Patent Abstract:
Some embodiments describe methods, programs, and systems for speech encoding. Among other things, a received input signal representing a property of speech is quantized to generate a quantized output signal. Prior to the quantization, a version of the input signal is supplied to a first noise shaping filter having a first set of filter coefficients effective to generate a first filtered signal. Following the quantization, the quantized output signal is supplied to a second noise shaping filter having a second set of filter coefficients, thus generating a second filtered signal. A noise shaping operation is performed to control a frequency spectrum of a noise effect in the quantized output signal caused by the quantization, wherein the noise shaping operation is based on both the first and second filtered signals. Finally, the quantised output signal is transmitted in an encoded signal.

Full Description:
RELATED APPLICATIONS 
       [0001]    This application is a continuation of, and claims priority to, U.S. patent application Ser. No. 14/162,707 filed Jan. 23, 2014, which is a continuation of, and claims priority to, U.S. patent application Ser. No. 13/905,864 filed May 30, 2013, which is a continuation of, and claims priority to, U.S. patent application Ser. No. 12/455,100 filed May 28, 2009. U.S. patent application Ser. No. 12/455,100 additionally claims priority under 35 USC 119 or 365 to Great Britain Application No. 0900143.9 filed Jan. 6, 2009, the disclosure of which is incorporated by reference herein in its entirety. 
     
    
     BACKGROUND 
       [0002]    In speech coding, it is typically necessary to quantize a signal representing some property of the speech. Quantization is the process of converting a continuous range of values into a set of discrete values; or more realistically in the case of a digital system, converting a larger set of approximately-continuous discrete values into a smaller set of more substantially discrete values. The quantized discrete values are typically selected from predetermined representation levels. Types of quantization include scalar quantization, trellis quantization, lattice quantization, vector quantization, algebraic codebook quantization, and others. The quantization has the effect that the quantized version of the signal requires fewer bits per unit time, and therefore takes less signalling overhead to transmit or less storage space to store. 
         [0003]    However, quantization is also a form of distortion of the signal, which may be perceived by an end listener as a kind of noise, sometimes referred to as coding noise. To help alleviate this problem, a noise shaping quantizer may be used to quantize the signal. The idea behind a noise shaping quantizer is to quantize the signal in a manner that weights or biases the noise effect created by the quantization into less noticeable parts of the frequency spectrum, e.g. where the human ear is more tolerant to noise, and/or where the speech energy is high such that the relative effect of the noise is less. That is, noise shaping is a technique to produce a quantized signal with a spectrally shaped coding noise. The coding noise may be defined quantitatively as the difference between input and output signals of the overall quantizing system, i.e. of the whole codec, and this typically has a spectral shape (whereas the quantization error usually refers to the difference between the immediate inputs and outputs of the actual quantization unit, which is typically spectrally flat). 
         [0004]      FIG. 1   a  is a schematic block diagram showing one example of a noise shaping quantizer  11 , which receives an input signal x(n) and produces a quantized output signal y(n). The noise shaping quantizer  11  comprises a quantization unit  13 , a noise shaping filter  15 , an addition stage  17  and a subtraction stage  19 . The subtraction stage  19  calculates an error signal in the form of the coding noise q(n) by taking the difference between the quantized output signal y(n) and the input to the quantization unit  13 , where n is the sample number. The coding noise q(n) is supplied to the noise shaping filter  15  where it is filtered to produce a filtered output. The addition stage  17  then adds this filtered output to the input signal x(n) and supplies the resulting signal to the input of the quantization unit  13 . 
         [0005]    The input, output and error signals are represented in  FIG. 1   a  in the time domain as functions of time x(n), y(n) and q(n) respectively (with time being measured in number of samples n). As will be familiar to a person skilled in the art, the same signals can also be represented in the frequency domain as functions of frequency X(z), Y,(z) and Q(z) respectively (z representing frequency). In that case, the noise shaping filter can be represented by a function F(z) in the frequency domain, such that the quantized output signal can be described in the frequency domain as: 
         [0000]        Y ( z )= X ( z )+(1+ F ( z ))· Q ( z )
 
         [0006]    The quantization error Q(z) typically has a spectrum that is approximately white (i.e. approximately constant energy across its frequency spectrum). Therefore the coding noise has a spectrum approximately proportional to 1+F(z). 
         [0007]    Another example of a noise shaping quantizer  21  is shown schematically in  FIG. 1   b . The noise shaping quantizer  21  comprises a quantization unit  23 , a noise shaping filter  25 , an addition stage  27  and a subtraction stage  29 . Similarly to  FIG. 1   a , an error signal in the form of the coding noise q(n) is supplied to the noise shaping filter  25  where it is filtered to produce a filtered output, and the addition stage  27  then adds this filtered output to the input signal x(n) and supplies the resulting signal to the input of the quantization unit  13 . However, unlike  FIG. 1   a , the subtraction stage  29  of  FIG. 1   b  calculates the error q(n) as the coding noise signal, defined as the difference between the quantized output signal y(n) and the input signal x(n), i.e. the input signal before the filter output is added rather than the immediate input to the quantization unit  23 . In this case, the quantized output signal y(n) can be described in the frequency domain as: 
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             Therefore the coding noise has a spectrum proportional to (1−F(z))−1. 
           
         
       
