Audio Signal Recognition Based on Intervals’ Numbers (INs) Classification Techniques

Lytridis, Chris; Vrochidou, Εleni; Sidiropoulos, George K.; Papakostas, George A.; Kaburlasos, Vassilis G.; Kourampa, Efi; Karageorgiou, Elpida

doi:10.1109/iisa.2019.8900749

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process resulted in six linguistic cues presented in Table 4 that were used to control the robot's behaviour, when the normal flow has to be changed. The trigger words-recognition implemented algorithm was based on Interval Numbers (INs) [18]. An IN is an established mathematical object that may represent either a fuzzy interval or a distribution of numbers.…”

Section: Resultsmentioning

confidence: 99%

Identifying Linguistic Cues; Towards Developing Robots With Empathy in Autism Interventions

et al. 2020

Self Cite

View full text Add to dashboard Cite

The last decade autism research has been enhanced by the use of social robots and several studies suggest that children with Autism Spectrum Disorders could benefit from robot-assisted interventions. In order to design and implement therapeutic interventions with robots without interrupting the intervention's flow, one should take into account possible technical issues that could arise. The main objective of this study was to gather information from experts in the field of autism and develop linguistic cues to which the robot would respond automatically. A qualitative approach was used to explore specialists' preferences. Online surveys were completed by 33 professionals from different backgrounds to select the vocabulary more often used in psychosocial interventions with autism spectrum disorders children, in specific situations. Six linguistic cues were identified and specific phrases were used so as to accordingly program the robot to show empathy and respond, when a crisis emerges. The session's flow in robot-enhanced interventions could benefit by controlling robot's behaviour with linguistic cues phrased by the therapist. The implications of these findings are discussed in relation to pilot implementation. This work consists of a qualitative study aiming at strengthening the application of a larger research intervention protocol to explore the interaction of children with autism spectrum disorder with a social robot.

show abstract

Section: Resultsmentioning

confidence: 99%

Identifying Linguistic Cues; Towards Developing Robots With Empathy in Autism Interventions

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…During GENETIC optimization, the parameter ranges v 1 ∈[0,10], v 0 ∈[−1000,1000], θ 1 ∈[0,10], θ 0 ∈[−1000,1000], k∈ [4,10] were used; moreover, n ch = 1. Table 5 shows the classification results by the WINkNN classifier compared to the authors' previous results [50] (in brackets), where a time-series was represented by a single IN. For all aforementioned three class groups, the WINkNN classifier resulted in a superior improvement.…”

Section: Audio Signal Classificationmentioning

confidence: 99%

WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

et al. 2020

Self Cite

View full text Add to dashboard Cite

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

show abstract

Toward Robot-Assisted Psychosocial Intervention for Children with Autism Spectrum Disorder (ASD)

Holeva

Nikopoulou

Papadopoulou

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Audio Signal Recognition Based on Intervals’ Numbers (INs) Classification Techniques

Cited by 5 publications

References 12 publications

Identifying Linguistic Cues; Towards Developing Robots With Empathy in Autism Interventions

Identifying Linguistic Cues; Towards Developing Robots With Empathy in Autism Interventions

WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Toward Robot-Assisted Psychosocial Intervention for Children with Autism Spectrum Disorder (ASD)

Contact Info

Product

Resources

About