Signal processing systems

摘要

This invention relates to methods, apparatus, and computer program code for processing acoustic signal data to determine where an object has been tapped with a stylus, finger nail or the like. The method involved storing a set of labelled training data comprising digitized waveforms from a sensor for taps at a plurality of different locations. The labelled training data is then processed to determine mean value and covariance data for the waveforms, which is afterwards used in conjunction with a digitized waveform of a tap at an unknown location to identify the location of the tap. Preferably the covariance is decomposed into a plurality of basis functions for each region each with a respective weighting, which are used to represent captured data for an unknown tap and parameters of the representation are classified to locate the tap.

主权项

1. A method of detecting a tap on an object having at least one acoustic/vibration sensor, including a method of determining a threshold for declaring a detected tap, the method comprising:
capturing tap data comprising a digitised waveform of a tap on said object captured by said at least one acoustic/vibration sensor; processing said captured tap data by applying a probabilistic tap detection procedure to generate time series tap metric data comprising data providing a detection metric of a potential said tap at a succession of times, wherein said time series tap metric data comprises a time series of tap detection probabilities; determining a threshold for a classifier wherein said threshold comprises a probability threshold to apply to said tap detection probabilities from said probabilistic tap detection procedure to declare a detected tap; and applying said classifier to said time series of tap detection probabilities data to declare a detected said tap on said object; and wherein said determining of said probability threshold for said classifier comprises: identifying a region of background noise without taps; adding to this background noise one or more examples of stored clean taps at one or more time locations in said background noise to generate synthetic training data; applying said probabilistic tap detection procedure to said generated synthetic training data comprising said one or more examples of stored clean taps in said background noise without taps, to generate training probability data; and using said training probability data to determine said threshold for said classifier.