## 1 Introduction
In another study by [PERSON] [7], a similar approach was also applied to vehicle-door-closing sounds. In addition, two experiments on customer product selection based on acoustic performance were conducted in the same publication, relating to vacuum cleaners and hair dryers. All these experiments show that a design addressing product sound sufficiently increases the willingness to buy that product. In another study, [PERSON] et al. [8] focused on the annoyance estimations of vacuum cleaners and derived a prediction model for annoyance. One example of vacuum cleaner noise was recorded, and some frequency ranges were classified in terms of their importance. By increasing and decreasing the levels of these defined frequencies and using the orthogonal array technique, they designed listening tests with the aim of developing an annoyance index for vacuum cleaners. In addition to annoyance, the effects of defined frequency bands on the \"performance\", \"loudness\" and \"sharpness\" of the vacuum cleaners were also investigated in the listening tests. The study concluded with an artificial neural network model that was developed for the prediction of vacuum cleaner annoyance. [PERSON] [9] also used vacuum cleaners as an example in his work to explain the main stages of product sound quality analyses. Different components of vacuum cleaner noise were modified, and a listening test was created using a central composite design, so that a smaller number of stimuli could be used, rather than a full factorial design. The sounds of vacuum cleaner components (motor sound, suction fan noise, airflow noise and rotating brush noise) were changed, and it was found that the acceptability function of vacuum cleaner noise was dependent mainly on the airflow noise and motor noise components. It was explainedthat an equal reduction of 5 dB in both noise sources was required to obtain an optimally acceptable design. In their study, [PERSON] et al. [10] presented a comparison of the acoustic parameters of a vacuum cleaner on different surfaces. They performed various measurements of vacuum cleaners in different arrangements, taking into account that vacuum cleaners can be classified as devices with extensive sound sources. They found that the noise emitted by vacuum cleaners depends on the type of surface used and the arrangements of the device (canister and suction nozzle). They also concluded that it is sufficient to parameterize the acoustic performance of the device with single values, where it would be more feasible for the well-being of the end user to report the most unfavorable working conditions. In addition to perceptual studies, other studies have focused on understanding the noise generation mechanisms of vacuum cleaners. A detailed acoustic characterization of a wet-type vacuum cleaner was conducted in the publication by [PERSON] et al. [11]. They explained that the total emitted noise is the sum of several contributions, such as aerodynamic noise, and mechanical and electromagnetic components. The mechanical and electromagnetic components generate rotational discrete tonal noise and the aerodynamic noise generates broadband noise. In a series of three publications [12; 13; 14], [PERSON] and [PERSON] explained the noise generation mechanisms of a vacuum cleaner in detail. These highly detailed publications showed the complexity of the generated noise and its components. The first publication provided an overview of the noise components that can be found in vacuum cleaner noise and how the mechanical and electromagnetic portions create tonal and broadband noise characteristics. Moreover, the consideration of the performance and noise characteristics at the same time offered insight into the inconsistency between the desired suction power and the noise level. The second publication of this series focused on the aerodynamic portion of the noise and the effect of flower geometry on different flow rates. A conclusion was made that vaned diffusers have more disadvantages than advantages and need to be omitted to reduce noise.