Interactions with physical objects usually evoke sounds, i.e., auditory feedback that depends on the interacting objects (e.g., table, hand, or pencil) and interaction type (e.g., tapping or scratching). The continuous real-time adaptation of sound during interaction enables the manipulation/refinement of perceived characteristics (size, material) of physical objects. Furthermore, when controlled by unrelated external data, the resulting ambient sonifications can keep users aware of changing data. This article introduces the concept of plausibility to the topic of auditory augmentations of physical interactions, aiming at providing an experimentation platform for investigating surface-based physical interactions, understanding relevant acoustic cues, redefining these via auditory augmentation / blended sonification and particularly to empirically measure the plausibility limits of such auditory augmentations. Besides conceptual contributions along the trade-off between plausibility and usability, a practical experimentation system is introduced, together with a very first qualitative pilot study.
Efficient feedback on energy consumption is regarded as one step towards a more sustainable lifestyle. Sonification is very apt to convey such information continuously in an ambient and effective way. This paper presents a pilot system for sonifying the electric power consumption of an institute’s kitchen. The reverberation of the kitchen is changed depending on the actual consumption and its difference to a weekly baseline. If the actual consumption is low, it is mapped to a plausible kitchen reverberation. If it is high compared to the baseline, the reverberation becomes unnatural. Evaluating the system gave insights on perceptibility and acceptance of auditory augmentation in a semi-home context.
Every day, we rely on the information that is encoded in the auditory feedback of our physical interactions. With the goal to perceptually enhance those sound characteristics that are relevant to us — especially within professional practices such as percussion and auscultation — we introduce the method of real-time Auditory Contrast Enhancement (ACE). It is derived from algorithms for speech enhancement as well as from the remarkable sound processing mechanisms of our ears. ACE is achieved by individual sharpening of spectral and temporal structures contained in a sound while maintaining its natural gestalt. With regard to the targeted real-time applications, the proposed method is designed for low latency. As the discussed examples illustrate, it is able to significantly enhance spectral and temporal contrast.
We introduce Auditory Contrast Enhancement (ACE) as a technique to enhance sounds at hand of a given collection of sound or sonification examples that belong to different classes, such as sounds of machines with and without a certain malfunction, or medical data sonifications for different pathologies/conditions. A frequent use case in inductive data mining is the discovery of patterns in which such groups can be discerned, to guide subsequent paths for modelling and feature extraction. ACE provides researchers with a set of methods to render focussed auditory perspectives that accentuate inter-group differences and in turn also enhance the intra-group similarity, i.e. it warps sounds so that our human built-in metrics for assessing differences between sounds is better aligned to systematic differences between sounds belonging to different classes. We unfold and detail the concept along three different lines: temporal, spectral and spectrotemporal auditory contrast enhancement and we demonstrate their performance at hand of given sound and sonification collections.
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