Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Seo, Yeong-Seok; Huh, Jun‐Ho

doi:10.3390/electronics8020164

Cited by 88 publications

(36 citation statements)

References 25 publications

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“…20,21 Zigbee wireless network is composed of several routers, a coordinator, and several terminal devices. By connecting to Wi-Fi, digital information can be converted into action mode signals, so as to realize the control of equipment.…”

Section: Wireless Information Transmission Designmentioning

confidence: 99%

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

2019

Trans Emerging Tel Tech

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With the development of society, Internet of Things (IoT) technology has attracted more and more attention. This paper uses IoT technology to enhance the music teaching assistant platform. Firstly, through analyzing users' requirements for music teaching assistant platform, based on IoT technology, the assistant platform is analyzed, and the wireless information transmission system is established. Secondly, aiming at the consumption of router nodes, the consumption model of nodes is established. By analyzing the energy consumption and service life of router nodes, it is concluded that traditional IoT has great disadvantages. Then, an improved repair strategy of IoT is proposed according to immune algorithm. By comparing three kinds of wireless sensor networks, the improved repair strategy of IoT is better when the number of router nodes is more than 400. When it comes to saving energy, there are great advantages in quantity consumption. Finally, by simulating and analyzing the fault repair of router nodes, it is concluded that the number of effective router nodes in traditional IoT decreases the fastest with the number of simulated runs, followed by energy‐gathering IoT, and the lifetime of effective router nodes in IoT is the longest with the energy‐gathering improved by immune algorithm. This research mainly focuses on the energy consumption of router nodes in the perception layer of IoT, as well as the network immunity and repair mechanism of the system. It improves and designs the traditional music teaching assistant platform, which has certain practical significance.

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Section: Wireless Information Transmission Designmentioning

confidence: 99%

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

2019

Trans Emerging Tel Tech

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“…The FRR is the frequency of the system rejecting a true claim of authorization. The FAR is defined as the frequency of the system incorrectly accepting an unauthorized user [38,[46][47][48][49][50][51][52]. The ERR is defined as We employed the true acceptance rate (TAR), the false rejection rate (FRR), the false acceptance rate (FAR), and the equal error rate (EER) [38,[42][43][44][45][46] as performance evaluation measurements.…”

Section: Methodsmentioning

confidence: 99%

“…The FRR is the frequency of the system rejecting a true claim of authorization. The FAR is defined as the frequency of the system incorrectly accepting an unauthorized user [38,[46][47][48][49][50][51][52]. The ERR is defined as the error rate when FAR and FRR are equal for a particular threshold [44].…”

Section: Methodsmentioning

confidence: 99%

Face Recognition at a Distance for a Stand-Alone Access Control System

Lee

Park

Yoo

et al. 2020

Sensors

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Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

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“…A large number of studies have focused on emotion [18]. This study argues that, for long-term HCI, the interface's personality plays a more crucial role than simple emotional reactions.…”

Section: Expressing Internal Machine States In Hcimentioning

confidence: 99%

“…In particular, the gaze of the conversational agents were used to deliver back-channel feedback in turn-taking situations [13]. Diverse colors and lights were also used to express the social robots' emotions [14][15][16], and a simple blinking light was used as a subtle expression to smooth human-robot speech interactions [17].A large number of studies have focused on emotion [18]. This study argues that, for long-term HCI, the interface's personality plays a more crucial role than simple emotional reactions.…”

mentioning

confidence: 99%

Expressing Personalities of Conversational Agents through Visual and Verbal Feedback

Lee

Kim

et al. 2019

Electronics

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As the uses of conversational agents increase, the affective and social abilities of agents become important with their functional abilities. Agents that lack affective abilities could frustrate users during interaction. This study applied personality to implement the natural feedback of conversational agents referring to the concept of affective computing. Two types of feedback were used to express conversational agents' personality: (1) visual feedback and (2) verbal cues. For visual feedback, participants (N = 45) watched visual feedback with different colors and motions. For verbal cues, participants (N = 60) heard different conditions of agents' voices with different scripts. The results indicated that the motions of visual feedback were more significant than colors. Fast motions could express distinct and positive personalities. Different verbal cues were perceived as different personalities. The perceptions of personalities differed according to the vocal gender. This study provided design implications for personality expressions applicable to diverse interfaces. amplitudes and speeds of gestures and movements. Extroverts demonstrate faster, wider, and broader movements than introverts [4]. In addition, extroverts tend to demonstrate more reactive and faster movements and body gestures than introverts [5]. Personality and verbal traits are also highly interrelated. Extroverts tend to express more emotionality with positive emotions and use fewer formal expressions with agreement and gratitude [6]. Extroverts also demonstrate shorter silences and use more positive words and informal expressions than introverts. Introverts use more abstract words and formal words than extroverts [2,7].Gestures are difficult to implement with current conversational agents because most of them are designed in the form of AI speakers. Existing conversational agents in the current market, including Amazon Alexa and Google Home, are not able to implement gesture movements. Instead of using gestures, AI speakers deliver simple visual feedback through a smart display in response to voice commands. Considering the current form of conversational agents, visual feedback was chosen as the personality expression element. As people with different personalities demonstrate different gesture speeds, the lighting speed could also be perceived as different personalities. Quick lighting is perceived as more active than slow lighting; therefore, the study posits the following research question: RQ 1. Can different visual feedback be perceived as different personalities?Most current AI speakers use a consistent voice with the same speech style. As extroverts and introverts demonstrate different verbal traits, different verbal traits are perceived as different personalities. Therefore, the study posits the second research question: RQ 2. Can different verbal cues be perceived as different personalities?Unlike previous studies, the current study applied a wider range of personalities rather than focusing on expressing only two contrasting persona...

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Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Cited by 88 publications

References 25 publications

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

Retracted: Design and implementation of music teaching assistant platform based on Internet of Things

Face Recognition at a Distance for a Stand-Alone Access Control System

Expressing Personalities of Conversational Agents through Visual and Verbal Feedback

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