652 research outputs found

    Experimental evaluation of vehicle cabin noise from suspension induced vibrations using transfer path and psychoacoustic analysis techniques

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    Given the automotive industry\u27s awareness of the importance of the perception of NVH emissions, there is an increased focus on the psychoacoustics, or sound quality, of vehicle cabin noise. The present work aims to qualitatively evaluate and compare automobile cabin noise by measuring the road-induced noise and vibration of a driven and motored vehicle. Evaluation of transmission paths and psychoacoustic analysis of the cabin acoustics are primary objectives. A psychoacoustic analysis using the acoustic pressure measurements taken inside the vehicle cabin was performed using both subjective and objective approaches. Testing also included vibration measurements from several structural positions to evaluate vibroacoustic excitations. Using this noise and vibration data, it was possible to evaluate the transfer path of the excitation energy into the vehicle cabin. Further, an attempt to establish a correlation between the noise and vibration measurements and the psychoacoustic observations was also proven possible with some inherent limitations

    Objective Predictor metric of Annoyance for Hydraulic Engine Mount Cavitation

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    Vehicle acoustics has been found to have a direct impact on customer experience. Unexpected noises play a role in this experience. Hydraulic engine mount cavitation, the noise heard from the collapse of vapor bubbles in the mount, is considered one of those unexpected noises. During the design phase of a vehicle when an unexpected noise is found there is a need for a method to quantify how much of the noise is too much. Subjective evaluations alone are not enough due to variability from engineer to engineer. An objective way needed to be developed in order to evaluate the cavitation noise. To address this issue, an objective predictor metric of annoyance was developed. The model was developed by comparing psychoacoustic metrics to subjective ratings by means of regression analysis. Once the psychoacoustic metrics were chosen multiple regression analysis was used to develop the predictor metric

