Applied Psychoacoustics and Sound Quality
In the area of applied psychoacoustics we see human perception as an integral part of acoustical design problems. We conduct psychoacoustic experiments that allow us to decode the perceivable information incorporated in a sound. This information encompasses different levels of perceptual representation; it starts from:
- descriptions of the sound character, like loud-soft, high-low, sharp-dull, rough-smooth, tonal-noisy, sonorous-soundless, impulsive-not impulsive, etc.
- over to evaluations of the sound, like pleasant-unpleasant, preferred-not preferred, annoying-not annoying
- to the point of sound quality features like active-calm, sincere-untrustworthy, expensive-cheap, colorful-colorless
and includes the criteria underlying the judgments and their context dependency.
In addition to the judgments of participants from listening tests, also physiological data (heart rate, skin conductance, pupillometry, etc.) is recorded as another form of human response to the presented stimuli.
Besides pure acoustic perception the group is also working in the field of multi-modal perception with vibro-acoustic (vehicle acoustics, singing bowls) and audio-visual stimuli (spatial perception) - factors which can also be relevant in sound design.
We develop algorithms that are able to predict the human description of sounds (e.g. roughness, tonality) which are inspired by human auditory perception. One goal is to optimize the algorithms in terms of their robustness especially for complex acoustic scenarios like environmental and vehicle noise.
The combination of judgments from psychoacoustic experiments, algorithms for basic auditory sensations and long lasting experience enables us to support sound engineering solutions that are optimized for the human listener.
(Die Kennzeichnung der akustische Güte von Ventilatoren mit dem guteäquivalenten Pegel – Entwicklung eines psychoakustisch motivierten Berechnungsverfahrens)
In this iGF/BMWi funded project, the perception of ventilator and fan noise is investigated and an algorithm for the prediction of quality equivalent levels is developed.