Assessing annoyance in automotive seat adjustments; perception and prediction modeling of subjective annoyance responses using advanced approaches; a comparison of regression methods and neural networks
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the automotive industry, acoustic comfort is a key aspect of perceived quality,
especially in high-end vehicles where even subtle noise such as squeaks, buzzes, or
rattles can negatively impact user satisfaction. Sound Quality (SQ) assessments help
ensure a premium experience but typically rely on subjective jury testing, which is
time-consuming and depends on expert judgment that may not reflect everyday user
expectations.
This thesis aims to develop a predictive model for annoyance ratings based on objective
acoustic parameters. This is a challenging task, as annoyance is inherently
subjective and influenced by various perceptual factors. While traditional methods
such as linear regression can estimate simple perceptual attributes like loudness,
they fall short when modeling more complex, non-linear characteristics. To overcome
these limitations, machine learning approaches, including neural networks and
random forests, are investigated and compared to linear and polynomial regression
models.
The study focuses on seat adjustment mechanisms as a use case. These sounds
are relatively easy to isolate and analyze, show noticeable variation across vehicle
brands, and are less affected by external noise sources. This makes them a suitable
candidate for controlled testing and model development. Using those sounds,
a large scale listening test is made to assess annoyance and be able to train the
models. Results demonstrate that machine learning models can successfully predict
perceived annoyance based on objective metrics, offering a promising alternative to
traditional jury testing. Such models could significantly improve the efficiency and
scalability of SQ evaluations in the automotive industry.
Beskrivning
Ämne/nyckelord
Psychoacoustics, machine learning, jury testing, sound quality, regression, neural networks prediction