Sound quality assessments are an integral part of vehicle design. Especially now,
as manufacturers move towards electrification, vehicle sounds are fundamentally
changing. By improving the quality of the interior sounds of a vehicle,
consumers’ subjective evaluation of it can be increased. Therefore, the field of
psychoacoustics, which is the study of human perception of sound, is broadly
applicable here. In fact, the perceived quality of a sound signal is influenced
by several psychoacoustic indicators, including loudness, sharpness, and
roughness. Of particular utility is identifying in advance how to distribute
audible frequency content in a way that optimizes psychoacoustic metrics as this
can help automotive engineers obtain specific design targets that optimize
vehicle noise, vibration, and harshness (NVH).
In this article, a novel modified gradient-based optimization technique (MGOT) is
developed to optimize psychoacoustic loudness and sharpness. The new technique
is applied to identify targeted adjustments to a measured vehicle interior sound
signal that keep the signal energy constant but reduce loudness and/or
sharpness. The MGOT numerically approximates the objective function gradient for
small changes in the signal power distribution for which constant overall signal
power is maintained. These gradient calculations identify power spectrum
one-third octave band trades that minimize a sound signal metric that is a
weighted sum of loudness and sharpness while conserving the total signal power.
A trade consists of a reduction of power content from a one-third octave band
designated as a source together with a simultaneous addition of that power to
another receiver one-third octave band. In the MGOT, a one-third octave band
that is at any time identified as a source can never later become a receiver of
power. The MGOT results and execution times are compared with two widely
available general-purpose optimization routines (a standard gradient-based
optimizer and a “genetic,” non-gradient optimizer) are used to achieve identical
optimization objectives. In comparison to existing optimization techniques, MGOT
is found to identify spectrum modifications that produce a superior minimization
of the objective function for comparable or even reduced execution times. The
resultant sound spectrum modifications can guide vehicle structural or
calibration design recommendations that realize a preferred frequency
distribution for enhancing the vehicle driving experience.