    ์ฐจ๋Ÿ‰์˜ ์Šคํฌํ‹ฐํ•œ ์—”์ง„์Œ ์ •๋Ÿ‰ํ™”๋ฅผ ์œ„ํ•œ ์Œ์งˆ ์ง€์ˆ˜ ๊ฐœ๋ฐœ๊ณผ ๊ทธ ์ •ํ™•๋„ ํ–ฅ์ƒ์„ ์œ„ํ•œ ๋ฐฉ๋ฒ• ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2020. 8. ๊ฐ•์—ฐ์ค€.Developments in vehicle technology and accompanying improvements in NVH performance have led to increased consumer demand for high sound quality, such as a sporty engine sound. As sporty sound is subjective, this thesis sought to express its meaning quantitatively and to develop a model that accommodates the differences in individuals tastes. This thesis tackles two main issues. The first is to identify the efficiency of factor analysis for utilizing it in developing a sound quality index of sportiness. The second is to further improve the accuracy of the sound quality index and to refine the definition of sportiness by adding K-means cluster analysis. In Chapter 2 and 3, the initial procedure for developing the sportiness index is presented. Accordingly, the process of recording the vehicles interior operating sound under wide open throttle acceleration conditions for 4 different vehicles and producing 13 evaluation samples by using parametric band-pass filtering is described. Acoustic and psychoacoustic parameters of the samples produced were calculated, and the preferences for sportiness were identified through jury testing. Jury test was jointly carried out by 23 evaluators and a semantic differential method was used to find adjectives that could explain the concept and preference for sportiness. The Sportiness index was developed using factor analysis and multiple linear regression analysis between the calculated values and the previously collected jury test results. The index was then validated by examining the correlation coefficient through a new sample group. Furthermore, the necessity of factor analysis for the sportiness index development was concluded. In Chapter 4, after K-means clustering, factor and multiple linear regression analysis were repeated to develop a model reflecting differences for each group in evaluators tastes. The improved index was also retested using new evaluators and new samples, demonstrating its reliability through the high correlation observed in the validation studies. This sound quality evaluation index is useful for producing highly accurate results and reflecting the opinions of groups expressing a variety of commonalities.