Abstract: A method for generating a sound output is based on an interaction with a data set. The data set comprises a plurality of data points. Each data point stores one or more data features. An interaction is obtained with at least a part of the data points and a sound model is used to generate a sound output based on the interaction. The sound model maps at least one of the one or more data features to one or more acoustic properties of the sound output as a function of the interaction. The data features can be one of a spatial feature, a time feature, a physical property, and a data label. The acoustic properties can be one of pitch, pulsing frequency, duty cycle, loudness, and tone colour. The method can be used to validate labels assigned to ground truth input data and to train a machine learning algorithm.

Abstract: A method for generating an acoustic calibration signal for adjusting a tool in a plurality of dimensions relying on acoustic feedback. Each dimension of the plurality of dimensions corresponds to a respective degree of freedom of the tool. The method comprises generating the calibration acoustic signal having an acoustic property for each dimension of the plurality of dimensions, wherein each of the acoustic properties varies towards a corresponding predetermined value when the tool is being adjusted in the corresponding dimension towards a corresponding predetermined target adjustment. A related method and a related alignment system for adjusting a tool in a plurality of dimensions.