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In a vehicle's development process, Transfer Path Analysis (TPA) is commonly used for identifying sound sources and their transmission to a receiver. Forces acting on the structure are the reason for the structure-borne sound share of the vehicle interior noise. In practice it is not possible, or too extensive, to measure operational forces directly. Instead, they are often calculated indirectly from accelerations and from additionally measured inertances. As the car body is a strongly coupled system, a force acting at one position results in accelerations throughout the structure. This crosstalk must be considered by using a dense inertance matrix consisting of the ratios between each force excitation and the accelerations at every sensor position. Then a matrix inversion is performed to solve the system of equations describing the coupling of the structure.

Since NVH is always a property of the whole system, one must have a deep understanding of the dependencies and all the components that interact. The well known in-situ Transfer Path Analysis (TPA) provides methods to separate different components of an acoustical system such as source and receiver. The source including excitation and structural dynamics of the exciting subsystem can be described independently of the structural dynamics of the receiving structure by means of the in-situ blocked forces. The Experimental Modal Analysis (EMA) is a common method as well and aims to identify the structural dynamics of a structure. This paper addresses the combination of both methods using the example of an e-drive of an electric car, which has been analyzed on a test rig. The combination of modal analysis and TPA yields a better understanding of the system and its dependencies.

For many years in vehicle and other product noise assessments, tonality measurement procedures such as the Tone-to-Noise Ratio, Prominence Ratio and DIN 45681 Tonality have been available to quantify the audibility of prominent tones. Especially through the recent past as product sound pressure levels have become lower, disagreements between perceptions and measurements have increased across a wide range of product categories including automotive, Information Technology and residential products. One factor is that tonality perceptions are caused by spectrally-elevated noise bands of various widths and slopes as well as by pure tones, and usually escape measure in extant tools. Near-superpositions of discrete tones and elevated narrow noise bands are increasingly found in low-level technical sounds. Existing pure-tone methodologies tend to misrecognize an elevated noise band as general masking lowering the audibility of a tone in the measured vicinity, whereas perceptually they add.

Microphone arrays used in vehicle acoustics are mainly designed for fast setup and basic evaluation (e.g. using delay-and-sum beamforming) resulting in a restriction to free field environments. Applications in vehicle interiors require advanced source localization techniques taking into account the reflections at the different panels appearing as mirror sources. Coherence filtering techniques allow for the detection of these mirror sources. An additional sensor is placed as a reference close to the main source. This reference signal is used to filter the array signals increasing the overall dynamic range of the acoustic source mapping. The discrimination of the original source and the reflections is obtained by manipulating the impulse responses between the reference signal and all microphone signals.

The powertrain noise of cars has been reduced in the last decades. Therefore in many cases, rolling tires have increasingly become the dominant sources of vehicles' interior noise. For sound design or a reduction of tire-road noise it is important to know the individual noise shares of the tires and their transfer paths. Authentic tire-road noise can only be measured on a real road, not on a roller dynamometer. So far measurements have been performed during a coast-down on the road with the engine switched off, avoiding the influence of engine noise. Operational Transfer Path Analysis (OTPA) can be used to remove the uncorrelated wind noise, and to synthesize structure-borne and airborne tire-road noise based on input signals measured with microphones at the tires and a triaxial accelerometer at each wheel carrier. Simultaneously, the interior noise is recorded by an artificial head.

Interior vehicle sound is an important factor for customer satisfaction. To achieve an optimized product sound at an early stage of development, subjective evaluation methods as well as analysis and prediction tools must be combined to provide reliable information relevant to product quality and comfort judgments. Binaural Transfer Path Synthesis (BTPS) is a well-known method to calculate interior noise and vibrations based on multi-channel input measurements. Recent enhancements of the BTPS method enable taking into account also simulated excitations, for example engine mount vibrations calculated using MBS and/or FEM simulations, allowing the prediction of interior noise even if the engine is not available in hardware. Interactive evaluation of the generated sounds in a vibro-acoustic driving simulator helps to increase understanding of customer responses and perception of target sounds.

Vehicle interior noise consists of a superposition of broadband contributions from powertrain, wind, and tire-road noise. Tire-road noise has become increasingly important referring to overall acoustic comfort, especially for (luxury) sedans with pleasant low-noise engine sounds. An interior noise recording during a coast-down (engine switched off) contains different components: a mixture of wind along with airborne and structure-borne tire-road noise shares. Separating the mixture into these components requires appropriate algorithms and additional measurements. Therefore, structure-borne excitation signals as well as the airborne noise radiation of all four tires are measured simultaneously to an artificial head recording in the vehicle interior during a coast-down test from maximum vehicle speed to standstill.

Many applications in acoustics, such as transfer path analysis and synthesis (the well-known tools for troubleshooting and sound design of vehicle interior or exterior noise), require the measurement of transfer functions. Several methods are available to determine the transfer functions between identified sources and selected receiver locations. For example, transfer functions can be obtained by means of direct or reciprocal measurements. Due to errors and restrictive constraints during the measurements, the results of the two methods differ. The quality of measured transfer functions must be evaluated with respect to the auralization of the synthesized receiver signals or even the auralization of individual noise shares caused by a specific source and transmitted via one or a combination of paths. This paper compares the different measurement techniques of transfer functions in theory and in practice.

