e Souza Filho

2023

Anais Do XIV Simpósio Brasileiro De Tecnologia Da Informação E Da Linguagem Humana (STIL 2023)

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Associar canções a gêneros não é fácil. A subjetividade e diversidade das obras musicais tornam a atribuição de rótulos inequívocos uma tarefa desafiadora. Porém, atributos textuais podem contribuir para a caracterização de gêneros. Este trabalho propõe um sistema para classificar letras de músicas em português, explorando modelos de Aprendizado Profundo, tais como Redes LSTM e Transformers. São também incluídas estratégias mais simples como a classificação por Regressão Logística de representações geradas por TF-IDF. Experimentos demonstraram que o modelo Transformer apresentou o melhor desempenho, alcançando uma acurácia de 61,6% para dez gêneros musicais.

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“…After decades of intense transformations in the music market, the Digital era brought novel challenges, including a substantial volume of data. As human inspection is almost impossible for music big data scale, specialized algorithms can help with several tasks in MIR, including music recommendation (BORGES; QUEIROZ, 2017), automatic genre classification (CORRÊA; RODRIGUEZ, 2016;SHINOHARA;FOLEISS;TAVARES, 2019;ARAÚJO LIMA et al, 2020), algorithmic composition (HOLOPAINEN, 2021) and so on. Another possible benefit is to feed machine-learning models for musical success early prediction, contributing to identify trends and new talent.…”

Section: Related Workmentioning

confidence: 99%

From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market

Seufitelli

Oliveira²,

Silva³

et al. 2022

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The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era.

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Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network

Cited by 12 publications

References 4 publications

Classification of Kazakh Music Genres Using Machine Learning Techniques

Classification of Kazakh Music Genres Using Machine Learning Techniques

Classificação de gêneros a partir de letras de músicas em português

From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market

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