Predicting emotional response to music through a compound neural network

2017 ◽  
Vol 46 (2) ◽  
pp. 222-237
Author(s):  
Elena Saiz-Clar ◽  
José M. Reales
Keyword(s):  
Neural Network ◽  
Emotional Valence ◽  
Self Organizing Map ◽  
Circumplex Model ◽  
Competing Models ◽  
Som Network ◽  
Cultural Component ◽  
The Relationship ◽  
Emotional Effects ◽  
Music And Emotion

The emotional effects of music have a cross-cultural component that can be explained through the tonal and non-tonal properties of musical pieces. To investigate the relationship between music and the emotions it arouses, we have built a composite neural network with the aim of predicting both the emotional categorization and the emotional valence and activation of Vieillard et al.’s (2008) musical stimuli. Our neural network uses two Adalines in the first level of the structure to predict activation and emotional valence from a minimal set of temporal and tonal properties of the stimuli (rhythm, tempo, time signature, mode, absolute tonal range and the frequency of the lowest note). In the second level, the network uses a Self-Organizing Map (SOM) network to classify the stimuli into four emotional categories (calm, happiness, fear and sadness). The results have allowed us to replicate the features of the Circumplex Model of Emotion. The percentage of explained variance obtained for activation is satisfactory and higher than in previous research for emotional valence. The percentage of music pieces correctly classified by the SOM was also very high (87%). We discuss the results in relation to competing models of music and emotion.

scholarly journals Clustering and mapping spatial-temporal datasets using SOM neural networks

2003 ◽  
Vol 13 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Irini Reljin ◽  
Branimir Reljin ◽  
Gordana Jovanovic
Keyword(s):  
Neural Network ◽  
High Efficiency ◽  
Function Analysis ◽  
Linear Method ◽  
Nonlinear Phenomena ◽  
Self Organizing Map ◽  
Climate Data ◽  
Significant Similarity ◽  
Nonlinear Methods ◽  
Som Network

Large datasets can be analyzed through different linear and nonlinear methods. Most frequently used linear method Is Principal Component Analysis (PCA) known also as EOF (Empirical Orthogonal Function) analysis, permitting both clustering and visualizing high-dimensional data Items. However, many problems are nonlinear In nature, so, for analyzing such a problems some nonlinear methods will be more appropriate. The SOM (Self-Organizing Map) neural network is very promising tool for clustering and mapping spatial-temporal datasets describing nonlinear phenomena. The SOM network is applied on the precipitation and temperature data observed in the region of Serbia and Montenegro during 48 years period (1951-1998) and the zonal maps of homogeneous geographical units are derived. These maps are compared with those recently derived via EOF analysis. Significant similarity of results derived from the two methods confirms high efficiency of the SOM network in analyzing spatial-temporal fields. Moreover, the SOM neural network is more appropriate in analyzing climate data since both climate data and the SOM analyzing method are nonlinear in nature.

scholarly journals Interactive relations among time, music and emotion

2020 ◽  
Vol 18 (1) ◽  
pp. 104
Author(s):  
Stavroula Samartzi ◽  
Maria Panagiotidi
Keyword(s):  
Cognitive Processing ◽  
Current Knowledge ◽  
Musical Training ◽  
Time Estimation ◽  
Emotional Valence ◽  
Structural Features ◽  
Hemispheric Dominance ◽  
Expression Of Emotions ◽  
The Relationship ◽  
Music And Emotion

Research so far has shown that the emotional content of information affects time perception, through the mechanism of subjectivisation (i.e. shrinking or expanding temporal duration as a result of positive and negative emotional valence, respectively). Additionally, preliminary studies suggest that musically trained individuals compared to untrained ones tend to make more accurate duration judgements. Finally, it is known that music can induce specific moods; two of the main factors that determine the relationship between music and emotion are the structural features of the song and the features of the listener. However, it is not clear whether any interactive relations exist among these factors. In this study we attempted to address this particular gap in our current knowledge. As neuroscience studies show, when non musicians are listening to music there is activation of right cerebral areas while musicians show left hemispheric dominance. Right cerebral areas arerelated to the recognition and the expression of emotions and their activation suggests a cognitive processing based on the emotional valence of songs. Thus, it seems that musical training affects emotion(by inducing certain moods) that, consecutively, affects time estimation.

scholarly journals A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Khaled Ben Khalifa ◽  
Ahmed Ghazi Blaiech ◽  
Mohamed Hédi Bedoui
Keyword(s):  
Neural Network ◽  
Recent Literature ◽  
Generic Model ◽  
Network Architectures ◽  
Self Organizing Map ◽  
Modular Architecture ◽  
Systolic Architecture ◽  
Som Network ◽  
Som Neural Network ◽  
Self Organizing

In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is formed by two levels of nested parallelism of neurons and connections. Thus, this solution provides a distributed set of independent computations between the processing units called neuroprocessors (NPs) which define the SSOM architecture. The NP modules have an innovative architecture compared to those proposed in the literature. Indeed, each NP performs three different tasks without requiring additional external modules. To validate our approach, we evaluate the performance of several SOM network architectures after their integration on an FPGA support. This architecture has achieved a performance almost twice as fast as that obtained in the recent literature.

Thermal anomaly detection in datacenters

2011 ◽  
Vol 226 (8) ◽  
pp. 2104-2117 ◽  
Author(s):  
Yang Yuan ◽  
Eun Kyung Lee ◽  
Dario Pompili ◽  
Junbi Liao
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Large Scale ◽  
Thermal Anomaly ◽  
Detection Methods ◽  
Support Vector ◽  
Self Organizing Map ◽  
Air Conditioner ◽  
Thermal Anomalies ◽  
The Relationship

The high density of servers in datacenters generates a large amount of heat, resulting in the high possibility of thermally anomalous events, i.e. computer room air conditioner fan failure, server fan failure, and workload misconfiguration. As such anomalous events increase the cost of maintaining computing and cooling components, they need to be detected, localized, and classified for taking appropriate remedial actions. In this article, a hierarchical neural network framework is proposed to detect small- (server level) and large-scale (datacenter level) thermal anomalies. This novel framework, which is organized into two tiers, analyzes the data sensed by heterogeneous sensors such as sensors built in the servers and external sensors (Telosb). The proposed solution employs a neural network to learn about (a) the relationship among sensing values (i.e. internal, external, and fan speed) and (b) the relationship between the sensing values and workload information. Then, the bottom tier of our framework detects thermal anomalies, whereas the top tier localizes and classifies them. Our solution outperforms other anomaly-detection methods based on regression model, support vector machine, and self-organizing map, as shown by the experimental results.

Music and Emotion

2017 ◽  
Author(s):  
Lawrence M. Zbikowski
Keyword(s):  
Emotional Responses ◽  
The Relationship ◽  
Music And Emotion

This chapter explores the relationship between music and emotion, beginning with a review of research on emotion, followed by a review of research on music and emotion. It is proposed that the connection between music and the emotions reflects music’s capacity to provide sonic analogs for some of the most salient aspects of emotion processes. This proposal is illustrated through analyses of two movements from J. S. Bach’s cantata “Ich habe genug,” which make explicit two important features of musical grammar: syntactic processes and syntactic layers. The chapter concludes with observations about the ways music is used to shape emotional responses within liturgical settings of the kind that motivated and framed Bach’s cantata.

scholarly journals A Method of Ore Blending Based on the Quality of Beneficiation and Its Application in a Concentrator

2021 ◽  
Vol 11 (11) ◽  
pp. 5092
Author(s):  
Bingyu Liu ◽  
Dingsen Zhang ◽  
Xianwen Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Industrial Test ◽  
Modeling Method ◽  
Daily Work ◽  
Production Site ◽  
Neural Network Algorithm ◽  
The Relationship ◽  
Ore Dressing

Ore blending is an essential part of daily work in the concentrator. Qualified ore dressing products can make the ore dressing more smoothly. The existing ore blending modeling usually only considers the quality of ore blending products and ignores the effect of ore blending on ore dressing. This research proposes an ore blending modeling method based on the quality of the beneficiation concentrate. The relationship between the properties of ore blending products and the total concentrate recovery is fitted by the ABC-BP neural network algorithm, taken as the optimization goal to guarantee the quality of ore dressing products at the source. The ore blending system was developed and operated stably on the production site. The industrial test and actual production results have proved the effectiveness and reliability of this method.

scholarly journals No Self Without Salience: Affective and Self-relevance Ratings of 552 Emotionally Valenced and Neutral Dutch Words

2021 ◽  
Author(s):  
Lora I. Dimitrova ◽  
Eline M. Vissia ◽  
Hanneke Geugies ◽  
Hedwig Hofstetter ◽  
Sima Chalavi ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Emotional Valence ◽  
Age And Gender ◽  
Gender And Age ◽  
Main Effect ◽  
Emotional Salience ◽  
And Gender ◽  
The Relationship

AbstractIt is unknown how self-relevance is dependent on emotional salience. Emotional salience encompasses an individual's degree of attraction or aversion to emotionally-valenced information. The current study investigated the interconnection between self and salience through the evaluation of emotional valence and self-relevance. 56 native Dutch participants completed a questionnaire assessing valence, intensity, and self-relevance of 552 Dutch nouns and verbs. One-way repeated-measures ANCOVA investigated the relationship between valence and self, age and gender. Repeated-measures ANCOVA also tested the relationship between valence and self with intensity ratings and effects of gender and age. Results showed a significant main effect of valence for self-relevant words. Intensity analyses showed a main effect of valence but not of self-relevance. There were no significant effects of gender and age. The most important finding presents that self-relevance is dependent on valence. These findings concerning the relationship between self and salience opens avenues to study an individual's self-definition.

scholarly journals Thermal-wet model of knitted double jersey based on backpropagation algorithm of neural network

2020 ◽  
Vol 15 ◽  
pp. 155892501990083
Author(s):  
Xintong Li ◽  
Honglian Cong ◽  
Zhe Gao ◽  
Zhijia Dong
Keyword(s):  
Neural Network ◽  
Water Vapor ◽  
Thermal Resistance ◽  
Resistance Test ◽  
Training Algorithm ◽  
Backpropagation Algorithm ◽  
Optimal Network ◽  
Resistance Model ◽  
Hidden Layer ◽  
The Relationship

In this article, thermal resistance test and water vapor resistance test were experimented to obtain data of heat and humidity performance. Canonical correlation analysis was used on determining influence of basic fabric parameters on heat and humidity performance. Thermal resistance model and water vapor resistance model were established with a three-layered feedforward-type neural network. For the generalization of the network and the difficulty of determining the optimal network structure, trainbr was chosen as training algorithm to find the relationship between input factors and output data. After training and verification, the number of hidden layer neurons in the thermal resistance model was 12, and the error reached 10−3. In the water vapor resistance model, the number of hidden layer neurons was 10, and the error reached 10−3.

Sign in / Sign up

Export Citation Format

Share Document