On Indexicality, Direction of Arrival of Sound Sources, and Human-Robot Interaction

Meza, Iván; Rascón, Caleb; Fuentes, Gibrán; Pineda, Luis A.

doi:10.1155/2016/3081048

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…This could provide information to assist the police and other emergency personnel by reducing their emergency response time [11]. Such a method could also be applicable in human-robot interaction systems [12], service robots [13], intelligent hearing aids [14], smart homes [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Yiwere

Rhee

2019

Sensors

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This paper presents a sound source distance estimation (SSDE) method using a convolutional recurrent neural network (CRNN). We approach the sound source distance estimation task as an image classification problem, and we aim to classify a given audio signal into one of three predefined distance classes—one meter, two meters, and three meters—irrespective of its orientation angle. For the purpose of training, we create a dataset by recording audio signals at the three different distances and three angles in different rooms. The CRNN is trained using time-frequency representations of the audio signals. Specifically, we transform the audio signals into log-scaled mel spectrograms, allowing the convolutional layers to extract the appropriate features required for the classification. When trained and tested with combined datasets from all rooms, the proposed model exhibits high classification accuracies; however, training and testing the model in separate rooms results in lower accuracies, indicating that further study is required to improve the method’s generalization ability. Our experimental results demonstrate that it is possible to estimate sound source distances in known environments by classification using the log-scaled mel spectrogram.

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Section: Introductionmentioning

confidence: 99%

Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Yiwere

Rhee

2019

Sensors

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“…Our work is developed in the context of the Golem project: a three generation family of service robots based on the evolution of the same conceptual model and functional structure [21]. The Golem robot performs several tasks such as being a clerk in a super market [23], being a waiter in a restaurant [25] or playing Marco-Polo [17]. The core of the robot consists on a set of compositional behaviors defining the structure of the task that the robot must perform, which is specified using the SitLog robot programming language [22].…”

Section: Introductionmentioning

confidence: 99%

Robot experience stories: First person generation of robotic task narratives in SitLog1

Flores

Meza

Colin

et al. 2018

IFS

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What if service robots could tell the story of the task they've just realized like a story? The aim of our work is to provide service robots with natural language capabilities to produce a Robot Experience Story for its human interlocutors. Robxp stories are narratives composed of the robot's holistic perception of a recently performed task: navigation, visual perceptions and action descriptions. We contribute with a narrate dialog model specifying the composition of situations necessary for a service robot to transform its task history record into a narrative knowledge representation. We provide SitLog algorithms allowing to analyze the robot's situation and behaviors sequence in order to generate a robxp story of the task. Both the dialogue model and the algorithms can be embedded as compositional behaviors in any other SitLog task structure. We instantiated our model into the Golem service robot framework on an experimental task. We believe Robxp stories generation could be integrated as a standard behavior for more complex service robot tasks.

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A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence

Jalayer,

Orsenigo

et al. 2023

Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems

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On Indexicality, Direction of Arrival of Sound Sources, and Human-Robot Interaction

Cited by 6 publications

References 33 publications

Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Sound Source Distance Estimation Using Deep Learning: An Image Classification Approach

Robot experience stories: First person generation of robotic task narratives in SitLog1

A Conceptual Framework for Localization of Active Sound Sources in Manufacturing Environment Based on Artificial Intelligence

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