ํ˜„์žฌ ์ฐจ๋Ÿ‰ ๊ฐœ๋ฐœ ๊ธฐ์ˆ ์ด ๋ฐœ์ „ํ•จ์— ๋”ฐ๋ผ ์ฐจ๋Ÿ‰์˜ NVH ์„ฑ๋Šฅ์ด ๋งŽ์ด ๊ฐœ์„ ๋˜์—ˆ๊ณ , ์ด๋กœ ์ธํ•ด ์†Œ์Œ ์ €๊ฐ์˜ ์ธก๋ฉด๋ณด๋‹ค ๋“ฃ๊ธฐ ์ข‹์€ ์†Œ๋ฆฌ์™€ ๊ฐ™์€ ์Œ์งˆ ์ธก๋ฉด์—์„œ์˜ ์†Œ๋น„์ž์˜ ์ˆ˜์š”๊ฐ€ ๊ณ„์†ํ•ด์„œ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์Šคํฌํ‹ฐํ•œ ์—”์ง„์Œ์ด ๊ทธ ๋ฒ”์ฃผ์— ์†ํ•˜๊ณ , ์ด๋Š” ์‚ฌ๋žŒ๋งˆ๋‹ค ๋– ์˜ฌ๋ฆฌ๋Š” ์ด๋ฏธ์ง€๊ฐ€ ๋‹ค๋ฅด๊ณ  ์†Œ๋ฆฌ์— ๋Œ€ํ•œ ์ทจํ–ฅ์˜ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ์ฃผ๊ด€์ ์ธ ๊ฐœ๋…์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์Œ์งˆ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด์„œ ๊ทธ๋Ÿฌํ•œ ๊ฐœ๋…์˜ ๊ฐ๊ด€์ ์ธ ์˜๋ฏธ๋ฅผ ์ฐพ์•„ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ทจํ–ฅ์˜ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์„ ์ˆ˜์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์„ ์ฐพ๊ธฐ ์œ„ํ•ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ค‘์ ์ ์œผ๋กœ ๋‹ค๋ฃจ๋Š” ๋‚ด์šฉ์€ ํฌ๊ฒŒ ๋‘ ๊ฐ€์ง€์ด๋‹ค. ์ฒซ ๋ฒˆ์งธ๋Š”, ์Šคํฌํ‹ฐํ•จ์˜ ์Œ์งˆ ์ง€์ˆ˜๋ฅผ ๊ฐœ๋ฐœํ•จ์— ์žˆ์–ด ์š”์ธ ๋ถ„์„์„ ํ™œ์šฉํ•จ์œผ๋กœ์จ ์š”์ธ ๋ถ„์„์˜ ํšจ์œจ์„ฑ์„ ํ™•์ธํ•˜๊ณ ์ž ํ•œ ๊ฒƒ์ด๊ณ , ๋‘ ๋ฒˆ์งธ๋Š”, K-ํ‰๊ท  ๊ตฐ์ง‘ ๋ถ„์„์„ ์ถ”๊ฐ€ํ•˜์—ฌ ์Œ์งˆ ์ง€์ˆ˜์˜ ์ •ํ™•๋„๋ฅผ ๋” ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์Šคํฌํ‹ฐํ•จ์˜ ์˜๋ฏธ๋ฅผ ๋”์šฑ ๊ตฌ์ฒดํ™”ํ•˜๊ณ ์ž ํ•œ ๊ฒƒ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ๋ณธ ๋…ผ๋ฌธ์˜ 2์žฅ๊ณผ 3์žฅ์—์„œ๋Š”, ์–‘์‚ฐ๋˜๊ณ  ์žˆ๋Š” ์ฐจ๋Ÿ‰ 4๋Œ€๋ฅผ wide open throttle ์กฐ๊ฑด์—์„œ ์—”์ง„์Œ์„ ๋…น์Œํ•˜์˜€๊ณ , ๋…น์Œ๋œ ์†Œ๋ฆฌ๋กœ๋ถ€ํ„ฐ parametric band-pass filter๋ฅผ ์‚ฌ์šฉํ•ด ์‹ ํ˜ธ๋ฅผ ๋ณ€์กฐํ•˜์—ฌ 13๊ฐœ์˜ ์ƒ˜ํ”Œ์„ ์ œ์ž‘ํ•˜์˜€๋‹ค. ์ œ์ž‘๋œ ์ƒ˜ํ”Œ์˜ ์Œํ–ฅ์‹ฌ๋ฆฌํ•™์  ๋งค๊ฐœ๋ณ€์ˆ˜๋“ค์„ ๊ณ„์‚ฐํ•˜์˜€๊ณ , ์ฒญ์Œ ํ‰๊ฐ€๋ฅผ ํ†ตํ•ด์„œ ์Šคํฌํ‹ฐํ•จ์— ๋Œ€ํ•œ ์„ ํ˜ธ๋„๋ฅผ ํŒŒ์•…ํ•˜์˜€๋‹ค. ์ฒญ์Œ ํ‰๊ฐ€๋Š” 23๋ช…์˜ ํ‰๊ฐ€์ž๊ฐ€ ์ฐธ์—ฌํ•˜์˜€๊ณ , ์˜๋ฏธ๋ฏธ๋ถ„๋ฒ•์„ ์‚ฌ์šฉํ•ด ์Šคํฌํ‹ฐํ•จ์˜ ์„ ํ˜ธ๋„์™€ ์Šคํฌํ‹ฐํ•จ์„ ์ž˜ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋Š” ํ˜•์šฉ์‚ฌ๋“ค์„ ์ฐพ์•„๋ƒˆ๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์š”์ธ ๋ถ„์„์— ์ ์šฉํ•ด ์‚ฌ๋žŒ๋“ค์ด ๊ณตํ†ต์ ์œผ๋กœ ๋Š๋ผ๋Š” ์Šคํฌํ‹ฐํ•จ์˜ ํŠน์„ฑ์„ ๋‘ ์š”์ธ์œผ๋กœ ํ‘œํ˜„ํ•˜์˜€๊ณ , ํ‰๊ฐ€ ๊ฒฐ๊ณผ ๊ฐ„ ๋‹ค์ค‘ ์„ ํ˜• ํšŒ๊ท€ ๋ถ„์„์„ ์ด์šฉํ•ด ๊ด€๋ จ๋œ ์Œ์งˆ ์ธ์ž๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ์Šคํฌํ‹ฐํ•จ ์ •๋Ÿ‰ํ™” ์ง€์ˆ˜๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์ง€์ˆ˜๋Š” ์ƒˆ๋กœ์šด ์ƒ˜ํ”Œ๊ตฐ์„ ํ†ตํ•ด ์ƒ๊ด€๊ณ„์ˆ˜๋ฅผ ํ™•์ธํ•˜์—ฌ ๊ทธ ์œ ํšจ์„ฑ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ์š”์ธ ๋ถ„์„ ์‚ฌ์šฉ ์œ ๋ฌด์— ๋”ฐ๋ฅธ ํšŒ๊ท€์‹์˜ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•จ์œผ๋กœ์จ ์š”์ธ ๋ถ„์„์˜ ํ•„์š”์„ฑ์— ๋Œ€ํ•ด์„œ๋„ ์–ธ๊ธ‰ํ•˜์˜€๋‹ค. 4์žฅ์—์„œ๋Š”, ์Šคํฌํ‹ฐํ•จ์— ๋Œ€ํ•œ ํ‰๊ฐ€์ž๋“ค์˜ ์„ฑํ–ฅ ์ฐจ์ด๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๊ฒƒ์„ ํ† ๋Œ€๋กœ K-ํ‰๊ท  ๊ตฐ์ง‘ ๋ถ„์„์„ ํ™œ์šฉํ•ด ๊ฐ ์ง‘๋‹จ์— ๋งž๋Š” ํšŒ๊ท€์‹์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•ด ์š”์ธ ๋ถ„์„๊ณผ ๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€ ๋ถ„์„์„ ์žฌ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ฐœ๋ฐœ๋œ ์ง€์ˆ˜์˜ ์‹ ๋ขฐ์„ฑ์„ ์—ญ์‹œ ํ™•๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์ƒˆ๋กœ์šด ํ‰๊ฐ€์ž๋“ค๋กœ ์žฌ๊ฒ€์‚ฌํ•˜์˜€๊ณ  ๋†’์€ ์ƒ๊ด€๊ณ„์ˆ˜๋ฅผ ํ† ๋Œ€๋กœ ๊ทธ ์‹ ๋ขฐ์„ฑ์„ ์ž…์ฆํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ฐœ๋ฐœ๋œ ์Œ์งˆ ํ‰๊ฐ€ ์ง€์ˆ˜๋Š” ์Šคํฌํ‹ฐํ•จ์„ ๊ฐ๊ด€์ ์œผ๋กœ ์ •์˜ํ•จ์— ์žˆ์–ด ๋˜ ๋‹ค๋ฅธ ๊ณตํ†ต์„ฑ์„ ๋‚˜ํƒ€๋‚ด๋Š” ์ง‘๋‹จ์˜ ์˜๊ฒฌ๊นŒ์ง€๋„ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๊ณ  ์ •ํ™•๋„ ๋†’์€ ๊ฒฐ๊ณผ๋ฅผ ์‚ฐ์ถœํ•ด์ฃผ๋Š” ์œ ์šฉํ•œ ์ง€์ˆ˜์ด๋‹ค.CHAPTER 1 INTRODUCTION 1 CHAPTER 2 SOUND QUALITY EVALUATION OF VEHICLE ENGINE SPORTINESS 6 2.1 Introduction 6 2.2 Sound recording and objective evaluation of engine sound 7 2.2.1 Recording of interior sound 7 2.2.2 Production of sound samples 12 2.2.3 Calculation of objective acoustic and psychoacoustic parameters 16 2.2.3.1 Sound pressure level 18 2.2.3.2 Loudness 19 2.2.3.3 Sharpness 20 2.2.3.4 Roughness 21 2.2.3.5 Tonality 22 2.3 Subjective evaluation of sound quality 23 2.3.1 Semantic differential method and pre-test 23 2.3.2 Jury testing 26 CHAPTER 3 DEVELOPMENT OF EVALUATION INDEX OF SPORTY ENGINE SOUND : USING FACTOR ANALYSIS 32 3.1 Introduction 32 3.2 Factor analysis 33 3.3 Regression analysis 42 3.3.1 Multiple linear regression 42 3.3.2 Development of a sound quality index for sportiness 44 3.4 Validation 50 3.5 Summary 55 CHAPTER 4 NEW APPROACH TO DEVELOPMENT OF EVALUATION INDEX OF SPORTY ENGINE SOUND : USING K-MEANS CLUSTER ANALYSIS 57 4.1 Introduction 57 4.2 Statistical analysis 59 4.2.1 K-means cluster analysis 59 4.2.2 Factor analysis after K-means clustering 66 4.2.3 Regression analysis after K-means clustering 71 4.3 Validation 78 4.4 Summary 83 CHAPTER 5 CONCLUSIONS 86 REFERENCES 89 APPENDIX 98 ๊ตญ ๋ฌธ ์ดˆ ๋ก 102Docto

