Implementation of an Autonomous Impulse Response Measurement System

Data collection is crucial for researchers, as it can provide important insights for describing phenomena. In acoustics, acoustic phenomena are characterized by Room Impulse Responses (RIRs) occurring when sound propagates in a room. Room impulse responses are needed in vast quantities for various reasons, including the prediction of acoustical parameters and the rendering of virtual acoustical spaces. Recently, mobile robots navigating within indoor spaces have become increasingly used to acquire information about its environment. However, little research has attempted to utilize robots for the collection of room acoustic data. This thesis presents an adaptable automated system to measure room impulse responses in multi-room environments, using mobile and stationary measurement platforms. The system, known as Autonomous Impulse Response Measurement System (AIRMS), is divided into two stages: data collection and post-processing. To automate data collection, a mobile robotic platform was developed to perform acoustic measurements within a room. The robot was equipped with spatial microphones, multiple loudspeakers and an indoor localization system, which reported real time location of the robot. Additionally, stationary platforms were installed in specific locations inside and outside the room. The mobile and stationary platforms wirelessly communicated with one another to perform the acoustical tests systematically. Since a major requirement of the system is adaptability, researchers can define the elements of the system according to their needs, including the mounted equipment and the number of platforms. Post-processing included extraction of sine sweeps and the calculation of impulse responses. Extraction of the sine sweeps refers to the process of framing every acoustical test signal from the raw recordings. These signals are then processed to calculate the room impulse responses. The automatically collected information was complemented with manually produced data, which included rendering of a 3D model of the room, a panoramic picture. The performance of the system was tested under two conditions: a single-room and a multiroom setting. Room impulse responses were calculated for each of the test conditions, representing typical characteristics of the signals and showing the effects of proximity from sources and receivers, as well as the presence of boundaries. This prototype produces RIR measurements in a fast and reliable manner. Although some shortcomings were noted in the compact loudspeakers used to produce the sine sweeps and the accuracy of the indoor localization system, the proposed autonomous measurement system yielded reasonable results. Future work could expand the amount of impulse response measurements in order to further refine the artificial intelligence algorithms

User localization during human-robot interaction

This paper presents a user localization system based on the fusion of visual information and sound source localization, implemented on a social robot called Maggie. One of the main requisites to obtain a natural interaction between human-human and human-robot is an adequate spatial situation between the interlocutors, that is, to be orientated and situated at the right distance during the conversation in order to have a satisfactory communicative process. Our social robot uses a complete multimodal dialog system which manages the user-robot interaction during the communicative process. One of its main components is the presented user localization system. To determine the most suitable allocation of the robot in relation to the user, a proxemic study of the human-robot interaction is required, which is described in this paper. The study has been made with two groups of users: children, aged between 8 and 17, and adults. Finally, at the end of the paper, experimental results with the proposed multimodal dialog system are presented.The authors gratefully acknowledge the funds provided by the Spanish Government through the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation)

Increasing The Precision Of Noise Source Detection System using KNN Method

This paper proposes Accurate Noise Source Detection System using K-Nearest Neighbor (KNN) Method. Noise or sound intensity is usually measured in decibels (dB). In an educational environment the recommended noise index limit is 55 dB. It means that noise louder than that limit is prohibited. While a loud noise in a campus area occurred, it will be troublesome for the authorities to deal with the matter. This paper proposes a noise source detection system that can locate the position of the noise source. This system used Df analog V2 voice sensor for detecting the loud noise intensity. A microcontroller with WiFi capabilities will allow the system to communicate with an Internet of Things (IoT) platform that can perform a learning method to detect the location of the loud noise source. KNN method is used as the learning method. The result shows a user is able to get a warning related to the noise that occurs in an area at once. The precision position of the noise source can be detected with 70% average accuracy rat

Incorporation of acoustic sensors in the regulation of a mobile robot

This article introduces the incorporation of acoustic sensors for the localization of a mobile robot. The robot is considered as a sound source and its position is located applying a Time Delay of Arrival (TDOA) method. Since the accuracy of this method varies with the microphone array, a navigation acoustic map that indicates the location errors is built. This map also provides the robot with navigation trajectories point-to-point and the control is capable to drive the robot through these trajectories to a desired configuration. The proposed localization method is thoroughly tested using both a 900 Hz square signal and the natural sound of the robot, which is driven near the desired point with an average error of 0:067 m.This is an Accepted Manuscript of an article published by Taylor & Francis in Advanced Robotics on 01/01/2019, available online: http://www.tandfonline.com/10.1080/01691864.2019.1573703.”Peer ReviewedPostprint (author's final draft