Using Acoustic Holography for Vibration Analysis

Disertační práce se zabývá bezkontaktní analýzou vibrací pomocí metod akustické holografie v blízkém poli. Akustická holografie v blízkém poli je experimentální metoda, která rekonstruuje akustické pole v těsné blízkosti povrchu vibrujícího předmětu na základě měření akustického tlaku nebo akustické rychlosti v určité vzdálenosti od zkoumaného předmětu. Konkrétní realizace této metody závisí na použitém výpočetním algoritmu. Vlastní práce je zaměřena zejména na rozbor algoritmů, které využívají k rekonstrukci zvukového pole v blízkosti vibrujícího objektu transformaci do domény vlnových čísel (prostorová transformace), kde probíhá vlastní výpočet. V úvodu práce je vysvětlena základní teorie metody akustické holografie v blízkém poli s popisem základních vlastností a dále rozborem konkrétních nejčastěji používaných algoritmům pro lokalizaci a charakterizaci zdroje zvuku a pro následnou vibrační analýzu. Stěžejní část práce se věnuje pokročilým metodám zpracování, které se snaží určitým způsobem optimalizovat přesnost predice zvukového pole v blízkosti vibrujícího předmětu v reálných podmínkách. Jde zejména o problematiku použitého měřicího systému s akustickými snímači, které nejsou ideální, a dále o možnost měření v prostorách s difúzním charakterem zvukového pole. Pro tento případ byla na základě literárního průzkumu optimalizována a ověřena metoda využívající dvouvrstvé mikrofonní pole, které umožňuje oddělení zvukových polí přicházejících z různých stran a tedy úspěšné měření v uzavřených prostorách např. kabin automobilů a letadel. Součástí práce byla také optimalizace, rozšíření a následné ověření algoritmů publikovaných v posledních letech pro měření v reálných podmínkách za použití běžně dostupných akustických snímačů.The main aim of the thesis is application of near-field acoustic holography for non-contact vibration analysis. Near-field acoustic holography is an experimental technique for reconstruction of sound field close to the surface of the vibrating object based on measurement of sound pressure or acoustic particle velocity in certain distance from the examined object. Practical realization of this method depends on used calculation procedure. The thesis is focused on analysis of acoustic holography algorithms with transformation into wavenumber domain (spatial transformation) where the reconstruction of the sound field near vibrating object is calculated. The introductory part of the thesis describes the theory of near-field acoustic holography with general characteristics and with analysis of most common algorithms used for localization and characterization of sound source and consequent vibration analysis. Principal part of the thesis deals with advanced processing methods where these methods try to optimize the accuracy of prediction of sound field near vibrating object in real environment. In this study, real measurement conditions represent the measurement system with non-ideal acoustic sensors and also areas with reverberant sound field. Based on literature study, there has been optimized and verified the new method which uses double layer microphone array to separate incoming and outgoing sound field, thus allows successful measurement in confined space e.g. cabins of cars and airplanes. Part of the thesis has been also focused on optimization, extension and successive experimental validation of selected classical algorithms published in last decade for possible measurement in real conditions and with common acoustic sensors.

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

A Semantic Labeling of the Environment Based on What People Do

In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

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