Abstract: A computer-implemented method for restoring a wrapped audio signal comprising a plurality of digitised signal samples at respective sample times, the method comprising: estimating a sequence of corrections comprising a sequence of numerical values the estimating comprising, for each signal sample: applying, at the sample time, a numerical filter to each of a set of potential corrections to determine a filtered value associated with each set of potential integer corrections wherein the filter enhances the filtered value at sample times when a change in a degree of wrapping occurs relative to sample times when a change in degree of wrapping does not occur; determining a cumulative objective over a plurality of signal samples by accumulating objective values and determining a sequence by selecting for each sample time a correction from the set of potential corrections wherein the correction for each sample time are selected to optimise the cumulative objective.

Abstract: We describe a method of blind source separation for use, for example, in a listening or hearing aid. The method processes input data from multiple microphones each receiving a mixed signal from multiple audio sources, performing independent component analysis (ICA) on the data in the time-frequency domain based on an estimation of a spectrogram of each acoustic source. The spectrograms of the sources are determined from non-negative matrix factorization (NMF) models of each source, the NMF model representing time-frequency variations in the output of an acoustic source in the time-frequency domain. The NMF and ICA models are jointly optimized, thus automatically resolving an inter-frequency permutation ambiguity.

Abstract: We describe techniques for restoring an audio signal. In embodiments these employ masked positive semi-definite tensor factorization to process the signal in the time-frequency domain. Broadly speaking the methods estimate latent variables which factorize a tensor representation of the (unknown) variance/covariance of an input audio signal, using a mask so that the audio signal is separated into desired and undesired audio source components. In embodiments a masked positive semi-definite tensor factorization of ?ftk=MftkUfkVtk is performed, where M defines the mask and U, V the latent variables. A restored audio signal is then constructed by modifying the input signal to better match the variance/covariance of the desired components.