arrow_back_ios

Main Menu

See All Software See All Instruments See All Transducers See All Vibration Testing Equipment See All Electroacoustics See All Acoustic End-of-Line Test Systems See All Academy See All Resource Center See All Applications See All Industries See All Services See All Support See All Our Business See All Our History See All Global Presence
arrow_back_ios

Main Menu

See All nCode - Durability and Fatigue Analysis See All ReliaSoft - Reliability Analysis and Management See All Test Data Management See All DAQ Software See All Drivers & API See All Utility See All Vibration Control See All High Precision and Calibration Systems See All DAQ Systems See All S&V Hand-held Devices See All Industrial Electronics See All Power Analyzer See All S&V Signal Conditioner See All Acoustic See All Current and Voltage Sensors See All Displacement See All Force sensors See All Load Cells See All Pressure See All Strain Gauges See All Temperature Sensors See All Torque Sensors See All Vibration See All Accessories for Vibration Testing Equipment See All Vibration Controllers See All Measurement Exciters See All Modal Exciters See All Power Amplifiers See All LDS Shaker Systems See All Test Solutions See All Actuators See All Combustion Engines See All Durability See All eDrive See All Production Testing Sensors See All Transmission & Gearboxes See All Turbo Charger See All Training Courses See All Acoustics See All Asset & Process Monitoring See All Custom Sensors See All Data Acquisition & Analysis See All Durability & Fatigue See All Electric Power Testing See All NVH See All Reliability See All Smart Sensors See All Vibration See All Weighing See All Automotive & Ground Transportation See All Calibration See All Installation, Maintenance & Repair See All Support Brüel & Kjær See All Release Notes See All Compliance See All BKSV Worldwide Contacts
arrow_back_ios

Main Menu

See All API See All Microphone Cartridges See All Microphone Sets See All Microphone Pre-amplifiers See All Sound Sources See All Acoustic Calibrators See All Special Microphones See All Accessories for acoustic transducers See All Experimental testing See All Transducer Manufacturing (OEM) See All Piezoelectric Charge Accelerometers See All Piezoelectric CCLD (IEPE) accelerometers See All Electroacoustics See All Noise Source Identification See All Environmental Noise See All Sound Power and Sound Pressure See All Noise Certification See All Industrial Process Control See All Structural Health Monitoring See All Electrical Devices Testing See All Electrical Systems Testing See All Grid Testing See All High-Voltage Testing See All Vibration Testing with Electrodynamic Shakers See All Structural Dynamics See All Machine Analysis and Diagnostics See All Dynamic Weighing See All Vehicle Electrification See All Calibration Services for Transducers See All Calibration Services for Handheld Instruments See All Calibration Services for Instruments & DAQ See All On-Site Calibration See All Resources See All Software License Management
Feet from a man near an electric car's wheel, cross-walking the road to predict the detectability and annoyance of warning sounds using partial loudness

Predicting detectability and annoyance of EV warning sounds using partial loudness

May 26, 2020  AUTOMOTIVE, WHITEPAPERS



Electric vehicles (EVs) are so quiet at low speeds that they can be a danger to pedestrians, cyclists and other road users. So the challenge for car manufacturers is to create sounds that are detectable but not annoying.

 

To avoid them becoming a hazard, proposed legislation requires EVs to emit warning sounds at low speeds. For consistent evaluation, an objective algorithm is needed to predict how quickly subjects can detect the sounds and their perceived annoyance level.

 

Generally, there is agreement that detectability and annoyance are strongly related to loudness and that a loudness model can potentially objectively predict and assess them. However, EVs operate in urban environments, so to account for the masking effect of background noise, a partial loudness model should be used. The model used for this paper was Moore-Glasberg time-varying loudness, which computes loudness following an advanced model of the human ear based on the auditory filter bank concept.

 

Twenty-three people took part in three tests to obtain subjective detection thresholds. For the first test, subjects listened to warning sounds without background noise to familiarize them with the stimuli. The subjective reaction time was measured by asking the subject to press a button as soon as they heard a short noise impulse. The subject was then presented with one of the four warning sounds in the presence of one of five background noises, totalling 20 combinations. 

 

The second test used an adaptive force choice paradigm to reduce the effects of individual bias when detecting warning sounds in background noise. The subject was presented with three consecutive sound samples, all containing the same one-second segment of simulated urban noise, but one also containing a one-second segment of a warning sound.

 

The third test investigated the influence of background noise on perceived annoyance. The warning sounds were evaluated in five noise conditions and the subjects told to imagine themselves in an urban environment, for example, sitting outside a café.

 

The results confirm that the detection thresholds are heavily influenced by subject’s confidence when they provide their responses. The detection thresholds in terms of partial loudness are similar for stationary warning sounds. The perceived annoyance increases with partial loudness as expected, and the model explains the effect of background noise on annoyance perception rather well.