Non Negative Matrix Factorization for Music Emotion Classification

Rosli, Nurlaila Binti; Rajaee, Nordiana; Bong, David B. L.

doi:10.1007/978-3-319-32213-1_16

Cited by 2 publications

(2 citation statements)

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“…The three major basic level categories in the negative category were anger, fear, and sadness. Emotion recognition tasks can be performed in various modalities, such as textual [13], audio [14], speech [15], and brain activity [16]. Furthermore, it can also be performed in a multimodal dataset [4].…”

Section: A Emotion Recognitionmentioning

confidence: 99%

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Sutoyo,

Warnars,

Isa

et al. 2023

IJACSA

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Twitter is a social media platform that has a large amount of unstructured natural language text. The content of Twitter can be utilized to capture human behavior via emphasized emotions located in tweets. In their tweets, people commonly express emotions to show their feelings. Hence, it is crucial to recognize the text's underlined emotions to understand the message's meaning. Feature engineering is the process of improving raw data into often overlooked features. This research explores feature engineering techniques to find the best features for building an emotion recognition model on the Indonesian Twitter dataset. Two different text data representations were used, namely, TF-IDF and word embedding. This research proposed 12 feature engineering configurations in TF-IDF by combining data stemming, data augmentation, and machine learning classifiers. Moreover, this research proposed 27 feature engineering configurations in word embedding by combining three-word embedding models, three pooling techniques, and three machine-learning classifiers. In total, there are 39 feature engineering combinations. The configuration with the best F1 score is TF-IDF with logistic regression, stemmed dataset, and augmented dataset. The model achieved 65.27% accuracy and 66.09% F1 score. The detailed characteristics from the top seven models in TF-IDF also follow the same feature engineering configuration. Lastly, this work improves performance from the previous research by 1.44% and 2.01% on the word2vec and fastText approaches, respectively.

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Section: A Emotion Recognitionmentioning

confidence: 99%

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Sutoyo,

Warnars,

Isa

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…However, based on the profound influence of music on people's lives, the study of music emotion cognition is of great research value and importance. It is worthy of further in-depth exploration and research [15][16].…”

Section: Introductionmentioning

confidence: 99%

Emotional Characterization Mining in Music Performance and Its Guiding Role

Yan,

2024

Applied Mathematics and Nonlinear Sciences

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Emotional attributes are crucial in music performance, serving a pivotal role in guiding interpretation and execution. This study employs a Wavenet layer within a Waveform-Deep Neural Network (WLDNN) to extract emotional features from musical performances. An activation function is then applied to process and refine these features. Additionally, a Generative Adversarial Network (GAN) is utilized to identify and remove irrelevant features, thereby enhancing the quality of the emotional attributes extracted. The evaluation of these emotional features employs both music emotion language values and an emotion vector model. Our analysis demonstrates that this methodology achieves a 90% accuracy rate in identifying and extracting emotional features from music performances. Based on these findings, a stage lighting control system was developed, tailored to respond to the emotional cues within the music. The system was tested across ten different performances with a sampling frequency of 5 seconds, achieving an average synchronization rate of 94.01% with the emotional content of the music. This approach not only proves effective for stage lighting design but also offers valuable insights for enhancing the emotional expressiveness of musical performances.

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Non Negative Matrix Factorization for Music Emotion Classification

Cited by 2 publications

References 10 publications

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Indonesian Twitter Emotion Recognition Model using Feature Engineering

Emotional Characterization Mining in Music Performance and Its Guiding Role

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