Emotion extraction and recognition from music

Zhang, Fan; Meng, Hongying; Li, Maozhen

doi:10.1109/fskd.2016.7603438

Cited by 19 publications

(13 citation statements)

References 15 publications

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“…Although there are some works that are focused only on prediction like [11], [29], [14] or [7], and that are some others centered only on solving classification problems like [28], [13], [44]; there are also several works that make predictions and later extend their systems to achieve classifications, such as [37] or [2]. Generally, these works that include prediction and classification use datasets annotated in dimensional models, in which a valence and arousal (V/A) coordinate system is established [34].…”

Section: Related Workmentioning

confidence: 99%

“…It can be observed that for some works no details on data balancing are incorporated, especially in prediction systems (see Table 1) in which it has no major relevance. Regarding classification systems (see Table 2), the data of the works [28], [13] and [44] are balanced and do not require any special treatment. Instead, [27] presents unbalance data, and although there is no detail of the strategy used to balance the data, apparently some songs are removed from the original dataset.…”

Section: Related Workmentioning

confidence: 99%

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Emotional classification of music using neural networks with the MediaEval dataset

Medina

Beltrán

2020

Pers Ubiquit Comput

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The proven ability of music to transmit emotions provokes the increasing interest in the development of new algorithms for music emotion recognition (MER). In this work, we present an automatic system of emotional classification of music by implementing a neural network. This work is based on a previous implementation of a dimensional emotional prediction system in which a Multilayer Perceptron (MLP) was trained with the freely available MediaEval database. Although these previous results are good in terms of the metrics of the prediction values, they are not good enough to obtain a classification by quadrant based on the valence and arousal values predicted by the neural network, mainly due to the imbalance between classes in the dataset. To achieve better classification values a pre-processing phase was implemented to stratify and balance the dataset. Three different classifiers have been compared: Linear Support Vector Machine (SVM), Random Forest and MLP. The best results are obtained with the MLP. An averaged F-measure of 50% is obtained in a four-quadrant classification schema. Two binary classification approaches are also presented: one vs. rest (OvR) approach in four-quadrants and binary classifier in valence and arousal. The OvR approach has an average F-measure of 69%, and the second one obtained F-measure of 73% and 69% in valence and arousal respectively. Finally, a dynamic classification analysis with different time windows was performed using the temporal annotation data of the MediaEval database. The results obtained show that the classification F-measures in four quadrants are practically constant, regardless of the duration of the time window. Also, this work reflects some limitations related to the characteristics of the dataset, including: size, class balance, quality of the annotations and the sound features available.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Emotional classification of music using neural networks with the MediaEval dataset

Medina

Beltrán

2020

Pers Ubiquit Comput

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“…A categorical classification [10] was performed across 4 categories whose labels were fear, happy, relax and sad. Using the APM [11] database, 100 audio tracks under each label was obtained.…”

Section: Different Machine Learning Classifiers For Music Emotion Recmentioning

confidence: 99%

“…Therefore, to better serve this need, identifying the emotion carried by musical tracks and categorizing them accordingly is essential. This task, formally known as Music Emotion Recognition (MER) has been a subject of intense research in recent times [4] [10] [13]. Competitions like the annual Music Information Retrieval Evaluation eXchange (MIREX) challenge are based on this exact task.…”

Section: Introductionmentioning

confidence: 99%

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Different Machine Learning Classifiers for Music Emotion Recognition

Suresh*¹,

A*²

2019

IJRTE

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Music in an essential part of life and the emotion carried by it is key to its perception and usage. Music Emotion Recognition (MER) is the task of identifying the emotion in musical tracks and classifying them accordingly. The objective of this research paper is to check the effectiveness of popular machine learning classifiers like XGboost, Random Forest, Decision Trees, Support Vector Machine (SVM), K-Nearest-Neighbour (KNN) and Gaussian Naive Bayes on the task of MER. Using the MIREX-like dataset [17] to test these classifiers, the effects of oversampling algorithms like Synthetic Minority Oversampling Technique (SMOTE) [22] and Random Oversampling (ROS) were also verified. In all, the Gaussian Naive Bayes classifier gave the maximum accuracy of 40.33%. The other classifiers gave accuracies in between 20.44% and 38.67%. Thus, a limit on the classification accuracy has been reached using these classifiers and also using traditional musical or statistical metrics derived from the music as input features. In view of this, deep learning-based approaches using Convolutional Neural Networks (CNNs) [13] and spectrograms of the music clips for MER is a promising alternative.

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Music Emotion Recognition Based on Chord Identification

Zhang

Meng

et al. 2021

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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As one of the most classic human inventions, music can be seen as another language, used to express the author's thoughts and emotion. Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognising emotions from music. The recognition methods and tools for music signals are growing fast recently. However the attentions are paid mainly on music signal feature extraction and machine learning methods. The theoretical knowledge of music on emotion is usually ignored. This paper propose a new method through important music units -chord to recognize emotion from music. We firstly build a new chord database with chord segments based on selected emotions. Then FFT and statistics (STAT) features are extracted from the music signals in the database. Then one-shot learning are used for chord identification and emotion recognition using Euclidean distance and correlation. The proposed method can recognise 6 emotions from music signals through chord identification with 76.6% accuracy of chord identification. This idea can be developed further using more advanced feature extraction and one-shot learning methods.

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Emotion extraction and recognition from music

Cited by 19 publications

References 15 publications

Emotional classification of music using neural networks with the MediaEval dataset

Emotional classification of music using neural networks with the MediaEval dataset

Different Machine Learning Classifiers for Music Emotion Recognition

Music Emotion Recognition Based on Chord Identification

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