Remarks on computational emotion classification from physiological signal - Evaluation of how jazz music chord progression influences emotion

2012 ◽  
Author(s):  
Megumi Maekawa ◽  
Kazuhiko Takahashi ◽  
Masafumi Hashimoto
Keyword(s):  
Emotion Classification ◽  
Physiological Signal ◽  
Jazz Music ◽  
Signal Evaluation

scholarly journals EEG-based human emotion recognition using entropy as a feature extraction measure

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Pragati Patel ◽  
Raghunandan R ◽  
Ramesh Naidu Annavarapu
Keyword(s):  
Emotion Recognition ◽  
Emotional State ◽  
Computer Interface ◽  
Eeg Signal ◽  
Emotion Classification ◽  
Emotion Identification ◽  
Physiological Signal ◽  
Wide Range ◽  
Electrical Activities ◽  
The Brain

AbstractMany studies on brain–computer interface (BCI) have sought to understand the emotional state of the user to provide a reliable link between humans and machines. Advanced neuroimaging methods like electroencephalography (EEG) have enabled us to replicate and understand a wide range of human emotions more precisely. This physiological signal, i.e., EEG-based method is in stark comparison to traditional non-physiological signal-based methods and has been shown to perform better. EEG closely measures the electrical activities of the brain (a nonlinear system) and hence entropy proves to be an efficient feature in extracting meaningful information from raw brain waves. This review aims to give a brief summary of various entropy-based methods used for emotion classification hence providing insights into EEG-based emotion recognition. This study also reviews the current and future trends and discusses how emotion identification using entropy as a measure to extract features, can accomplish enhanced identification when using EEG signal.

scholarly journals The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 866 ◽  
Author(s):  
SeungJun Oh ◽  
Jun-Young Lee ◽  
Dong Keun Kim
Keyword(s):  
Emotion Recognition ◽  
Identification Accuracy ◽  
Physiological Signals ◽  
Dominant Factor ◽  
Basic Emotion ◽  
Emotion Classification ◽  
Physiological Signal ◽  
Proposed Model ◽  
Respiration Sensor ◽  
Signal Combination

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

Zeitstrukturen im Jazz

2014 ◽  
Vol 59 (1) ◽  
pp. 79-92
Author(s):  
Alexander Becker
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Time Frame ◽  
Scale Structure ◽  
Classical Form ◽  
Jazz Music ◽  
The Moment

Wie erlebt der Hörer Jazz? Bei dieser Frage geht es unter anderem um die Art und Weise, wie Jazz die Zeit des Hörens gestaltet. Ein an klassischer Musik geschultes Ohr erwartet von musikalischer Zeitgestaltung, den zeitlichen Rahmen, der durch Anfang und Ende gesetzt ist, von innen heraus zu strukturieren und neu zu konstituieren. Doch das ist keine Erwartung, die dem Jazz gerecht wird. Im Jazz wird der Moment nicht im Hinblick auf ein Ziel gestaltet, das von einer übergeordneten Struktur bereitgestellt wird, sondern so, dass er den Bewegungsimpuls zum nächsten Moment weiterträgt. Wie wirkt sich dieses Prinzip der Zeitgestaltung auf die musikalische Form im Großen aus? Der Aufsatz untersucht diese Frage anhand von Beispielen, an denen sich der Weg der Transformation von einer klassischen zu einer dem Jazz angemessenen Form gut nachverfolgen lässt.<br><br>How do listeners experience Jazz? This is a question also about how Jazz music organizes the listening time. A classically educated listener expects a piece of music to structure, unify and thereby re-constitute the externally given time frame. Such an expectation is foreign to Jazz music which doesn’t relate the moment to a goal provided by a large scale structure. Rather, one moment is carried on to the next, preserving the stimulus potentially ad infinitum. How does such an organization of time affect the large scale form? The paper tries to answer this question by analyzing two examples which permit to trace the transformation of a classical form into a form germane to Jazz music.

Survey Paper on Gender and Emotion Classification using Facial Expression Detection

2020 ◽  
Author(s):  
Aishwarya Gupta ◽  
Devashish Sharma ◽  
Shaurya Sharma ◽  
Anushree Agarwal
Keyword(s):  
Facial Expression ◽  
Emotion Classification ◽  
Survey Paper ◽  
Gender And Emotion
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