     
         [0009]    Another example is shown in  FIG. 1   c , which is a schematic block diagram of an analysis-by-synthesis quantizer  31 . Analysis-by-synthesis is a method in speech coding whereby a quantizer codebook is searched to minimize a weighted coding error signal (the codebook defines the possible representation levels for the quantization). This works by trying representing samples of the input signal according to a plurality of different possible representation levels in the codebook, and selecting the levels which produce the least energy in the weighted coding error signal. The weighting is to bias the coding error towards less noticeable parts of the frequency spectrum. 
         [0010]    Referring to  FIG. 1   c , the analysis-by-synthesis quantizer  31  receives an input signal x(n) and produces a quantized output signal y(n). It comprises a controllable quantization unit  33 , a weighting filter  35 , an energy minimization block  37 , and a subtraction stage  39 . The quantization unit  33  generates a plurality of possible versions of a portion of the quantized output signal y(n). For each possible version, the subtraction stage  39  subtracts the quantized output y(n) from the input signal x(n) to produce an error signal, which is supplied to the weighting filter  35 . The weighting filter  35  filters the error signal to produce a weighted error signal, and supplies this filtered output to the energy minimization block  37 . The energy minimization block  37  determines the energy in the weighted error signal for each possible version of the quantized output signal y(n), and selects the version resulting in the least energy in the weighted error signal. 
         [0011]    Thus the weighted coding error signal is computed by filtering the coding error with a weighting filter  35 , which can be represented in the frequency domain by a function W(z). For a well-constructed codebook able to approximate the input signal, the weighted coding noise signal with minimum energy is approximately white. That means that the coding noise signal itself has a noise spectrum shaped proportional the inverse of the weighting filter: W(z)−1. By defining W(z)=1−F(z), and noting that the quantizer in  FIG. 1   c  searches a codebook to minimize the quantization error between quantizer output and input, it is clear that analysis-by-synthesis quantization can be interpreted as noise shaping quantization. 
         [0012]    Once a quantized output signal y(n) is found according to one of the above techniques, indices corresponding to the representation levels selected to represent the samples of the signal are transmitted to the decoder in the encoded signal, such that the quantized signal y(n) can be reconstructed again from those indices in the decoding. In order to efficiently encode these quantization indices, the input to the quantizer is commonly whitened with a prediction filter. 
         [0013]    A prediction filter generates predicted values of samples in a signal based on previous samples. In speech coding, it is possible to do this because of correlations present in speech samples (correlation being a statistical measure of a degree of relationship between groups of data). These correlations could be “long-term” correlations between quasi-periodic portions of the speech signal, or “short-term” correlations on a timescale shorter than such periods. The predicted samples are then subtracted from the actual samples to produce a residual signal. This residual signal, i.e. the difference between the predicted and actual samples, typically has a lower energy than the original speech samples and therefore requires fewer bits to quantize. That is, it is only necessary to quantize the difference between the original and predicted signals. 
         [0014]      FIG. 1   d  shows an example of a noise shaping quantizer  41  where the quantizer input is whitened using linear prediction filter P(z). The predictor operates in closed-loop, meaning that a prediction of the input signal is based on the quantized output signal. The output of the prediction filter is subtracted from the quantizer input and added to the quantizer output to form the quantized output signal. 
         [0015]    Referring to  FIG. 1   d , the noise shaping quantizer  41  comprises a quantization unit  42 , a prediction filter  44 , a noise shaping filter  45 , a first addition stage  46 , a second addition stage  47 , a first subtraction stage  48  and a second subtraction stage  49 . The first subtraction stage  48  calculates the coding error (i.e. coding noise) by taking the difference between the quantized output signal y(n) and the input signal x(n), and supplies the coding noise to the noise shaping filter  45  where it is filtered to generate a filtered output. The quantized output signal y(n) is also supplied to the prediction filter  44  where it is filtered to generate another filtered output. The output of the noise shaping filter  45  is added to the input signal x(n) at the first addition stage  46  and the output of the prediction filter  44  is subtracted from the input signal x(n) at the second subtraction stage  49 . The resulting signal is input to the quantization unit  42 , to generate an output being a quantized version of its input, and also to generate quantization indices i(n) corresponding to the representation levels selected to represent that input in the quantization. The output of the prediction filter  44  is then added back to the output of the quantization unit  42  at the second addition stage  47  to produce the quantized output signal y(n). 
         [0016]    Note that, in the encoder, the quantized output signal y(n) is generated only for feedback to the prediction filter  44  and noise shaping filter  45 : it is the quantization indices i(n) that are transmitted to the decoder in the encoded signal. The decoder will then reconstruct the quantized signal y(n) using those indices i(n). 
         [0017]      FIG. 1   e  shows another example of a noise shaping quantizer  51  where the quantizer input is whitened using a linear prediction filter P(z). The predictor operates in open-loop manner, meaning that a prediction of the input signal is based on the input signal and a prediction of the output is based on the quantized output signal. The output of the input prediction filter is subtracted from the quantizer input and the output of the output prediction filter is added to the quantizer output to form the quantized output signal. 
         [0018]    Referring to  FIG. 1   e , the noise shaping quantizer  51  comprises a quantization unit  52 , a first instance of a prediction filter  54 , a second instance of the same prediction filter  54 ′, a noise shaping filter  55 , a first addition stage  56 , a second addition stage  57 , a first subtraction stage  58  and a second subtraction stage  59 . The quantization unit  52 , noise shaping filter  55 , and first addition and subtraction stages  56  and  58  are arranged to operate similarly to those of  FIG. 1   d . However, in contrast to  FIG. 1   d , the output of the first addition stage  54  is supplied to the first instance of the prediction filter  54  where it is filtered to generate a filtered output, and this output of the first instance of the prediction filter  54  is then subtracted from the output of the first addition stage  56  at the second subtraction stage  59  before the resulting signal is input to the quantization unit  52 . The output of the second instance of the prediction filter  54 ′ is added to the output of the quantization unit  52  at the second addition stage  57  to generate the quantized output signal y(n), and this quantized output signal y(n) is supplied to the second instance of the prediction filter  54 ′ to generate its filtered output. 
       SUMMARY 
       [0019]    According to at least one embodiment, there is provided a method of encoding speech, comprising: receiving an input signal representing a property of speech; quantizing the input signal, thus generating a quantized output signal; prior to said quantization, supplying a version of the input signal to a first noise shaping filter having a first set of filter coefficients, thus generating a first filtered signal based on that version of the input signal and the first set of filter coefficients; following said quantization, supplying a version of the quantized output signal to a second noise shaping filter having a second set of filter coefficients different than said first set, thus generating a second filter signal based on that version of the quantized output signal and the second set of filter coefficients; performing a noise shaping operation to control a frequency spectrum of a noise effect in the quantized output signal caused by said quantization, wherein the noise shaping operation is performed based on both the first and second filtered signals; and transmitting the quantised output signal in an encoded signal. 
         [0020]    In embodiments, the method may further comprise updating at least one of the first and second filter coefficients based on a property of the input signal. Said property may comprise at least one of a signal spectrum and a noise spectrum of the input signal. Said updating may be performed at regular time intervals. 
         [0021]    The method may further comprise multiplying the input signal by an adjustment gain prior to said quantization, in order to compensate for a difference between said input signal and a signal decoded from said quantized signal that would otherwise be caused by the difference between the first and second noise shaping filters. 
         [0022]    Said noise shaping operation may comprise, prior to said quantization, subtracting the first filtered signal from the input signal and adding the second filtered signal to the input signal. 
         [0023]    The first noise shaping filter may be an analysis filter and the second noise shaping filter may be a synthesis filter. 
         [0024]    Said noise shaping operation may comprise generating a plurality of possible quantized output signals and selecting that having least energy in a weighted error relative to the input signal. 
         [0025]    Said noise shaping filters may comprise weighting filters of an analysis-by-synthesis quantizer. 
         [0026]    The method may comprise subtracting the output of a prediction filter from the input signal prior to said quantization, and adding the output of a prediction filter to the quantized output signal following said quantization. 
         [0027]    According to one or more embodiments, there is provided an encoder for encoding speech, the encoder comprising: an input arranged to receive an input signal representing a property of speech; a quantization unit operatively coupled to said input configured to quantize the input signal, thus generating a quantized output signal; a first noise shaping filter having a first set of filter coefficients and being operatively coupled to said input, arranged to receive a version of the input signal prior to said quantization, and configured to generate a first filtered signal based on that version of the input signal and the first set of filter coefficients; a second noise shaping filter having a second set of filter coefficients different from the first set and being operatively coupled to an output of said quantization unit, arranged to receive a version of the quantized output signal following said quantization, and configured to generate a second filter signal based on that version of the quantized output signal and the second set of filter coefficients; a noise shaping element operatively coupled to the first and second noise shaping filters, and configured to perform a noise shaping operation to control a frequency spectrum of a noise effect in the quantized output signal caused by said quantization, wherein the noise shaping element is further configured to perform the noise shaping operation based on both the first and second filtered signals; and an output arranged to transmit the quantised output signal in an encoded signal. 
         [0028]    According to one or more embodiments, there is provided a computer program product for encoding speech, the program comprising code configured so as when executed on a processor to: 
         [0029]    receive an input signal representing a property of speech; 
         [0030]    quantize the input signal, thus generating a quantized output signal; 
         [0031]    prior to said quantization, filter a version of the input signal using a first noise shaping filter having a first set of filter coefficients, thus generating a first filtered signal based on that version of the input signal and the first set of filter coefficients; 
         [0032]    following said quantization, filter a version of the quantized output signal using a second noise shaping filter having a second set of filter coefficients different than said first set, thus generating a second filter signal based on that version of the quantized output signal and the second set of filter coefficients; 
         [0033]    perform a noise shaping operation to control a frequency spectrum of a noise effect in the quantized output signal caused by said quantization, wherein the noise shaping operation is performed based on both the first and second filtered signals; and 
         [0034]    output the quantised output signal in an encoded signal. 
         [0035]    According to one or more embodiments, there are provided corresponding computer program products such as client application products configured so as when executed on a processor to perform the methods described above. 
         [0036]    According to one or more embodiments, there is provided a communication system comprising a plurality of end-user terminals each comprising a corresponding encoder. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0037]    For a better understanding of the described embodiments and to show how it may be carried into effect, reference will now be made by way of example to the accompanying drawings in which: 
           [0038]      FIG. 1   a  is a schematic diagram of a noise shaping quantizer, 
           [0039]      FIG. 1   b  is a schematic diagram of another noise shaping quantizer, 
           [0040]      FIG. 1   c  is a schematic diagram of an analysis-by-synthesis quantizer, 
           [0041]      FIG. 1   d  is a schematic diagram of a noise shaping predictive quantizer, 
           [0042]      FIG. 1   e  is a schematic diagram of another noise shaping predictive quantizer, 
           [0043]      FIG. 2   a  is a schematic diagram of another noise shaping predictive quantizer, 
           [0044]      FIG. 2   b  is a schematic diagram of another noise shaping predictive quantizer, 
           [0045]      FIG. 2   c  is a schematic diagram of a predictive analysis-by-synthesis quantizer, 
           [0046]      FIG. 3  illustrates a modification to a signal frequency spectrum, 
           [0047]      FIG. 4   a  is a schematic representation of a source-filter model of speech, 
           [0048]      FIG. 4   b  is a schematic representation of a frame, 
           [0049]      FIG. 4   c  is a schematic representation of a source signal, 
           [0050]      FIG. 4   d  is a schematic representation of variations in a spectral envelope, 
           [0051]      FIG. 5  is a schematic diagram of an encoder, 
           [0052]      FIG. 6   a  is another schematic diagram of a noise shaping predictive quantizer, 
           [0053]      FIG. 6   b  is another schematic diagram of a noise shaping predictive quantizer, 
           [0054]      FIG. 7   a  is another schematic diagram of a decoder, and 
           [0055]      FIG. 7   b  shows more detail of the decoder of  FIG. 7   a.    
       
