Electric vehicle sound stimuli data and enhancements

Data for six electric vehicle WOT interior sound measurements and eight enhanced sound signatures are presented. The measurement of electric vehicle interior sound signature data and the enhancement of these stimuli are documented in this data article. The procedures and equipment that were used to record the data, as well as the transposition, harmony and order addition, frequency filtering and modulation enhancement techniques that were applied to these stimuli are explained in detail. The transient frequency content of the 12 sound stimuli is presented in acoustic spectrograms along with the audio files in.mp3 format.


a b s t r a c t
Data for six electric vehicle WOT interior sound measurements and eight enhanced sound signatures are presented. The measurement of electric vehicle interior sound signature data and the enhancement of these stimuli are documented in this data article. The procedures and equipment that were used to record the data, as well as the transposition, harmony and order addition, frequency filtering and modulation enhancement techniques that were applied to these stimuli are explained in detail. The transient frequency content of the 12 sound stimuli is presented in acoustic spectrograms along with the audio files in.mp3 format.
& 2018 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Subject area
Engineering More specific subject area Automotive Acoustics, Psychoacoustics Type of data Channel 2 (Right) -The interior sound pressure measurement at the right ear position in the driver's seat.

Experimental factors
The sound in the interior of the vehicle was acquired using a Squadriga mobile front end, which passes the analog voltage signal from the microphones through a analog high-pass filter, where after it is converted to a digital signal. The sound signatures were further enhanced using the Audacity and GarageBand sound software, where different sound techniques and harmonics were added.

Experimental features
The interior WOT sound signatures of 5 electric vehicles and one hybrid electric vehicle was recorded using a binaural measurement system. Additional enhanced sound signatures were created to diversify the stimuli pool and evaluate the consumer satisfaction.

Data source location
The vehicles were tested in various secluded public asphalt roads in Bavaria, Germany.

Data accessibility
Data are published with this article Related Research Article The data provide researchers with accurate binaural sound recordings of commercial electric vehicles, which can be used for future jury evaluations of electric vehicle psychoacoustics.
Additional enhanced stimuli are presented that were created as potential future sound signatures. The enhanced stimuli provide industry with realistic variations in sound signatures that enrich different components of electric vehicle sound character.

Data
Six vehicle sound signatures were recorded in the interior of standard production electric/hybrid electric vehicles on public secluded asphalt roads. The measurements were opportunistic and vehicles were assessed during test drives from vehicle dealerships. The sound signatures were acquired during WOT acceleration, which involves accelerating the vehicle from rest to a maximum speed of 120 km/h in the shortest time possible (Avg. 18.7 s). WOT acceleration provokes the maximum response of the electric motor and vehicle drive-train, which augments different aspects of the vehicle sound character during the run-up.

Standard production EV stimuli
Six standard production EV/HEV's were evaluated with details as shown in Table 1. The vehicle specifications, driving conditions and locations are, respectively, indicated. The stimuli were recorded under WOT driving conditions (maximum acceleration) from a position of rest to a maximum speed of 120 km/h. The measurements were repeated a minimum of four times in both driving directions depending on the available time and test conditions (traffic, weather, etc). The interior sound stimuli  were recorded in the driver seat of the vehicle using a Squadriga I data acquisition system from Head Acoustics [5] and a BHS I binaural headset as shown in Fig. 1, using a sample rate of 44.1 kHz. The recorded runs were evaluated in the Head Acoustics Artemis Suite 5 using the FFT versus time and Level versus time analyses to assess the quality of the measurement based on external noise influences such as pass-byes and environmental noise. The best measurement was selected for each vehicle and exported to a sound file (.mp3) for jury evaluations [4].

Enhanced EV stimuli
Several concepts were explored to add character and variation to the acoustic vehicle recordings. In the process a further six enhanced stimuli were created in order to evaluate their potential to evoke  known sound quality attributes. The full description of enhanced stimuli data is provided in Table 2. Several software packages were used to generate the enhanced stimuli. Matlab was used to generate new or additive stimuli, such as side bands or harmonics. GarageBand was used to enhance the quality of the stimuli by adding reverberation effects and adjusting the sound equalization. Audacity was used to add pink noise, trim the stimuli to the desired length and amplify the stimuli to a suitable   dB level. All the enhanced stimuli were amplified to fall within a 3 dB(A) absolute range (RMS) of the original vehicle stimuli in order to simulate real EV sound signatures that are level accurate. The comparison of the original and correctly amplified enhanced stimuli can be seen in Fig. 2. Sound stimuli B and D were enhanced from the original BWM i3 stimulus (Sound A). The harmony and order additions of these two stimuli were generated in Matlab using the chirp function, sweeping   from 20 Hz to the respective frequencies within the 10 s stimulus period. The addition of these enhancements can be seen in Figs. 4 and 6 respectively.
Sound stimulus E was generated in Matlab by isolating several lower electric motor orders and enhancing the stimuli with reverberation in the GarageBand software before finalizing the length and level of the stimuli in Audacity.   Sound J was created in Matlab using a Shepard's Risset Glissando with a fundamental frequency of 110 Hz, sample frequency of 44.1 kHz and a cycle time of 2 s [1]. The Shepard's tone is a set of frequency sweeps that increase linearly with time and that are specifically spaced to create an auditory illusion of a continually increasing sound [2]. These linear relationships can clearly be seen in the FFT vs time analysis of the stimuli shown in Fig. 11.   Sound K was developed in an attempt to make the enhanced sound E more realistic by adding simulated road and tyre noise. Sound l was developed to improve the warning characteristics of the base stimulus (Sound A), through the addition of frequency modulated pink noise, which improves the localization and differentiation of similar sounding stimuli [3].

Original and enhanced stimuli
In order to analyze and visualize the differences between the sound stimuli with respect to the spectral and temporal content, spectrograms (FFT versus time) of the stimuli were created and are shown in Figs. 3-14. These figures display the spectral content (ordinate) of the respective original and enhanced stimuli with respect to time (abscissa). Several sound phenomenon and enhancements are emphasized to provide an in depth explanation to the reader. The spectrograms have been widely used to illustrate sound phenomenon and attributes in vehicles sound signatures [6,7] and [8].

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at https://doi.org/ 10.1016/j.dib.2018.10.074.