Investigating the Impact of Sound Angular Position on the Listener Affective State

Drossos, Konstantinos; Floros, Andreas; Γιαννακουλόπουλος, Ανδρέας; Kanellopoulos, Nikolaos

doi:10.1109/taffc.2015.2392768

Cited by 17 publications

(28 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cunningham et al [53,54] used shallow neural networks and regression to predict emotion using the IADS dataset. The authors employed 76 MFCC features using the MIRToolbox.…”

Section: Sound Emotion Recognitionmentioning

confidence: 99%

A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification

et al. 2021

View full text Add to dashboard Cite

Sonification is the utilization of sounds to convey information about data or events. There are two types of emotions associated with sounds: (1) “perceived” emotions, in which listeners recognize the emotions expressed by the sound, and (2) “induced” emotions, in which listeners feel emotions induced by the sound. Although listeners may widely agree on the perceived emotion for a given sound, they often do not agree about the induced emotion of a given sound, so it is difficult to model induced emotions. This paper describes the development of several machine and deep learning models that predict the perceived and induced emotions associated with certain sounds, and it analyzes and compares the accuracy of those predictions. The results revealed that models built for predicting perceived emotions are more accurate than ones built for predicting induced emotions. However, the gap in predictive power between such models can be narrowed substantially through the optimization of the machine and deep learning models. This research has several applications in automated configurations of hardware devices and their integration with software components in the context of the Internet of Things, for which security is of utmost importance.

show abstract

“…Cunningham et al [53,54] used shallow neural networks and regression to predict emotion using the IADS dataset. The authors employed 76 MFCC features using the MIRToolbox.…”

Section: Sound Emotion Recognitionmentioning

confidence: 99%

A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Such contexts demonstrate the close relationship existing between soundscapes and the emotions they evoke, i.e., soundscapes may cause emotional manifestations on the listener side, such as joy. That said, SEP can have a significant impact in a series of application domains, such as sound design [18,22], urban planning [3,24], and acoustic ecology [4,11], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

Emotional quantification of soundscapes by learning between samples

Ntalampiras

2020

Multimed Tools Appl

View full text Add to dashboard Cite

Predicting the emotional responses of humans to soundscapes is a relatively recent field of research coming with a wide range of promising applications. This work presents the design of two convolutional neural networks, namely ArNet and ValNet, each one responsible for quantifying arousal and valence evoked by soundscapes. We build on the knowledge acquired from the application of traditional machine learning techniques on the specific domain, and design a suitable deep learning framework. Moreover, we propose the usage of artificially created mixed soundscapes, the distributions of which are located between the ones of the available samples, a process that increases the variance of the dataset leading to significantly better performance. The reported results outperform the state of the art on a soundscape dataset following Schafer’s standardized categorization considering both sound’s identity and the respective listening context.

show abstract

“…Previous research indicates that the location of a sound source influences the affective states associated with that sound. When the user cannot see where the object is (outside of the field of view), it is often associated with more arousing affective states than when the user can see it (inside the field of view) (Drossos et al, 2015;Tajadura-Jiménez, Larsson, et al, 2010a). Similarly, sounds located further away in the space are related to less arousing responses (Tajadura-Jiménez et al, 2008).…”

Section: Sound Cuesmentioning

confidence: 99%

Affective Visualization in Virtual Reality: An Integrative Review

Pinilla

García

Raffe

et al. 2021

Front. Virtual Real.

View full text Add to dashboard Cite

A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users’ affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Reality (VR) users. Visualization of users’ affective states in VR can lead to biofeedback therapies for mental health care. Understanding how to visualize affective states in VR requires an interdisciplinary approach that integrates psychology, electrophysiology, and audio-visual design. Therefore, this review aims to integrate previous studies from these fields to understand how to develop virtual environments that can automatically create visual representations of users’ affective states. The manuscript addresses this challenge in four sections: First, theories related to emotion and affect are summarized. Second, evidence suggesting that visual and sound cues tend to be associated with affective states are discussed. Third, some of the available methods for assessing affect are described. The fourth and final section contains five practical considerations for the development of virtual reality environments for affect visualization.

show abstract

Investigating the Impact of Sound Angular Position on the Listener Affective State

Cited by 17 publications

References 37 publications

A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification

A Comparative Analysis of Modeling and Predicting Perceived and Induced Emotions in Sonification

Emotional quantification of soundscapes by learning between samples

Affective Visualization in Virtual Reality: An Integrative Review

Contact Info

Product

Resources

About