CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority under 35 U.S.C. § 119 of European Patent application no. 18194358.0, filed on 13 Sep. 2018, the contents of which are incorporated by reference herein.

BACKGROUND

The present disclosure relates to an apparatus for processing a signal, such as an analog signal, for input to a neural network. It also relates to a neural network processing device including the apparatus for processing the analog signal and a neural network. Associated methods and computer programs are also disclosed.

SUMMARY

According to a first aspect of the present disclosure there is provided an apparatus for processing a signal for input to a neural network, the apparatus configured to:receive a signal comprising a plurality of samples of an analog signal over time;determine at least one frame comprising a group of consecutive samples of the signal, wherein the or each frame includes a first number of samples;for each frame, determine a set of correlation values comprising a second number of correlation values, the second number less than the first number, each correlation value of the set of correlation values based on an autocorrelation of the frame at a plurality of different time lags;provide an output based on the set of correlation values corresponding to the or each of the frames for a neural network for one or more of classification of the analog signal by the neural network and training the neural network based on a predetermined classification.

In one or more embodiments, for each frame, the apparatus is configured to normalize each of the correlation values of the set of correlation values based on an autocorrelation of the frame at a zero time lag.

In one or more embodiments, the apparatus is configured to determine a plurality of frames, each frame comprising a group of consecutive samples of the signal wherein one of:at least some of the consecutive frames comprise groups of consecutive samples that have a partial overlap with each other; andat least some of the consecutive frames comprise consecutive groups of consecutive samples that are non-overlapping.

In one or more embodiments, a maximum time lag of the plurality of different time lags is based on a sampling frequency used to obtain the plurality of samples of the analog signal.

In one or more embodiments, the apparatus is configured to determine a plurality of frames and determine one or more further correlation values based on a cross-correlation between frames of the plurality of frames, the output including said further correlation values and the sets of correlation values.

In one or more embodiments, the apparatus is further configured to generate an image, comprising a plurality of pixels, based on the one or more sets of correlation values, wherein the correlation values determine one or more of a pixel colour and pixel brightness and a pixel position in the image is based on a position of the correlation value in the set of correlation values and a position of the set in the plurality of sets; andwherein said output, provided for the neural network, comprises said image.

In one or more embodiments, said one or more sets of correlation values comprise time-domain correlation values.

In one or more examples, wherein the apparatus is configured to determine a plurality of frames and wherein each frame is represented as a column vector of the consecutive samples, and wherein the apparatus is configured to form a matrix having columns formed of the plurality of column vectors organized such that the samples thereof comprise consecutive groups of samples.

In one or more embodiments, the apparatus is configured to determine a plurality of frames and the apparatus is configured to represent the set of correlation values of each frame as a column vector wherein the set of correlation values have a predetermined order in the column vector and wherein the apparatus is configured to generate a correlation value matrix having columns formed of the column vectors, and wherein the column vectors of correlation values are arranged in an order corresponding to a time order of the frames used to generate the sets, in terms of the samples the frames contain.

In one or more embodiments, the apparatus is configured to generate an image from said correlation value matrix.

In one or more embodiments, at least one of:the apparatus comprises a speech processing apparatus and the signal comprises a signal representative of speech; andthe apparatus comprises a gesture processing apparatus and the signal comprises a signal representative of movement of at least part of a body of a user.

According to a second aspect of the present disclosure there is provided a neural network processing device comprising a neural network configured to receive, as input, the output of the apparatus of the first aspect.

In one or more embodiments, the neural network comprises a convolutional neural network and the output of the apparatus comprises an image, comprising a plurality of pixels, based on the one or more sets of correlation values, wherein the correlation values determine one or more of a pixel colour and pixel brightness and a pixel position in the image is based on a position of the correlation value in the set of correlation values and a position of the set in the plurality of sets when the sets are arranged in an order corresponding to a time order of the frames used to generate the sets, in terms of the samples the frames contain.

In one or more embodiments, the neural network comprises a neural network having less than four layers.

According to a third aspect of the present disclosure there is provided a method for processing a signal for input to a neural network, the method comprising:receiving a signal comprising a plurality of samples of an analog signal over time;determining at least one frame comprising a group of consecutive samples of the signal, wherein the or each frame includes a first number of samples;for each frame, determining a set of correlation values comprising a second number of correlation values, the second number less than the first number, each correlation value of the set of correlation values based on an autocorrelation of the frame at a plurality of different time lags; andproviding an output based on the set of correlation values corresponding to the or each of the frames for a neural network for one or more of classification of the analog signal by the neural network and training the neural network based on a predetermined classification.

According to a fourth aspect of the present disclosure there is provided computer program or computer readable medium comprising computer program code configured to, when executed on a processor having said computer program code stored in a memory associated with the processor provides for the performance of the method of the third aspect.

While the disclosure is amenable to various modifications and alternative forms, specifics thereof have been shown by way of example in the drawings and will be described in detail. It should be understood, however, that other embodiments, beyond the particular embodiments described, are possible as well. All modifications, equivalents, and alternative embodiments falling within the spirit and scope of the appended claims are covered as well.

The above discussion is not intended to represent every example embodiment or every implementation within the scope of the current or future Claim sets. The figures and Detailed Description that follow also exemplify various example embodiments. Various example embodiments may be more completely understood in consideration of the following Detailed Description in connection with the accompanying Drawings.

