Conference Papers are technical papers that have been presented at sound and vibration conferences around the world.
Over the past five years, MTS Systems Corp. has witnessed, through customer feedback from around the world, a slow but definite shift within the noise and vibration practices of the automotive industry. The automotive industry represents more than half of the $300 million market for noise and vibration data acquisition and analysis hardware and software.
The noise of a residential refrigerator exhibited some undesirable high frequency tones. The tones did not affect the overall A-weighted sound power level of the unit, but they represented a sound quality concern for the refrigerator manufacturer. This paper describes the analytical and experimental techniques that were applied to troubleshoot and solve the problem. An analytical model of the compressor discharge muffler was built, validated with test data, and used to identify the best countermeasures.
For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC. Acoustic and structural loads to the cab in the new model are estimated from measurements on hardware components and from supplier provided data. The hybrid FE-SEA model is then updated with the new loads and re-run to predict total in-cab noise and contributions from each source in the new model, which is then used to identify the best countermeasures for noise reduction and SQ improvement.
Ubiquitous availability of media content through portable devices like media players and smartphones has resulted in an immensely increased popularity of headphones in recent years. However, while conventional stereo recordings usually create a good sense of space when listened to through loudspeakers, the sounds tend to be perceived inside the head (internalized) when headphones are used for listening. A more natural perception in headphone listening with sounds being perceived outside the head (externalized) can be achieved when recordings are made with dummy head microphones or with microphones placed inside the ear canals of a person. In this study, binaural room impulse responses (BRIRs) were measured with several commercially available binaural microphones, both placed inside the listeners' ears (individual BRIR) and on a head and torso simulator (generic BRIR). The degree of externalization of speech and noise stimuli was tested in a listening experiment with a multi-stimulus test. No inuence was found for the stimulus signal, but the externalization scores were found to be lower for 0 incidence. With all microphones, relatively high externalization scores were achieved, and for all but one microphone, individual BRIRs resulted in slightly better externalization than generic ones.
Subspace based algorithms for estimating modal parameter have now become common within modal analysis domain. This is especially true for Operational Modal Analysis, where Stochastic Subspace Identification (SSI) algorithm is a well-known and commonly used algorithm. Despite their increasing use and popularity, one often encounters basic questions such as (and not limited to)
In fact, even before addressing the questions listed above, there is a fundamental need to look at these algorithms from the perspective of modal parameter estimation, whose requirements and demands differ from those of system identification within Control Systems Engineering, where these algorithms originated.
This paper aims at addressing these issues and examine subspace algorithms from a purely modal parameter estimation perspective. The author expects that this paper will provide readers with a simple and clear understanding of these algorithms towards their utilization for modal parameter estimation.
Indoor vehicle pass-by noise applications deal with measuring the exterior noise from a vehicle fixed on a chassis dynamometer in a large hemi-anechoic room. During a standardised acceleration test, the noise is measured with an array of microphones placed in the far-field, and the overall noise level versus vehicle position can be simulated. The indoor facility allows controlled and repeatable measurements independent of weather. For engineering purposes, pass-by contribution analysis can be included in the test leading to information about the pass-by noise contribution from major noise sources. This work presents a novel application of blind source separation to vehicle measurements from an indoor pass-by measurement campaign. In contrast to the classical transfer path approach using point sources for modelling vehicle noise sources and combining an operational measurement with transfer functions, the blind approach does not consider a specific noise source model. It only assumes that the noise is produces by a set of independent noise sources using only a single operational measurement for a given vehicle condition as input. Near-field microphone measurements are blindly separated into independent components and further correlated with the signals measured at the far-field indoor pass-by microphones to get the time-domain contributions. Finally, we apply the indoor simulated pass-by algorithm to produce noise contribution levels as a function of vehicle position. We discuss the specified application of blind source separation to vehicle measurements for different operating conditions from a real indoor pass-by test. Separation of tyre and engine related noise at tyre near-field microphones is verified. Furthermore, the tyre pass-by noise contribution is extracted from the overall vehicle measurement.
Though Blind Source Separation (BSS) techniques have typically been applied within Operational Modal Analysis (OMA) framework, there joint diagonalization property can also be utilized within traditional input-output Experimental Modal Analysis (EMA) for the purpose of modal parameter estimation. This paper explores this possibility and suggests an approach to achieve the same. The paper presents the mathematical concepts that make it possible for Second Order Blind Identification (SOBI), a blind source separation algorithm to be applied to impulse response functions for the purpose of modal parameter estimation. It is also shown in the paper that, in its current form, the approach provides real mode shapes, irrespective of the nature of the system under investigation. In addition to demonstrating the approach by means of studies conducted on an analytical 5 degrees-of-freedom system, the real mode shapes extracted using the suggested approach are also used for model updating purposes.
In last decade, Operational modal analysis has emerged as a technique of choice for identifying dynamic characteristics of large complex structures whose identification, using traditional experimental modal analysis techniques, is otherwise quite challenging. In this regard, several parameter estimation techniques have been suggested, which can broadly be categorized in three categories: 1) those based on traditional EMA algorithms, 2) those based on subspace identification and 3) those based on single degree-of-freedom system techniques. This paper reviews various algorithms used for modal parameter estimation in OMA framework. In the process of reviewing these algorithms, associated data preparation and signal processing aspects are also covered. Overall, emphasis is on understanding common aspects that these seemingly different algorithms share.
In this paper, a modal appropriation methodology is suggested for Operational Modal Analysis. The method is based on performing a numerical convolution of a sine wave force with correlation functions of observed output responses of the structure. Classical modal appropriation based techniques are then used to estimate modal parameters characterizing the dynamics of the structure. The paper explains the theory and steps associated with this method and its performance is illustrated by means of studies conducted on an analytical 15 DOF system. Further, the results are compared with those obtained using standard OMA algorithms such as Stochastic Subspace Identification (SSI) algorithm.
The localization of sound sources with delay-and-sum (DAS) beamforming is limited by a poor spatial resolution—particularly at low frequencies. Various methods based on deconvolution are examined to improve the resolution of the beamforming map, which can be modeled by a convolution of the unknown acoustic source distribution and the beamformer’s response to a point source, i.e., point-spread function. A significant limitation of deconvolution is, however, an additional computational effort compared to beamforming. In this paper, computationally efficient deconvolution algorithms are examined with computer simulations and experimental data. Specifically, the deconvolution problem is solved with a fast gradient projection method called Fast Iterative Shrikage-Thresholding Algorithm (FISTA), and compared with a Fourier-based non-negative least squares algorithm. The results indicate that FISTA tends to provide an improved spatial resolution and is up to 30% faster and more robust to noise. In the spirit of reproducible research, the source code is available online.