High-Level Libraries for Emotion Recognition in Music: A Review

Medina, Yesid Ospitia; Baldassarri, Sandra

doi:10.1007/978-3-030-05270-6_12

Cited by 5 publications

(2 citation statements)

References 12 publications

(18 reference statements)

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“…Proses ekstraksi fitur dari sinyal audio merupakan bagian yang sangat penting untuk akurasi deteksi stress. Beragam fitur dapat diekstraksi menggunakan jAudio yang merupakan framework open source untuk ekstraksi fitur audio berbasis platform Java [12]. Terdapat total 136 fitur audio yang dapat diekstrak melalui jAudio, diantara fitur tersebut antara lain yang cukup diskriminatif dalam deteksi stress adalah Strongest frequency via fast fourier transform dan mel-frequency cepstral coeffiecient [13].…”

Section: Ekstraksi Fiturunclassified

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

Chyan

Kasmara²

2021

RESTI

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The need for monitoring of children today is very important, especially for children under five, whose physical and verbal abilities are still inadequate to be able to communicate effectively with their parents or caregivers about the conditions they are experiencing. Previous studies related to child safety that were studied generally focused on responses to events that could potentially harm children.This study aims to design a prototype child monitoring system consisting of a wearable device that is used on a child's wrist equipped with sensors and connected to a server via a wireless network. Monitoring software that runs on the server will collect all data parameters from wearable devices with built-in audio signal, temperature, and heart rate sensor then with machine learning algorithm implemented in software will allow the system to predict if stress conditions happen on children and then system can give warnings to child-caregiver through monitoring applications or SMS messages. Using the Decision Tree and Naive Bayes classification methods the system can effectively predict stress conditions in children with an accuracy of 82.8 percent using audio, temperature, and heart rate parameters. This shows that the system has the capability to contribute to increasing child safety in the supervision environment.

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Section: Ekstraksi Fiturunclassified

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

Chyan

Kasmara²

2021

RESTI

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“…MIR provides information about recognition of musical instruments, classification of musical phrases or melodies, and rhythm and high level-based music retrieval [ 17 ]. MIR analysis is also focused on recognition of emotions in music [ 18 ] and research continues concerning ECG and emotion in relation to music. Applications have been developed in which heart rate emotion data was used for generating music [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Idrobo-Ávila

Correa

Vargas-Cañas

et al. 2021

Heliyon

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The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research to improve the readability and classification of cardiac signals includes studies geared toward sonification of the electrocardiographic signal and others involving features related to music processing, such as Mel-frequency cepstral coefficients. In terms of music processing features, this study seeks to use music information retrieval (MIR) features as electrocardiographic signal descriptors. The study compares the discriminatory capability of the introduced features in relation to standard groups such as heart rate variability, wavelet transform, descriptive statistics, Mel coefficients and fractal analysis, evaluated using classification algorithms; the signals analyzed were extracted from public databases. The group of features extracted from wavelet transform and the MIR group showed a high level of discrimination; the best representation of the ECG signals in the study was achieved in most cases by the MIR features. Moreover, a correlation coefficient higher than 0.8 was found between a number of MIR and other feature groups, indicating a likely relationship between the electrocardiographic signals and MIR features. These results suggest the feasibility of representing the analyzed signals by music information retrieval descriptors, giving the potential to consider these electrocardiographic signals as analogues to musical signals.

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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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High-Level Libraries for Emotion Recognition in Music: A Review

Cited by 5 publications

References 12 publications

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

Sistem Monitoring dan Deteksi Stres Pada Anak Berbasis Wearable Device

Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Emotional classification of music using neural networks with the MediaEval dataset

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