Acoustic Mapping of Trucks
Reducing vehicle noise is becoming increasingly important in freight transport. Statutory limit values, rising noise protection requirements and the use of lorries in sensitive areas such as residential areas or logistics centres necessitate a precise analysis of noise emissions.
A thorough acoustic investigation using the acoustic camera provides the basis for assessing the noise behaviour of lorries under realistic operating conditions. The measurement data obtained serves as a reliable basis for approval procedures, noise abatement schemes and compliance with statutory noise limits.
Our high-performance Acoustic Camera is used for the metrological analysis of noise from passing lorries. It detects and localises sound sources as the lorry passes by, enabling a detailed acoustic analysis under real-world operating conditions. This allows noise-relevant components such as the engine, tyres, drivetrain or attachments to be specifically identified and assessed.
Localisation of sound sources from passing lorries using beamforming
Heavy-duty truck with trailer
Two Acoustic Cameras were used for the measurements: the Star48 and the Fibonacci AC Pro. They were installed one after the other on the pavement, 10 metres from truck to array. The road surface was in good condition along the measurement section. The noise emission measurements were carried out on a busy public road.
All arrays can be mounted flexibly. The Star48 array can be set up and unfolded in an open field in a matter of seconds.
Following the measurement, the software first combines the video signal to create a visual panoramic image, whilst determining the speed profile. In the second step, the time-synchronised audio data is used to calculate an acoustic map of the entire vehicle. With one click and within a few minutes only, sound sources or specific frequency ranges that contribute significantly to the overall noise can be precisely localised.
However, the vehicle’s movement affects how we perceive its sound. This results in an apparent change in frequency. This phenomenon is known as the Doppler effect or Doppler shift, and is corrected by our PassBy 2D module using a customised beamforming algorithm.
Star 48 microphone array
Fibonacci AC Pro Microphone Array
gt4 data recorder
NoiseImage software
The Acoustic Camera measurements identified the main vehicle noise sources as tire–road rolling noise and the powertrain. Rolling noise is generated by vibrations of the tire tread and carcass caused by the interaction between the tire and the road surface. Powertrain noise originates from the engine and drivetrain components. The measurements show that rolling noise becomes the dominant source at higher vehicle speeds, while powertrain noise is more significant at lower speeds and during acceleration.
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In this video, acoustic measurements were performed on an ICE high-speed train. When measuring moving objects, the sound sources are located at different points on the acoustic map at any given time. In addition, the so-called Doppler effect changes the spectrum and the time course of the amplitude of the radiated signal at the receiver. Conventional beamforming methods therefore lead to diffuse acoustic maps.
The NoiseImage software module PassBy was specially developed for moving measurement objects. It takes motion into account and eliminates the Doppler effect. Thus, exact acoustic maps can be calculated even for fast moving objects.
The delay-and-sum beamforming technique used in the PassBy module is widely used to analyze and localize sound sources, including those generated by fast-moving objects. When applied to fast-moving objects such as trains, the delay-and-sum beamforming algorithm adjusts the time delay and amplitude of the signals received by each microphone in the array. Dynamic sound sources can thus be effectively tracked and localized.
In the case of fast-moving objects such as a passing train, the sound signals picked up by the microphones exhibit frequency changes due to the Doppler effect. The Doppler effect occurs when the source of a sound signal passes a fixed position of the observer and the measurement tool, causing a change in the perceived frequency.
To account for this, the delay-and-sum beamforming algorithm takes into account the speed and direction of the moving object, such as a train, and adjusts the time delay and amplitude of the microphone signals accordingly. By taking into account the Doppler effect and compensating for the frequency shifts caused by the moving train, the algorithm can provide accurate measurements of the actual sound characteristics of the passing train.