    
    
     DETAILED DESCRIPTION 
       [0056]    Various embodiments apply one filter to a signal before quantization and another filter with different filter coefficients to a signal after quantization. As will be discussed in more detail below, this allows a signal spectrum and coding noise spectrum to be manipulated separately, and can be applied in order to improve coding efficiency and/or reduce noise. 
         [0057]    To achieve the desired noise shaping, either the filter outputs can be combined to create an input to a quantization unit, or the filter outputs can be subtracted to create a weighted speech signal that is minimized by searching a codebook. In one or more embodiments, both filters are updated over time based on a noise shaping analysis of the input signal. The noise shaping analysis determines exactly how the signal and coding noise should be shaped over spectrum and time such that the perceived quality of the resulting quantized output signal is maximized. 
         [0058]    One example of a noise shaping predictive quantizer  200  with different filters for input and output signals is shown in  FIG. 2   a . The noise shaping predictive quantizer  200  comprises a quantization unit  202 , a prediction filter  204  in a closed-loop configuration, a first noise shaping filter  206  having first filter coefficients, and a second noise shaping filter  208  having second filter coefficients different from the first filter coefficients. The noise shaping predictive quantizer  200  also comprises an amplifier  210 , a first subtraction stage  212 , a first addition stage  214 , a second subtraction stage  216  and a second addition stage  218 . 
         [0059]    The first noise shaping filter  206  and the first subtraction stage  212  each have inputs arranged to receive an input signal x(n) representing speech or some property of speech. The other input of the first subtraction stage  212  is coupled to the output of the first noise shaping filter  206 , and the output of the first subtraction stage  212  is coupled to the input of the amplifier  210 . The output of the amplifier  210  is coupled to an input of the first addition stage  214 , and the other input of the first addition stage  214  is coupled to the output of the second noise shaping filter  208 . The output of the first addition stage  214  is coupled to an input of the second subtraction stage  216 , and the other input of the second subtraction stage is coupled to the output of the prediction filter  204 . The output of the second subtraction stage is coupled to the input of the quantization unit  202 , which has an output arranged to supply quantization indices i(n) for transmission in an encoded signal over a transmission medium. The quantization unit  202  also has an output arranged to generate a quantized version of its input, and that output is coupled to an input of the second addition stage  218 . The other input of the second addition stage  218  is coupled to the output of the prediction filter  204 . The output of the second addition stage is thus arranged to generate a quantized output signal y(n), and that output is coupled to the inputs of both the prediction filter  204  and the second noise shaping filter  208 . 
         [0060]    In operation, the input signal x(n) is filtered by the first noise shaping filter  206 , which is an analysis shaping filter which may be represented by a function F1(z) in the frequency domain. The output of this filtering is subtracted from the input signal x(n) at the first subtraction stage  212 , and the result of the subtraction is then multiplied by a compensation gain G at the amplifier  210 . The second noise shaping filter  208  is a synthesis shaping filter which may be represented by a function F2(z) in the frequency domain. The predictive filter  204  may be represented by a function P(z) in the frequency domain. The output of the second noise shaping filter  208  is added to the output of the amplifier  210  at the first addition stage  214 , and the output of the prediction filter  204  is subtracted from the output of the amplifier  210  at the second subtraction stage  216  to obtain the difference between actual and predicted versions of the signal at this point, thus producing the input to the quantization unit  202 . The quantization unit  202  quantizes its input, thus producing quantization indices for transmission to a decoder over a transmission medium as part of an encoded signal, and also producing an output which is quantized version of its input. The output of the prediction filter  204  is added to this output of the quantization unit  202  at the second addition stage  218 , thus producing the quantized output signal y(n). The quantized output signal is fed back for input to each of the second noise shaping filter  208  F2(z) and the prediction filter  204  to produce their respective filtered outputs (note again that the quantized output y is produced in the encoder only for feedback: it is the quantization indices i which form part of the encoded signal, and these will be used at the decoder to reconstruct the quantised signal y). 
         [0061]    In the z-domain (i.e. frequency domain), the quantized output signal of this example can be described as: 
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         [0062]    The equation above shows that the noise shaping with different filters for input and output signal accomplishes two goals. Firstly, the signal spectrum is modified with a pre-processing filter: 
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         [0063]    Secondly, the noise spectrum is shaped according to (1−F2(z)) −1 . 
         [0064]    Thus, using two different filters allows for an independent manipulation of signal and coding noise spectrum. 
         [0065]    Modifying the signal spectrum in such a manner can be used to produce two advantageous effects. The first effect is to suppress, or deemphasize, the values in between speech formants using short-term shaping and the valleys in between speech harmonics using long-term shaping. The effect of this suppression is to reduce the entropy of the signal relative to the coding noise level, thereby increasing the efficiency of the encoder. An example of this effect is demonstrated in  FIG. 3 , which is a frequency spectrum graph (i.e. of signal power or energy vs. frequency) showing a reduced entropy by de-emphasizing the valleys in between speech formants. The top curve shows an input signal, the middle curve shows the de-emphasised valleys, and the lower curve shows the coding noise. By reducing the signal spectrum in the valleys between the spectral peaks, while keeping the coding noise spectrum constant, the entropy, as defined as the area between the signal and noise spectra, is reduced. 
         [0066]    The second effect that can be achieved by modifying the signal spectrum is to reduce noise in the input signal. By estimating the signal spectrum and noise spectrum of the signal at regular time intervals, the analysis and synthesis shaping filters (i.e. first and second noise shaping filters  206  and  208 ) can be configured such that the parts of the spectrum with a low signal-to-noise ratio are attenuated while parts of the spectrum with a high signal-to-noise ratio are left substantially unchanged. 
         [0067]    A noise shaping analysis can be performed to update the analysis and synthesis shaping filters F1(z) and F2(z) in a joint manner. 
         [0068]      FIG. 2   b  shows an alternative implementation of a noise shaping predictive quantizer  230 , again with different filters for input and output signals but this time based on open-loop prediction instead of closed loop. The noise shaping predictive quantizer  230  comprises a quantization unit  232 , a first instance of a prediction filter  234 , a second instance of the prediction filter  234 ′, a first noise shaping filter  236  having first filter coefficients, an a second noise shaping filter  238  having second filter coefficients. The noise shaping predictive quantizer  230  further comprises a first subtraction stage  240 , a first addition stage  242 , a second subtraction stage  244  and a second addition stage  246 . 
         [0069]    The first subtraction stage  240  and the first instance of the prediction filter  234  each have inputs arranged to receive the input signal x(n). The other input of the first subtraction stage  240  is coupled to the output of the first instance of the prediction filter  234 , and the output of the first subtraction stage is coupled to the input of the first addition stage  242 . The other input of the first addition stage  242  is coupled to the output of the second subtraction stage  244 , and the output of the first addition stage  242  is coupled to the inputs of the quantization unit  232  and the first noise shaping filter  236 . The quantization unit  232  has an output arranged to supply quantization indices i(n), and another output arranged to generate a quantized version of its input. The latter output is coupled to an input of the second addition stage  246  and to the input of the second noise shaping filter  238 . The outputs of the first and second noise shaping filters  236  and  238  are coupled to respective inputs of the second subtraction stage  244 . The output of the second addition stage  246  is coupled to the input of the second instance of the prediction filter  234 ′, and the output of the second instance of the prediction filter  234 ′ fed back to the other input of the second addition stage  246 . The signal output from the second addition stage  246  is the quantized output signal y(n), as will be reconstructed using the indices i(n) at the decoder. 
         [0070]    In operation, the prediction is done open loop, meaning that a prediction of the input signal is based on the input signal and a prediction of the output is based on the quantized output signal. Also, noise shaping is done by filtering the input and output of the quantizer instead of the input and output of the codec. The input signal x(n) is supplied to the first instance of the prediction filter  234 , which may be represented by a function P(z) in the frequency domain. The first instance of the prediction filter  234  thus produces a filtered output based on the input signal x(n), which is then subtracted from the input signal x(n) at the first subtraction stage  240  to obtain the difference between the actual and predicted input signals. Also, the second subtraction stage  244  takes the difference between the filtered outputs of the first and second noise shaping filters  236  and  238 , which may be represented by functions F1(z) and F2(z) respectively in the frequency domain. These two differences are added together at the first addition stage  242 . The resulting signal is supplied as an input to the quantization unit  232 , and also supplied to the input of the first noise shaping filter  236  in order to produce its respective filtered output. The quantization unit  202  quantizes its input, thus producing quantization indices for transmission to a decoder, and also producing an output which is quantized version of its input. This quantized output is supplied to an input of the second addition stage  246 , and also supplied to the second noise shaping filter  238  in order to produce its respective filtered output. At the second addition stage  246  the output of the second instance of the prediction filter  234 ′ is added to the quantized output of the quantization unit  232 , thus producing the quantized output signal y(n), which is fed back to the input of the second instance of the prediction filter  234 ′ to produce its respective filtered output. 
         [0071]    In the z-domain (i.e. frequency domain), the quantized output signal of this example can be described as: 
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                     ) 
                   
