The Composite Sensing of Affect

McIntyre, Gordon; Göcke, Roland

doi:10.1007/978-3-540-85099-1_9

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…In recent years, it has become possible to measure users' physiological responses by means of wireless wearable sensors such as the SenseWear armband from BodyMedia. As a result, some multimedia metadata standards now allow for some limited affective description [1,41] and, as was seen in Section 2.1, some internal video summarisation techniques have been developed that summarise video streams based on their affective content. With regards to processing and evaluating physiological response data for the production of video summaries, however, to the best of our knowledge there appears to be no research to date.…”

Section: Physiological Responses For External Video Summarisationmentioning

confidence: 99%

Analysing user physiological responses for affective video summarisation

Money

Agius

2009

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Video summarisation techniques aim to abstract the most significant content from a video stream. This is typically achieved by processing low-level image, audio and text features which are still quite disparate from the high-level semantics that end users identify with (the 'semantic gap'). Physiological responses are potentially rich indicators of memorable or emotionally engaging video content for a given user. Consequently, we investigate whether they may serve as a suitable basis for a video summarisation technique by analysing a range of user physiological response measures, specifically electro-dermal response (EDR), respiration amplitude (RA), respiration rate (RR), blood volume pulse (BVP) and heart rate (HR), in response to a range of video content in a variety of genres including horror, comedy, drama, sci-fi and action. We present an analysis framework for processing the user responses to specific sub-segments within a video stream based on percent rank value normalisation. The application of the analysis framework reveals that users respond significantly to the most entertaining video sub-segments in a range of content domains. Specifically, horror content seems to elicit significant EDR, RA, RR and BVP responses, and comedy content elicits comparatively lower levels of EDR, but does seem to elicit significant RA, RR, BVP and HR responses. Drama content seems to elicit less significant physiological responses in general, and both sci-fi and action content seem to elicit significant EDR responses. We discuss the implications this may have for future affective video summarisation approaches.

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Section: Physiological Responses For External Video Summarisationmentioning

confidence: 99%

Analysing user physiological responses for affective video summarisation

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Agius

2009

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“…In this article, Eyben et al [17] have presented a novel approach to improve the intelligent measures for driving safety in automatic driving systems. Gordon McIntyre and Roland Gocke [18] discussed particular problems faced in emotion recognition system and try to deal with natural way. They presented a novel approach which incorporates semantic descriptions and feature sets of computer vision.…”

Section: Literature Reviewmentioning

confidence: 99%

Passenger Communication System for Next-Generation Self-Driving Cars: A Buddy

Lodhi¹,

Riaz²,

Mehmood³

et al. 2020

IJACSA

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With the rapid emergence of autonomous vehicles, there is a need to build such communication systems which help the passengers to communicate with autonomous vehicles (AVs) robustly. In this regard, this research work presents a multimodal passenger communication system. The communication system is known as "buddy" for AVs. Buddy is an all in one control system for AVs which incorporates touch, speech, text, and emotion recognition methods of interaction. Buddy makes it easy for passengers to interact with AVs. It enables the communication between the passengers and the AV which eventually provides a safe driving experience. Moreover, we have proposed and developed our own simulator two evaluate the performance of our proposed passenger communication system. We have also conducted extensive infield-tests to test the effectiveness of the proposed system. The extensive rigor analysis validates the results and hence the significance of the proposed passenger communication system.

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“…Both the MOUD and CMU-MOSEAS databases share two common constraints. Firstly, their speakers record videos for YouTube, and awareness of being recorded might lead to unintentional emotional control or unnatural expression in response to the artificial situation [43]. Secondly, the nature of YouTube videos in both databases involves monologues, potentially lacking the naturalness found in conversational contexts, as highlighted in [44].…”

Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition from Social Media Voice Messages Recorded in the Wild

Gómez-Zaragozá

Marín‐Morales

Parra

et al. 2020

Communications in Computer and Information Science

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Speech is the most natural way for human communication, carrying the emotional state of the speaker that plays an important role in social interaction. Currently, many instant messaging apps offer the possibility of exchanging voice audios with other users. As a result, a great amount of voice data is generated every day, representing a new challenging approach for speech emotion recognition in real environments. In this study, we investigated emotion recognition from voice messages recorded in the wild using machine-learning algorithms. Unlike most research in this field, which use databases based on emotions evoked in lab environments, simulated by actors or subjectively selected from radio or TV talks, we created an ecological speech dataset with audios from real WhatsApp conversations of 30 Spanish speakers. Four external evaluators labelled each audio in terms of arousal and valence using the Self-Assessment Manikin (SAM) procedure. Pre-processing techniques were applied to the audios and different time and frequency domain features were extracted. Supervised machine learning classifiers were computed using feature reduction and hyper-parameter tuning in order to recognize the affective state of each voice message. The best recognition rate was obtained with Support Vector Machines, achieving 71.37% along the arousal dimension and 70.73% along the valence dimension. These results support the use of emotion recognition models on daily communication apps, helping to understand social human behavior and their interactions with devices in the real world.

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The Composite Sensing of Affect

Cited by 7 publications

References 41 publications

Analysing user physiological responses for affective video summarisation

Analysing user physiological responses for affective video summarisation

Passenger Communication System for Next-Generation Self-Driving Cars: A Buddy

Speech Emotion Recognition from Social Media Voice Messages Recorded in the Wild

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