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Due to increasingly stringent laws regarding the level of noise emitted by motor vehicles, especially when it comes from trucks, many techniques are used to determine the main source of noise. Levels must meet the standard pass-by noise as provided by the standard ISOR362. This work applies a technique that aims to identify the main source of noise of heavy vehicles during the pass-by noise test, called Pass-by Noise Beamforming. The technique use the method known as Generalized Inverse and an array of microphones optimized for low frequencies. The paper presents the steps of validation of the system using loudspeakers and application in two trucks with distinct contributions (engine and tire noise). The results of the technique showed advantages compared to the conventional method (delay-and-sum algorithm), obtaining better separation of coherent sources with better dynamic range in a wide frequency range (50 Hz - 7 kHz).

The constant Q transform consists of a geometrically spaced filter bank, which is close to the wavelet transform due to the feature of its increasing time resolution for high frequencies. On the other hand, it can be processed using the well-known FFT algorithm. In this sense, this tool is a middle term between Fourier and wavelet analyses, which can be used for stationary and non-stationary signals. Automotive NVH signals can be stationary (e.g., idle, cruise) or non-stationary, i.e., time-varying signals (e.g., door closing/opening, run-up, rundown). The objective of this work is to propose the use of the constant Q transform, developed originally for musical signal processing, for automotive NVH (run up, impact strip and door closing) time-frequency analyses. Also, similarities and differences of the proposed tool when compared with Fourier and wavelet analyses are addressed.

The most important challenge in knock detection is to detect its intensity. Depending on the phenomenon characteristic the spark ignition calibration can be optimized. For this reason, the scope of this paper is the use the Discrete Wavelet Transform (DWT) as a tool to analyze knock signals characteristics in the time-scale decomposition. A brief description of the Short-Time Fourier Transform (STFT) analysis and comparisons between Fourier analysis and DWT are also shown. Time-frequency analysis methods have become more usual in recent years and can be applied in different areas of the automotive field, such as noise, vibration and powertrain calibration. Due to the demand vehicles with better performance, fuel economy and emissions; the signal analysis tools have been important to optimize the system functionality, such as driveability and knocking. The knock is an undesired phenomenon and it is generated by the shock of flame fronts in the combustion chamber.

Automotive stationary noise and vibration signals are normally analyzed using Fourier methods. However, many noise and vibration signals are non-stationary (transient or time-varying). In those situations, the time characteristics of the signals can be lost using standard Fourier methods. Lately, time-frequency (TF) analysis methods have become more popular and are applied in many different areas of NVH (Noise, Vibration, and Harshness) in order to preserve the time-frequency information. The objective of this paper is to present some of the different time-frequency analysis tools, such as the Short Time Fourier transform (spectrogram), the Gabor Transform, the Wavelet transforms (scalograms), and the Wigner-Ville Distribution. Examples of application of these techniques to automotive non-stationary noise and vibration signals are presented.

Virtual Prototyping (VP) is an important method to assess the sound performance of possible designs in earlier stages of development. The common noise simulation with simple level determination can now be combined with subjective assessments that can be particularly interesting for noise content judgment. This paper will revise the literature found in this field that is applicable to the Engine Air Induction System inlet orifice noise and presents an example to illustrate the main advantages and difficulties in the implementation of VP.

Sound Quality is one of the most important factors to achieve a successful design for Engine Air Induction Systems. Vehicle and bench testing, and simulation tools can be used in order to optimize and refine emitted noise. One difficulty on using simulation in advanced development phases is the necessity to interpret the response curves and assess if sound quality is acceptable. One recent and promising area to help simulation interpretation is the sound synthesis of the emitted noise. This paper presents a simple procedure and example with the objective of reproducing the emitted noise, which allows subjective assessments of different tuning concepts. The example, even a simple one, shows the advantage to finally hear what was simulated.