                 
               
               + 
               
                 
                   
                     1 
                     + 
                     
                       F 
                        
                       
                           
                       
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                       1 
                        
                       
                         ( 
                         z 
                         ) 
                       
                     
                   
                   
                     1 
                     + 
                     
                       F 
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                        
                       1 
                        
                       
                         ( 
                         z 
                         ) 
                       
                     
                     - 
                     
                       F 
                        
                       
                           
                       
                        
                       2 
                        
                       
                         ( 
                         z 
                         ) 
                       
                     
                   
                 
                  
                 
                   
                     Q 
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                       ( 
                       z 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0072]    Again, it can be seen that using two different filters allows for an independent manipulation of signal and coding noise spectrum. 
         [0073]    A further embodiment is now described in relation to  FIG. 2   c , which shows an analysis-by-synthesis predictive quantizer  260  with different filters for input and output signals. The analysis-by-synthesis predictive quantizer  260  comprises a controllable quantization unit  262 , a prediction filter  264 , a first weighting filter  266 , a second weighting filter  268 , an energy minimization block  270 , a subtraction stage  272  and an addition stage  274 . The first weighting filter has its input arranged to receive the input signal x(n), and its output coupled to an input of the subtraction stage  272 . The other input of the subtraction stage  272  is coupled to the output of the second weighting filter  268 . The output of the subtraction stage is coupled to the input of the energy minimization block  270 , and the output of the energy minimization block  270  is coupled to a control input of the quantization unit  262 . The quantization unit  262  has outputs arranged to supply quantization indices i(n) and a quantized output respectively. The latter output of the quantization unit  262  is coupled to an input of the addition stage  274 , and the other input of the addition stage is coupled to the output of the prediction filter  264 . The output of the addition stage  274  is coupled to the inputs of the prediction filter  264  and the second weighting filter  268 . The signal output from the addition stage  264  is the quantized output signal y(n), as will be reconstructed using the indices i(n) at the decoder. 
         [0074]    In operation, the input and output signals are filtered with analysis and synthesis weighting filters. 
         [0075]    The quantization unit  262  generates a plurality of possible versions of a portion of the quantized output signal y(n). For each possible version, the addition stage  274  adds the quantized output of the quantization unit  262  to the filtered output of the prediction filter  264 , thus producing the quantized output signal y(n) which is fed back to the inputs of the prediction filter  264  and the second weighting filter  268  to produce their respective filtered outputs. Also, the input signal x(n) is filtered by the first weighting filter  266  to produce a respective filtered output. The prediction filter  264  and first and second weighting filters  266  and  268  may be represented by functions P(z), W1(z) and W2(z) respectively in the frequency domain. The subtraction stage  272  takes the difference between the filtered outputs of the first and second weighting filters  266  and  268  to produce an error signal, which is supplied to the input of energy minimization block  270 . The energy minimization block  270  determines the energy in this error signal for each possible version of the quantized output signal y(n), and selects the version resulting in the least energy in the error signal. 
         [0076]    In the frequency domain, the output signal of this example can be described as: 
         [0000]    
       
         
           
             
               Y 
                
               
                 ( 
                 z 
                 ) 
               