    Review of active noise control techniques with emphasis on sound quality enhancement

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    The traditional active noise control design aims to attenuate the energy of residual noise, which is indiscriminative in the frequency domain. However, it is necessary to retain residual noise with a specified spectrum to satisfy the requirements of human perception in some applications. In this paper, the evolution of active noise control and sound quality are briefly discussed. This paper emphasizes on the advancement of active noise control method in the past decades in terms of enhancing the sound quality

    Investigating Perceptual Congruence Between Data and Display Dimensions in Sonification

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    The relationships between sounds and their perceived meaning and connotations are complex, making auditory perception an important factor to consider when designing sonification systems. Listeners often have a mental model of how a data variable should sound during sonification and this model is not considered in most data:sound mappings. This can lead to mappings that are difficult to use and can cause confusion. To investigate this issue, we conducted a magnitude estimation experiment to map how roughness, noise and pitch relate to the perceived magnitude of stress, error and danger. These parameters were chosen due to previous findings which suggest perceptual congruency between these auditory sensations and conceptual variables. Results from this experiment show that polarity and scaling preference are dependent on the data:sound mapping. This work provides polarity and scaling values that may be directly utilised by sonification designers to improve auditory displays in areas such as accessible and mobile computing, process-monitoring and biofeedback

    CPX based synthesis for binaural auralization of vehicle rolling noise to an arbitrary positioned stander-by receiver

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    Virtual reality is becoming an important tool for studying the interaction between pedestrians and road vehicles, by allowing the analysis of potentially hazard situations without placing subjects in real risk. However, most of the current simulators are unable to accurately recreate traffic sounds that are congruent with the visual scene. This has been recognized as a fault in the virtual audio-visual scenarios used in such contexts. This study proposes a method for delivering a binaural auralization of the noise generated by a moving vehicle to an arbitrarily located moving listener (pedestrian). Building on previously developed methods, the proposal presented here integrates in a novel way a dynamic auralization engine, thus enabling real-time update of the acoustic cues in the binaural signal delivered via headphones. Furthermore, the proposed auralization routine uses Close ProXimity (CPX) tyre-road noise signal as sound source input, facilitating the quick interchangeability of source signals, and easing the noise collection procedure. Two validation experiments were carried out, one to quantitatively compare field signals with CPX-derived virtual signal recordings, and another to assess these same signals through psychoacoustic models. The latter aims to assure that the reproduction of the synthesized signal is perceptually similar to one occurring on pedestrian/vehicle interactions during situations of street crossing. Discrepancies were detected, and emphasized when the vehicle is within close distance from the receiver (pedestrian). However, the analysis indicated that these pose no hindrance to the study of vehicleโ€“pedestrian interaction. Improvements to the method are identified and further developments are proposed.This work was supported by the โ€˜โ€˜Fundaรงรฃo para a Ciรชncia e a Tecnologiaโ€ [PTDC/ECM-TRA/3568/2014, SFRH/BD/131638/2017, UIDB/04029/2020] This work is part of the activities of the research project AnPeB โ€“ โ€˜โ€˜ANalysis of PEdestrians Behaviour based on simulated urban environments and its incorporation in risk modellingโ€ (PTDC/ECM TRA/3568/2014), funded by the โ€˜โ€˜Promover a Produรงรฃo Cientรญfica e Desenvolvimento Tecnolรณgico e a Constituiรงรฃo de Redes Temรกti casโ€ (3599-PPCDT) project and supported by the โ€˜โ€˜European Com munity Fund FEDERโ€ and the doctoral scholarship SFRH/ BD/131638/2017, funded by โ€˜โ€˜Fundaรงรฃo para a Ciรชncia e a Tecnolo gia (FCT)โ€

    Squeak and Rattle Prediction for Robust Product Development

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    Squeak and rattle are nonstationary, irregular and impulsive sounds that happen inside the car cabin. For decades, customer complaints about squeak and rattle have been, and still are, among the top quality issues in the automotive industry. These annoying sounds are perceived as quality defect indications and burden warranty costs to the car manufacturers. Today, the quality improvements regarding the persistent type of sounds in the car, as well as the increasing popularity of electric engines, as green and quiet propulsion solutions, stress the necessity for suppressing annoying sounds like squeak and rattle more than in the past. The technical solution to this problem is to approach it in the pre-design-freeze phases of the product development and by employing design-concept-related practises. To nail this goal, prediction and evaluation tools and methods are needed to deal with the squeak and rattle quality issues upfront in the product development process. The available tools and methods for prediction of squeak and rattle sounds in the pre-design-freeze phase in a new car development process are not yet sufficiently mature. The existing knowledge gap about the mechanisms behind the squeak and rattle sounds, the lack of accurate simulation and post-processing methods, as well as the computational cost of complex simulations are some of the significant hurdles in this immaturity. This research addresses this problem by identifying a framework for prediction of squeak and rattle sounds in the form of a cause and effect diagram. The main domains and the elements and the sub-contributors to the problem in each domain within this framework are determined through literature studies, field explorations and the conducted descriptive studies on the subject. Further, improvement suggestions for the squeak and rattle evaluation and prediction methods are proposed through prescriptive studies. The applications of some of the proposed methods in the automotive industry are shown and examined in industrial problems
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