Exploring automatic translation between affect representation schemes of music affective content

Studies in Music Affect Content Analysis use varying emotion schemes to represent the states induced when listening to music. However, there are limited studies that explore the translation between these representation schemes. This paper explores the feasibility of using machine learning models to translate from a dimensional scheme of Valence,...

Exploring Automatic Translation between Different Affect Representation Schemes: Affective Image Content Analysis

Images possess the ability to convey a wide range of emotions, and extracting affective information from images is crucial for affect prediction systems. This process can be achieved through the application of machine learning algorithms. Categorical Emotion States (CES) and Dimensional Emotion Space (DES) are two typical models used for...

Survey of Affect Representation Schemes used in Automatic Affect Prediction for Speech Emotion Recognition: A Systematic Review

Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Various datasets for speech emotion recognition were also involved in...

Affect Representation Schemes used in Music Automatic Affect Prediction: A Systematic Literature Review

There is a correlation between music and affect which researchers try to define and use in technology to improve healthcare and users' experience in music-related technology. However, since affect is a complex term there is not a specified way on how to represent different affective states in systems. A systematic literature review was performed...

Survey of Affect Representation Schemes used in Vision-Based Automatic Affect Prediction: A Systematic Literature Review

In human-human interactions, the majority of information is conveyed through body language, specifically facial expressions. Consequently, researchers have been interested in improving human-computer interactions through developing systems with automatic understanding of body language and facial expressions.  This technology is especially useful...

Survey of Affect Representation Schemes in Physiological Automatic Affect Recognition: A Systematic Literature Review

Physiological signals, such as Electroencephalogram (EEG), Glavic Skin Response (GSR), or Body Temperature, are common inputs for Automatic Affect Recognition (AAR) systems. One of the crucial elements of AAR is the Affect Representation Scheme (ARS) used to define the affective states recognized by the system (e.g., happiness, sadness, fear,...

Affect Representation Schemes Used in Affective Video Content Analysis: A Systematic Literature Review

Affective Video Content Analysis aims to automatically analyze the intensity and type of affect (emotion or feeling) that are contained in a video and are expected to arise in users while watching that video. This study aims to provide a systematic overview of various affect representation schemes utilised by researchers in the field of...

Exploring Automatic Translation between Affect Representation Schemes - Text Content Analysis

This research delves into the exploration of translation methods between affect representation schemes within the domain of text content analysis. We assess their performance on various affect analysis tasks while concurrently developing a robust evaluation framework. Furthermore, we collect annotated datasets and take into account crucial...

Exploring Automatic Translation between Affect Representation Schemes: Video Affective Content Analysis

The objective of this report is to establish and present a machine learning model that effectively translates affect representation from emotional attributes such as arousal (passive versus active) and valence (negative versus positive) to dominance (weak versus strong). In the pursuit of this goal, various research questions are addressed. The...

How Reduction in Sample Frequency Hinders the Detection of Words

Living in a world where every single electronic device is online and interconnected, privacy is a growing concern. Finding the threshold where audio is unintelligible to transcription software is crucial when everything that we say can be recorded. Even if Automated Speech Recognition (ASR) is used in tools, such as Siri or Alexa, designed to...