             
             = 
             
               
                 
                   
                     W 
                      
                     
                         
                     
                      
                     1 
                      
                     
                       ( 
                       z 
                       ) 
                     
                   
                   
                     W 
                      
                     
                         
                     
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                     2 
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                       ( 
                       z 
                       ) 
                     
                   
                 
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                     z 
                     ) 
                   
                 
               
               + 
               
                 
                   1 
                   
                     W 
                      
                     
                         
                     
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                     2 
                      
                     
                       ( 
                       z 
                       ) 
                     
                   
                 
                  
                 
                   
                     Q 
                      
                     
                       ( 
                       z 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0077]    Again therefore, using two different filters allows for an independent manipulation of signal and coding noise spectrum. 
         [0078]    Remember that by defining W(z)=1−F(z), analysis-by-synthesis quantization can be interpreted as noise shaping quantization. Thus a suitably configured weighting filter can be considered as a noise shaping filter. 
         [0079]    An example implementation in the context of speech coding is now discussed. 
         [0080]    As illustrated schematically in  FIG. 4   a , according to a source-filter model speech can be modelled as comprising a signal from a source  402  passed through a time-varying filter  404 . The source signal represents the immediate vibration of the vocal chords, and the filter represents the acoustic effect of the vocal tract formed by the shape of the throat, mouth and tongue. The effect of the filter is to alter the frequency profile of the source signal so as to emphasise or diminish certain frequencies. Instead of trying to directly represent an actual waveform, speech encoding works by representing the speech using parameters of a source-filter model. 
         [0081]    As illustrated schematically in  FIG. 4   b , the encoded signal will be divided into a plurality of frames  406 , with each frame comprising a plurality of subframes  408 . For example, speech may be sampled at 16 kHz and processed in frames of 20 ms, with some of the processing done in subframes of 5 ms (four subframes per frame). Each frame comprises a flag  407  by which it is classed according to its respective type. Each frame is thus classed at least as either “voiced” or “unvoiced”, and unvoiced frames are encoded differently than voiced frames. Each subframe  408  then comprises a set of parameters of the source-filter model representative of the sound of the speech in that subframe. 
         [0082]    For voiced sounds (e.g. vowel sounds), the source signal has a degree of long-term periodicity corresponding to the perceived pitch of the voice. In that case, the source signal can be modelled as comprising a quasi-periodic signal, with each period corresponding to a respective “pitch pulse” comprising a series of peaks of differing amplitudes. The source signal is said to be “quasi” periodic in that on a timescale of at least one subframe it can be taken to have a single, meaningful period which is approximately constant; but over many subframes or frames then the period and form of the signal may change. The approximated period at any given point may be referred to as the pitch lag. An example of a modelled source signal  402  is shown schematically in  FIG. 4   c  with a gradually varying period P 1 , P 2 , P 3 , etc., each comprising a pitch pulse of four peaks which may vary gradually in form and amplitude from one period to the next. 
         [0083]    As mentioned, prediction filtering may be used to derive a residual signal having less energy that an input speech signal and therefore requiring fewer bits to quantize. 
         [0084]    According to many speech coding algorithms such as those using Linear Predictive Coding (LPC), a short-term prediction filter is used to separate out the speech signal into two separate components: (i) a signal representative of the effect of the time-varying filter  404 ; and (ii) the remaining signal with the effect of the filter  404  removed, which is representative of the source signal. The signal representative of the effect of the filter  404  may be referred to as the spectral envelope signal, and typically comprises a series of sets of LPC parameters describing the spectral envelope at each stage.  FIG. 4   d  shows a schematic example of a sequence of spectral envelopes  4041 ,  4042 ,  4043 , etc. varying over time. Once the varying spectral envelope is removed, the remaining signal representative of the source alone may be referred to as the LPC residual signal, as shown schematically in  FIG. 4   c . The LPC short-term filtering works by using an LPC analysis to determine a short-term correlation in recently received samples of the speech signal (i.e. short-term compared to the pitch period), then passing coefficients of that correlation to an LPC synthesis filter to predict following samples. The predicted samples are fed back to the input where they are subtracted from the speech signal, thus removing the effect of the spectral envelope and thereby deriving an LTP residual signal representing the modelled source of the speech. The LPC residual signal has less energy that the input speech signal and therefore requiring fewer bits to quantize. 
         [0085]    The spectral envelope signal and the source signal are each encoded separately for transmission. In the illustrated example, each subframe  406  would contain: (i) a set of parameters representing the spectral envelope  404 ; and (ii) an LPC residual signal representing the source signal  402  with the effect of the short-term correlations removed. 
         [0086]    To further improve the encoding of the source signal, its periodicity may also be exploited. To do this, a long-term prediction (LTP) analysis is used to determine the correlation of the LPC residual signal with itself from one period to the next, i.e. the correlation between the LPC residual signal at the current time and the LPC residual signal after one period at the current pitch lag (correlation being a statistical measure of a degree of relationship between groups of data, in this case the degree of repetition between portions of a signal). In this context the source signal can be said to be “quasi” periodic in that on a timescale of at least one correlation calculation it can be taken to have a meaningful period which is approximately (but not exactly) constant; but over many such calculations then the period and form of the source signal may change more significantly. A set of parameters derived from this correlation are determined to at least partially represent the source signal for each subframe. The set of parameters for each subframe is typically a set of coefficients C of a series, which form a respective vector C LTP =(C 1 , C 2 , . . . C i ). 
         [0087]    The effect of this inter-period correlation is then removed from the LPC residual, leaving an LTP residual signal representing the source signal with the effect of the correlation between pitch periods removed. To do this, an LTP analysis is used to determine a correlation between successive received pitch pulses in the LPC residual signal, then coefficients of that correlation are passed to an LTP synthesis filter where they are used to generate a predicted version of the later of those pitch pulses from the last stored one of the preceding pitch pulses. The predicted pitch pulse is fed back to the input where it is subtracted from the corresponding portion of the actual LPC residual signal, thus removing the effect of the periodicity and thereby deriving an LTP residual signal. Put another way, the LTP synthesis filter uses a long-term prediction to effectively remove or reduce the pitch pulses from the LPC residual signal, leaving an LTP residual signal having lower energy than the LPC residual. To represent the source signal, the LTP vectors and LTP residual signal are encoded separately for transmission. 
         [0088]    The sets of LPC parameters, the LTP vectors and the LTP residual signal are each quantised prior to transmission (quantisation being the process of converting a continuous range of values into a set of discrete values, or a larger approximately continuous set of discrete values into a smaller set of discrete values). The advantage of separating out the LPC residual signal into the LTP vectors and LTP residual signal is that the LTP residual typically has a lower energy than the LPC residual, and so requires fewer bits to quantize. 
         [0089]    So in the illustrated example, each subframe  406  would comprise: (i) a quantised set of LPC parameters representing the spectral envelope, (ii)(a) a quantised LTP vector related to the correlation between pitch periods in the source signal, and (ii)(b) a quantised LTP residual signal representative of the source signal with the effects of this inter-period correlation removed. 
         [0090]    In contrast with voiced sounds, for unvoiced sounds such as plosives (e.g. “T” or “P” sounds) the modelled source signal has no substantial degree of periodicity. In that case, long-term prediction (LTP) cannot be used and the LPC residual signal representing the modelled source signal is instead encoded differently, e.g. by being quantized directly. 
         [0091]    An example of an encoder  500  for implementing one or more embodiments is now described in relation to  FIG. 5 . 
         [0092]    The encoder  500  comprises a high-pass filter  502 , a linear predictive coding (LPC) analysis block  504 , a first vector quantizer  506 , an open-loop pitch analysis block  508 , a long-term prediction (LTP) analysis block  510 , a second vector quantizer  512 , a noise shaping analysis block  514 , a noise shaping quantizer  516 , and an arithmetic encoding block  518 . The noise shaping quantizer  516  could be of the type of any of the quantizers  200 ,  230  or  260  discussed in relation to  FIGS. 2   a ,  2   b  and  2   c  respectively. 
         [0093]    The high pass filter  502  has an input arranged to receive an input speech signal from an input device such as a microphone, and an output coupled to inputs of the LPC analysis block  504 , noise shaping analysis block  514  and noise shaping quantizer  516 . The LPC analysis block has an output coupled to an input of the first vector quantizer  506 , and the first vector quantizer  506  has outputs coupled to inputs of the arithmetic encoding block  518  and noise shaping quantizer  516 . The LPC analysis block  504  has outputs coupled to inputs of the open-loop pitch analysis block  508  and the LTP analysis block  510 . The LTP analysis block  510  has an output coupled to an input of the second vector quantizer  512 , and the second vector quantizer  512  has outputs coupled to inputs of the arithmetic encoding block  518  and noise shaping quantizer  516 . The open-loop pitch analysis block  508  has outputs coupled to inputs of the LTP  510  analysis block  510  and the noise shaping analysis block  514 . The noise shaping analysis block  514  has outputs coupled to inputs of the arithmetic encoding block  518  and the noise shaping quantizer  516 . The noise shaping quantizer  516  has an output coupled to an input of the arithmetic encoding block  518 . The arithmetic encoding block  518  is arranged to produce an output bitstream based on its inputs, for transmission from an output device such as a wired modem or wireless transceiver. 
         [0094]    In operation, the encoder processes a speech input signal sampled at 16 kHz in frames of 20 milliseconds, with some of the processing done in subframes of 5 milliseconds. The output bitstream payload contains arithmetically encoded parameters, and has a bitrate that varies depending on a quality setting provided to the encoder and on the complexity and perceptual importance of the input signal. 
         [0095]    The speech input signal is input to the high-pass filter  504  to remove frequencies below 80 Hz which contain almost no speech energy and may contain noise that can be detrimental to the coding efficiency and cause artifacts in the decoded output signal. In at least some embodiments, the high-pass filter  504  is a second order auto-regressive moving average (ARMA) filter. 
         [0096]    The high-pass filtered input x HP  is input to the linear prediction coding (LPC) analysis block  504 , which calculates 16 LPC coefficients a(i) using the covariance method which minimizes the energy of the LPC residual r LPC : 
         [0000]    
       
