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

2018 ◽  
pp. 158-168 ◽  
Author(s):  
Yesid Ospitia Medina ◽  
Sandra Baldassarri ◽  
José Ramón Beltrán
Keyword(s):  
Emotion Recognition ◽  
High Level

scholarly journals Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition

2017 ◽  
Author(s):  
Xinge Zhu ◽  
Liang Li ◽  
Weigang Zhang ◽  
Tianrong Rao ◽  
Min Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Feature Fusion ◽  
Feature Representation ◽  
Low Level ◽  
Learning Framework ◽  
Independent Entity ◽  
Internet Images ◽  
High Level ◽  
Different Levels

Visual emotion recognition aims to associate images with appropriate emotions. There are different visual stimuli that can affect human emotion from low-level to high-level, such as color, texture, part, object, etc. However, most existing methods treat different levels of features as independent entity without having effective method for feature fusion. In this paper, we propose a unified CNN-RNN model to predict the emotion based on the fused features from different levels by exploiting the dependency among them. Our proposed architecture leverages convolutional neural network (CNN) with multiple layers to extract different levels of features with in a multi-task learning framework, in which two related loss functions are introduced to learn the feature representation. Considering the dependencies within the low-level and high-level features, a new bidirectional recurrent neural network (RNN) is proposed to integrate the learned features from different layers in the CNN model. Extensive experiments on both Internet images and art photo datasets demonstrate that our method outperforms the state-of-the-art methods with at least 7% performance improvement.

scholarly journals Scanning Patterns of Faces do not Explain Impaired Emotion Recognition in Huntington Disease: Evidence for a High Level Mechanism

2012 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Asselen ◽  
Filipa Júlio ◽  
Cristina Januário ◽  
Elzbieta Bobrowicz Campos ◽  
Inês Almeida ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Huntington Disease ◽  
High Level

scholarly journals A Hybrid Technique using CNN+LSTM for Speech Emotion Recognition

2020 ◽  
Vol 9 (5) ◽  
pp. 1126-1130
Keyword(s):  
Feature Extraction ◽  
Human Computer Interaction ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Hybrid Technique ◽  
Proposed Model ◽  
High Level ◽  
High Level Feature ◽  
Convolutional Lstm

Automatic speech emotion recognition is a very necessary activity for effective human-computer interaction. This paper is motivated by using spectrograms as inputs to the hybrid deep convolutional LSTM for speech emotion recognition. In this study, we trained our proposed model using four convolutional layers for high-level feature extraction from input spectrograms, LSTM layer for accumulating long-term dependencies and finally two dense layers. Experimental results on the SAVEE database shows promising performance. Our proposed model is highly capable as it obtained an accuracy of 94.26%.

A probability and integrated learning based classification algorithm for high-level human emotion recognition problems

Measurement ◽  
2020 ◽  
Vol 150 ◽  
pp. 107049 ◽  
Author(s):  
Dazhi Jiang ◽  
Kaichao Wu ◽  
Dicheng Chen ◽  
Geng Tu ◽  
Teng Zhou ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Classification Algorithm ◽  
Integrated Learning ◽  
Human Emotion ◽  
High Level

scholarly journals Popular music and the role of vocal melody in perceived emotion

2017 ◽  
Vol 46 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Scott Beveridge ◽  
Don Knox
Keyword(s):  
Popular Music ◽  
Emotion Recognition ◽  
Emotional Valence ◽  
Circumplex Model ◽  
Recognition Systems ◽  
High Level ◽  
The Voice ◽  
Perceived Emotion ◽  
Arousal And Valence

The voice plays a crucial role in expressing emotion in popular music. However, the importance of the voice in this context has not been systematically assessed. This study investigates the emotional effect of vocal features in popular music. In particular, it focuses on nonverbal characteristics, including vocal melody and rhythm. To determine the efficacy of these features, they are used to construct a computational Music Emotion Recognition (MER) system. The system is based on the circumplex model that expresses emotion in terms of arousal and valence. Two independent studies were used to develop the system. The first study established models for predicting arousal and valence based on a range of acoustical and nonverbal vocal features. The second study was used for independent validation of these models. Results show that features describing rhythmic qualities of the vocal line produce emotion models with a high level of generalizability. In particular these models reliably predict emotional valence, a well-known issue in existing Music Emotion Recognition systems.

scholarly journals Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

2016 ◽  
Vol 6 (4) ◽  
pp. 243-253 ◽  
Author(s):  
Christina Brester ◽  
Eugene Semenkin ◽  
Maxim Sidorov
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Emotion Recognition ◽  
Optimization Model ◽  
Heuristic Procedure ◽  
Selection Methods ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Selection For ◽  
High Level

Abstract If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).

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