Besides powertrain and aerodynamic noise, tire-road noise is an important aspect of the acoustic comfort inside a vehicle. For the subjective evaluation of different tires or vehicles in a benchmark, authentic sound examples are essential. They should be recorded on a real road rather than on a roller dynamometer (avoiding artificial and periodic sounds, especially in the case of a small roller circumference and a smooth surface). The challenge of on-road measurements is the need for separating the components of the interior noise generated by rolling tires, aerodynamic flow and powertrain. This allows for individual judgment of the noise shares. A common approach for eliminating the engine sound is shutting the engine off after acceleration to the desired maximum speed. Operational Transfer Path Analysis (OTPA) can then be used to auralize the tire-road noise at a certain receiver location, where an artificial head records the interior noise during this coast-down.

Pass-by measurements on a test track are a standard test procedure for every new vehicle. Since there are only a few test tracks and the measurements are depending on the environmental conditions two indoor test procedures have been developed using a chassis dynamometer in a semi anechoic chamber. The first procedure delivers the standard pass-by analyses as well as monaural and binaural time signals using a far field array measurement. The second procedure delivers more detailed information about the different noise sources at the vehicle. Near field measurements of the main noise sources of the vehicle are combined with the airborne transfer functions between these sources and a far field observer position to get a simulated far field microphone signal of the whole vehicle or any set of components

Vehicle interior noise can be regarded as the sum of all panel contributions which enclose the compartment. In order to experimentally investigate the sound contributions of individual panels, the so-called “window method” is often used. Due to some fundamental drawbacks, a new method has been invented which is considered a useful alternative. The theoretical background of the method is covered in this paper, as well as application examples illustrating the performance and advantages of this new technique.

E-vehicles can generate strong tonal components that may disturb people inside the vehicle. However, such components, deliberately generated, may be necessary to meet audibility standards that ensure the safety of pedestrians outside the vehicle. A tradeoff must be made between pedestrian audibility and internal sound quality, but any iteration that requires additional measurements is costly. One solution to this problem is to modify the recorded signals to find the variant with the best sound quality that complies with regulations. This is only possible if there is a good separation of the tonal components of the signal. In this work, a method is proposed that uses the High-resolution Spectral Analysis (HSA) to extract the tonal components of the signal, which can then be recombined to optimize any sound quality metric, such as the tonality using the Sottek Hearing Model (standardized in ECMA 418-2).

To empirically estimate the radiation of sound sources, a measurement with microphone arrays is required. These are used to solve an inverse problem that provides the radiation characteristics of the source. The resolution of this estimation is a function of the number of microphones used and their position due to spatial aliasing. To improve the radiation resolution for the same number of microphones compared to standard methods (Ridge and Lasso), a method based on normalizing flows is proposed that uses neural networks to learn empirical priors from the radiation data. The method then uses these learned priors to regularize the inverse source identification problem. The effects of different microphone arrays on the accuracy of the method is simulated in order to verify how much additional resolution can be obtained with the additional prior information.

In the engine development process, the ability to judge NVH comfort as early as possible is a great benefit. The prediction of engine noise on the basis of a prototype engine without the need to install it in a real car significantly speeds up the development process and leads to a cost reduction, as prototype modifications can be evaluated faster. Meaningful predictions of the perceived NVH comfort cannot be achieved just by comparing order levels, but require listening to an auralization of the engine noise at the driver's position. With the methods of Transfer Path Analysis and Synthesis (TPA/TPS) a prototype engine can be virtually installed in a car using test-bench data. The interior noise can be estimated by combining source signals containing near-field airborne noise radiation and mount forces with transfer functions describing the transmission to the target position in the cabin.

Quantifying tonalities in technical sounds according to human perception is a task of growing importance. The psychoacoustic tonality method, published in the 15th edition of the ECMA-74 standard, is a new method that is capable of calculating the perceived tonality of a signal. Other methods, such as Prominence Ratio or Tone-to-Noise Ratio do not consider several essential psychoacoustic effects. The psychoacoustic tonality is based on a model of human hearing and thus is able to model human perception better than other methods. The algorithm described in ECMA-74 calculates tonality over time and frequency. In practice, tonalities often originate from rotating components, for example, parts of an electric motor. In these cases, quantification of the tonality of orders is often more interesting than the tonality over frequency. In this paper, an extension of the psychoacoustic tonality according to ECMA-74 is presented.

Clearly, sound quality evaluation has become a central focus for assuring customer satisfaction. To achieve an optimized product sound at an early stage of development, subjective evaluation methods must be combined with analysis and prediction tools to provide reliable information relevant to product quality judgments. Some years ago, a “Hearing Model” was developed explaining and describing many psychoacoustic effects [1], [2], and allowing for roughness calculation in accordance with subjective listening tests [3]. Existing roughness models work well for synthetic signals such as modulated tones or noise signals, but it is challenging to predict roughness for engine sounds because of their more complex spectral and temporal noise patterns [4].