         
           
             
               
                 r 
                 LPC 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 
                   x 
                   HP 
                 
                  
                 
                   ( 
                   n 
                   ) 
                 
               
               - 
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   16 
                 
                  
                 
                   
                     
                       x 
                       HP 
                     
                      
                     
                       ( 
                       
                         n 
                         - 
                         i 
                       
                       ) 
                     
                   
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                       a 
                        
                       
                         ( 
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                         ) 
                       
                     
                     . 
                   
                 
               
             
           
         
       
     
         [0097]    The LPC coefficients are transformed to a line spectral frequency (LSF) vector. The LSFs are quantized using the first vector quantizer  506 , a multi-stage vector quantizer (MSVQ) with 10 stages, producing 10 LSF indices that together represent the quantized LSFs. The quantized LSFs are transformed back to produce the quantized LPC coefficients a Q  for use in the noise shaping quantizer  516 . 
         [0098]    The LPC residual is input to the open loop pitch analysis block  508 , producing one pitch lag for every 5 millisecond subframe, i.e., four pitch lags per frame. The pitch lags are chosen between 32 and 288 samples, corresponding to pitch frequencies from 56 to 500 Hz, which covers the range found in typical speech signals. Also, the pitch analysis produces a pitch correlation value which is the normalized correlation of the signal in the current frame and the signal delayed by the pitch lag values. Frames for which the correlation value is below a threshold of 0.5 are classified as unvoiced, i.e., containing no periodic signal, whereas all other frames are classified as voiced. The pitch lags are input to the arithmetic coder  518  and noise shaping quantizer  516 . 
         [0099]    For voiced frames, a long-term prediction analysis is performed on the LPC residual. The LPC residual r LPC  is supplied from the LPC analysis block  504  to the LTP analysis block  510 . For each subframe, the LTP analysis block  510  solves normal equations to find  5  linear prediction filter coefficients b(i) such that the energy in the LTP residual r LTP  for that subframe: 
         [0000]    
       
         
           
             
               
                 r 
                 LTP 
               
                
               
                 ( 
                 n 
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         [0000]    is minimized. The normal equations are solved as: 
         [0000]        b=W   LTP   −1   C   LTP , 
         [0000]    where W LTP  is a weighting matrix containing correlation values 
         [0000]    
       
         
           
             
               
                 
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                   LTP 
                 
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             , 
           
         
       
     
         [0000]    and C LTP  is a correlation vector: 
         [0000]    
       
         
           
             
               
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                 LTP 
               
                
               
                 ( 
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         [0100]    Thus, the LTP residual is computed as the LPC residual in the current subframe minus a filtered and delayed LPC residual. The LPC residual in the current subframe and the delayed LPC residual are both generated with an LPC analysis filter controlled by the same LPC coefficients. That means that when the LPC coefficients were updated, an LPC residual is computed not only for the current frame but also a new LPC residual is computed for at least lag+2 samples preceding the current frame. 
         [0101]    The LTP coefficients for each frame are quantized using a vector quantizer (VQ). The resulting VQ codebook index is input to the arithmetic coder, and the quantized LTP coefficients b Q  are input to the noise shaping quantizer  516 . 
         [0102]    The high-pass filtered input is analyzed by the noise shaping analysis block  514  to find filter coefficients and quantization gains used in the noise shaping quantizer. The filter coefficients determine the distribution of the coding noise over the spectrum, and are chose such that the quantization is least audible. The quantization gains determine the step size of the residual quantizer and as such govern the balance between bitrate and coding noise level. 
         [0103]    All noise shaping parameters are computed and applied per subframe of 5 milliseconds, except for the quantization offset which is determines once per frame of 20 milliseconds. First, a 16 th  order noise shaping LPC analysis is performed on a windowed signal block of 16 milliseconds. The signal block has a look-ahead of 5 milliseconds relative to the current subframe, and the window is an asymmetric sine window. The noise shaping LPC analysis is done with the autocorrelation method. The quantization gain is found as the square-root of the residual energy from the noise shaping LPC analysis, multiplied by a constant to set the average bitrate to the desired level. For voiced frames, the quantization gain is further multiplied by 0.5 times the inverse of the pitch correlation determined by the pitch analyses, to reduce the level of coding noise which is more easily audible for voiced signals. The quantization gain for each subframe is quantized, and the quantization indices are input to the arithmetically encoder  518 . The quantized quantization gains are input to the noise shaping quantizer  516 . 
         [0104]    According to one or more embodiments, the noise shaping analysis block  514  determines separate analysis and synthesis noise shaping filter coefficients. The short-term analysis and synthesis noise shaping coefficients a shape,ana (i) and a shape,syn (i) are (i) obtained by applying bandwidth expansion to the coefficients found in the noise shaping LPC analysis. This bandwidth expansion moves the roots of the noise shaping LPC polynomial towards the origin, according to the formula: 
         [0000]        a   shape,ana ( i )= a   autocorr ( i ) g   ana     i      
         [0000]      and 
         [0000]        a   shape,syn ( i )= a   autocorr ( i ) g   syni     i      
         [0000]    where a autocorr (i) is the ith coefficient from the noise shaping LPC analysis and for the bandwidth expansion factors good results are obtained with: g ana =0.9 and g syn =0.96. 
         [0105]    For voiced frames, the noise shaping quantizer  516  also applies long-term noise shaping. It uses three filter taps in analysis and synthesis long-term noise shaping filters, described by: 
         [0000]        b   shape,ana =0.4sqrt(PitchCorrelation)[0.25, 0.5, 0.25] 
         [0000]      and 
         [0000]        b   shape,syn =0.5sqrt(PitchCorrelation)[0.25, 0.5, 0.25]. 
         [0106]    The short-term and long-term noise shaping coefficients are determined by the noise shaping analysis block  514  and input to the noise shaping quantizer  516 . 
         [0107]    In one or more embodiments, an adjustment gain G serves to correct any level mismatch between original and decoded signal that might arise from the noise shaping and de-emphasis. This gain is computed as the ratio of the prediction gain of the short-term analysis and synthesis shaping filter coefficients. The prediction gain of an LPC synthesis filter is the square-root of the output energy when the filter is excited by a unit-energy impulse on the input. An efficient way to compute the prediction gain is by first computing the reflection coefficients from the LPC coefficients through the step-down algorithm, and extracting the prediction gain from the reflection coefficients as: 
         [0000]    
       
