Emo-CNN for Perceiving Stress from Audio Signals: A Brain Chemistry Approach

• URL: http://arxiv.org/abs/2001.02329v1
• Date: Wed, 8 Jan 2020 01:01:48 GMT
• Title: Emo-CNN for Perceiving Stress from Audio Signals: A Brain Chemistry Approach
• Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier
• Abstract summary: We propose an approach that models human stress from audio signals. Emo-CNN consistently and significantly outperforms the popular existing methods. Lovheim's cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space.
• Score: 2.4087148947930634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotion plays a key role in many applications like healthcare, to gather patients emotional behavior. There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is defining the very meaning of stress and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation and emotion detection is the limited amount of annotated data of stress. The existing labelled stress emotion datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC features in Convolutional Neural Network. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. To tackle the second and the more significant problem of subjectivity in stress labels, we use Lovheim's cube, which is a 3-dimensional projection of emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim's cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Related papers

• UniEmoX: Cross-modal Semantic-Guided Large-Scale Pretraining for Universal Scene Emotion Perception [8.54013419046987]
  We introduce UniEmoX, a cross-modal semantic-guided large-scale pretraining framework for visual emotion analysis. By exploiting the similarity between paired and unpaired image-text samples, UniEmoX distills rich semantic knowledge from the CLIP model to enhance emotional embedding representations. We develop a visual emotional dataset titled Emo8, covering nearly all common emotional scenes.
  arXiv  Detail & Related papers  (2024-09-27T16:12:51Z)
• Emotion Rendering for Conversational Speech Synthesis with Heterogeneous Graph-Based Context Modeling [50.99252242917458]
  Conversational Speech Synthesis (CSS) aims to accurately express an utterance with the appropriate prosody and emotional inflection within a conversational setting. To address the issue of data scarcity, we meticulously create emotional labels in terms of category and intensity. Our model outperforms the baseline models in understanding and rendering emotions.
  arXiv  Detail & Related papers  (2023-12-19T08:47:50Z)
• Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion [87.18073195745914]
  We investigate how well human-annotated emotion triggers correlate with features deemed salient in their prediction of emotions. Using EmoTrigger, we evaluate the ability of large language models to identify emotion triggers. Our analysis reveals that emotion triggers are largely not considered salient features for emotion prediction models, instead there is intricate interplay between various features and the task of emotion detection.
  arXiv  Detail & Related papers  (2023-11-16T06:20:13Z)
• EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes [53.95428298229396]
  We introduce EmoSet, the first large-scale visual emotion dataset annotated with rich attributes. EmoSet comprises 3.3 million images in total, with 118,102 of these images carefully labeled by human annotators. Motivated by psychological studies, in addition to emotion category, each image is also annotated with a set of describable emotion attributes.
  arXiv  Detail & Related papers  (2023-07-16T06:42:46Z)
• Emotion Analysis on EEG Signal Using Machine Learning and Neural Network [0.0]
  The main purpose of this study is to improve ways to improve emotion recognition performance using brain signals. Various approaches to human-machine interaction technologies have been ongoing for a long time, and in recent years, researchers have had great success in automatically understanding emotion using brain signals.
  arXiv  Detail & Related papers  (2023-07-09T09:50:34Z)
• HICEM: A High-Coverage Emotion Model for Artificial Emotional Intelligence [9.153146173929935]
  Next-generation artificial emotional intelligence (AEI) is taking center stage to address users' desire for deeper, more meaningful human-machine interaction. Unlike theory of emotion, which has been the historical focus in psychology, emotion models are a descriptive tools. This work has broad implications in social robotics, human-machine interaction, mental healthcare, and computational psychology.
  arXiv  Detail & Related papers  (2022-06-15T15:21:30Z)
• Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
  Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
  arXiv  Detail & Related papers  (2022-01-10T02:11:25Z)
• DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning [0.0]
  This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. We train the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with responses to the Depression Anxiety Stress Scale (DASS-21) We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress.
  arXiv  Detail & Related papers  (2021-10-26T13:58:46Z)
• Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
  We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
  arXiv  Detail & Related papers  (2021-09-04T08:14:52Z)
• A Circular-Structured Representation for Visual Emotion Distribution Learning [82.89776298753661]
  We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning. To be specific, we first construct an Emotion Circle to unify any emotional state within it. On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
  arXiv  Detail & Related papers  (2021-06-23T14:53:27Z)
• Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments [1.7699344561127386]
  We present empirical analysis on basic and depression specific multi-emotion mining in Tweets. We choose our basic emotions from a hybrid emotion model consisting of the common emotions from four highly regarded psychological models of emotions. We augment that emotion model with new emotion categories because of their importance in the analysis of depression.
  arXiv  Detail & Related papers  (2021-05-26T07:13:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.