         
           
             
               predGain 
               = 
               
                 
                   ( 
                   
                     
                       
                         ∏ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                        
                       1 
                     
                     - 
                     
                       r 
                       k 
                       2 
                     
                   
                   ) 
                 
                 
                   - 
                   0.5 
                 
               
             
             , 
           
         
       
     
         [0000]    where r k  are the reflection coefficients. 
         [0108]    The high-pass filtered input x HP (n) is input to the noise shaping quantizer  516 , discussed in more detail in relation to  FIG. 6   b  below. All gains and filter coefficients and gains are updated for every subframe, except for the LPC coefficients which are updated once per frame. 
         [0109]    By way of contrast with the described embodiments, an example of a noise shaping quantizer  600  without separate noise shaping filters at the inputs and outputs is first described in relation to  FIG. 6   a.    
         [0110]    The noise shaping quantizer  600  comprises a first addition stage  602 , a first subtraction stage  604 , a first amplifier  606 , a quantization unit  608 , a second amplifier  609 , a second addition stage  610 , a shaping filter  612 , a prediction filter  614  and a second subtraction stage  616 . The shaping filter  612  comprises a third addition stage  618 , a long-term shaping block  620 , a third subtraction stage  622 , and a short-term shaping block  624 . The prediction filter  614  comprises a fourth addition stage  626 , a long-term prediction block  628 , a fourth subtraction stage  630 , and a short-term prediction block  632 . 
         [0111]    The first addition stage  602  has an input that would be arranged to receive the high-pass filtered input from the high-pass filter  502 , and another input coupled to an output of the third addition stage  618 . The first subtraction stage has inputs coupled to outputs of the first addition stage  602  and fourth addition stage  626 . The first amplifier has a signal input coupled to an output of the first subtraction stage and an output coupled to an input of the quantization unit  608 . The first amplifier  606  also has a control input which would be coupled to the output of the noise shaping analysis block  514 . The quantization unit  608  has an output coupled to input of the second amplifier  609  and would also have an output coupled to the arithmetic encoding block  518 . The second amplifier  609  would also have a control input coupled to the output of the noise shaping analysis block  514 , and an output coupled to the an input of the second addition stage  610 . The other input of the second addition stage  610  is coupled to an output of the fourth addition stage  626 . An output of the second addition stage is coupled back to the input of the first addition stage  602 , and to an input of the short-term prediction block  632  and the fourth subtraction stage  630 . An output of the short-tem prediction block  632  is coupled to the other input of the fourth subtraction stage  630 . The output of the fourth subtraction stage  630  is coupled to the input of the long-term prediction block  628 . The fourth addition stage  626  has inputs coupled to outputs of the long-term prediction block  628  and short-term prediction block  632 . The output of the second addition stage  610  is further coupled to an input of the second subtraction stage  616 , and the other input of the second subtraction stage  616  is coupled to the input from the high-pass filter  502 . An output of the second subtraction stage  616  is coupled to inputs of the short-term shaping block  624  and the third subtraction stage  622 . An output of the short-tem shaping block  624  is coupled to the other input of the third subtraction stage  622 . The output of the third subtraction stage  622  is coupled to the input of the long-term shaping block  620 . The third addition stage  618  has inputs coupled to outputs of the long-term shaping block  620  and short-term shaping block  624 . The short-term and long-term shaping blocks  624  and  620  would each also be coupled to the noise shaping analysis block  514 , the long-term shaping block  620  would also be coupled to the open-loop pitch analysis block  508  (connections not shown). Further, the short-term prediction block  632  would be coupled to the LPC analysis block  504  via the first vector quantizer  506 , and the long-term prediction block  628  would be coupled to the LTP analysis block  510  via the second vector quantizer  512  (connections also not shown). 
         [0112]    In operation, the noise shaping quantizer  600  generates a quantized output signal that is identical to the output signal ultimately generated in the decoder. 
         [0113]    The input signal is subtracted from this quantized output signal at the second subtraction stage  616  to obtain the coding noise signal d(n). The coding noise signal is input to a shaping filter  612 , described in detail later. The output of the shaping filter  612  is added to the input signal at the first addition stage  602  in order to effect the spectral shaping of the coding noise. From the resulting signal, the output of the prediction filter  614 , described in detail below, is subtracted at the first subtraction stage  604  to create a residual signal. The residual signal would be multiplied at the first amplifier  606  by the inverse quantized quantization gain from the noise shaping analysis block  514 , and input to the scalar quantizer  608 . The quantization indices of the scalar quantizer  608  represent an excitation signal that would be input to the arithmetically encoder  518 . The scalar quantizer  608  also outputs a quantization signal, which would be multiplied at the second amplifier  609  by the quantized quantization gain from the noise shaping analysis block  514  to create an excitation signal. The output of the prediction filter  614  is added at the second addition stage to the excitation signal to form the quantized output signal. The quantized output signal is input to the prediction filter  614 . 
         [0114]    On a point of terminology, note that there is a small difference between the terms “residual” and “excitation”. A residual is obtained by subtracting a prediction from the input speech signal. An excitation is based on only the quantizer output. Often, the residual is simply the quantizer input and the excitation is its output. 
         [0115]    The shaping filter  612  inputs the coding noise signal d(n) to a short-term shaping filter  624 , which uses the short-term shaping coefficients a shape  to create a short-term shaping signal s short (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 short 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 16 
               
                
               
                 
                   d 
                    
                   
                     ( 
                     
                       n 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       a 
                       shape 
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0116]    The short-term shaping signal is subtracted at the third addition stage  622  from the coding noise signal to create a shaping residual signal f(n). The shaping residual signal is input to a long-term shaping filter  620  which uses the long-term shaping coefficients b shape  to create a long-term shaping signal s long (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 long 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   
                     - 
                     2 
                   
                 
                 2 
               
                
               
                 
                   f 
                    
                   
                     ( 
                     
                       n 
                       - 
                       lag 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       b 
                       shape 
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0117]    The short-term and long-term shaping signals are added together at the third addition stage  618  to create the shaping filter output signal. 
         [0118]    The prediction filter  614  inputs the quantized output signal y(n) to a short-term prediction filter  632 , which uses the quantized LPC coefficients a, to create a short-term prediction signal p short (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 p 
                 short 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 16 
               
                
               
                 
                   y 
                    
                   
                     ( 
                     
                       n 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     a 
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0119]    The short-term prediction signal is subtracted at the fourth subtraction stage  630  from the quantized output signal to create an LPC excitation signal e LPC (n). The LPC excitation signal is input to a long-term prediction filter  628  which uses the quantized long-term prediction coefficients b i  to create a long-term prediction signal p long (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 p 
                 long 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   
                     - 
                     2 
                   
                 
                 2 
               
                
               
                 
                   
                     e 
                     LPC 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       lag 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     b 
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0120]    The short-term and long-term prediction signals are added together at the fourth addition stage  626  to create the prediction filter output signal. 
         [0121]    The LSF indices, LTP indices, quantization gains indices, pitch lags and excitation quantization indices would each be arithmetically encoded and multiplexed by the arithmetic encoder  518  to create the payload bitstream. 
         [0122]    As an illustration of one embodiment, a noise shaping predictive quantizer  516  having separate noise shaping filters at the input and output is now described in relation to  FIG. 6   b.    
         [0123]    The noise shaping quantizer  516  comprises: a first subtraction stage  652 , a first amplifier  654 , a first addition stage  656 , a second subtraction stage  658 , a second amplifier  660 , a quantization unit  662 , a third amplifier  664 , a second addition stage  666 , a first noise shaping filter in the form of an analysis shaping filter  668 , a second noise shaping filter in the form of a synthesis shaping filter  670 , and a prediction filter  672 . The analysis shaping filter  668  comprises a third addition stage  674 , a first long-term shaping block  676 , a third subtraction stage  678 , and a first short-term shaping block  680 . The synthesis shaping filter  670  comprises a fourth addition stage  682 , a second long-term shaping block  684 , a fourth subtraction stage  686 , and a second short-term shaping block  688 . The prediction filter  672  comprises a fifth addition stage  690 , a long-term prediction block  692 , a fifth subtraction stage  694 , and a short-term prediction block  696 . 
         [0124]    The first subtraction stage  652  has an input arranged to receive the high-pass filtered input signal x HP (n) from the high-pass filter  502 . Its other input is coupled to the output of the third addition stage  674  in the analysis shaping filter  668 . The output of the first subtraction stage  652  is coupled to a signal input of the first amplifier  654 . The first amplifier also has a control input coupled to the noise shaping analysis block  514 . The output of the first amplifier  654  is coupled to an input of the first addition stage  656 . The other input of the first addition stage  656  is coupled to the output of the fourth addition stage  682  in the synthesis shaping filter  670 . The output of the first addition stage  656  is coupled to an input of the second subtraction stage  658 . The other input of the second subtraction stage  658  is coupled to the output of the fifth addition stage  690  in the prediction filter  672 . The output of the second subtraction stage  658  is coupled to a signal input of the second amplifier  660 . The second amplifier  660  also has a control input coupled to the noise shaping analysis block  514 . The output of the second amplifier  660  is coupled to the input of the quantization unit  662 . The quantization unit  662  has an output coupled to a signal input of the third amplifier  664  and also has an output coupled to the arithmetic encoding block  518 . The third amplifier  664  also has a control input coupled to the noise shaping analysis block  514 . The output of the third amplifier  664  is coupled to an input of the second addition stage  666 . The other input of the second addition stage  666  is coupled to the output of the fifth addition stage  690  in the prediction filter  672 . The output of the second addition stage  666  is coupled to the inputs of the short-term prediction block  696  and fifth subtraction stage  694  in the prediction filter  672 , and of the second short-term shaping filter  688  and fourth subtraction stage  686  in the synthesis shaping filter  670 . The signal output from the second addition stage  666  is the quantized output y(n) fed back to the analysis, synthesis and prediction filters. 
         [0125]    In the analysis shaping filter  668 , the first short-term shaping block  680  and third subtraction stage  678  each have inputs arranged to receive the input signal x HP (n). The output of the first short-term shaping block  680  is coupled to the other input of the third subtraction stage  678  and an input of the third addition stage  674 . The output of the third subtraction stage  678  is coupled to the input of the first long-term shaping block  676 , and the output of the first short-term shaping block  676  is coupled to the other input of the third addition stage  674 . The first short-term and long-term shaping blocks  680  and  676  are each also coupled to the noise shaping analysis block  514 , and the first long-term shaping block  676  is further coupled to the open-loop pitch analysis block  508  (connections not shown). In the synthesis shaping filter  670 , the second short-term shaping block  688  and the fourth subtraction stage  686  each have inputs arranged to receive the quantized output signal y(n) from the output of the second addition stage  666 . 
         [0126]    The output of the second short-term shaping block  688  is coupled to the other input of the fourth subtraction stage  686 , and to an input of the fourth addition stage  682 . The output of the fourth subtraction stage  686  is coupled to the input of the second long-term shaping block  684 , and the output of the second long-term shaping block  684  is coupled to the other input of the fourth addition stage  682 . The second short-term and long-term shaping blocks  688  and  684  are each also coupled to the noise shaping analysis block  514 , and the second long-term shaping block  684  is further coupled to the open-loop pitch analysis block  508  (connections not shown). In the prediction filter  672 , the short-term prediction block  696  and fifth subtraction stage  694  each have inputs arranged to receive the quantized output signal y(n) from the output of the second addition stage  666 . The output of the short-term prediction block  696  is coupled to the other input of the fifth subtraction stage  694 , and to an input of the fifth addition stage  690 . The output of the fifth subtraction stage  694  is coupled to the input of the long-term prediction block  692 , and the output of the long-term prediction block is coupled to the other input of the fifth addition stage  690 . 
         [0127]    In operation, the noise shaping quantizer  516  generates a quantized output signal y(n) that is identical to the output signal ultimately generated in the decoder. The output of the analysis shaping filter  668  is subtracted from the input signal x(n) at the first subtraction stage  652 . At the first amplifier  654 , the result is multiplied by the compensation gain G computed in the noise shaping analysis block  514 . Then the output of the synthesis shaping filter  670  is added at the first addition stage  656 , and the output of the prediction filter  672  is subtracted at the second subtraction stage  658  to create a residual signal. At the second amplifier  660 , the residual signal is multiplied by the inverse quantized quantization gain from the noise shaping analysis block  514 , and input to the quantization unit  662 , in one or more embodiments, a scalar quantizer. The quantization indices of the quantization unit form a signal that is input to the arithmetic encoder  518  for transmission to a decoder in an encoded signal. The quantization unit  662  also outputs a quantization signal, which is multiplied at the third amplifier  664  by the quantized quantization gain from the noise shaping analysis block  514  to create an excitation signal. The output of the prediction filter  672  is added to the excitation signal to form the quantized output signal y(n). The quantized output signal is fed back to the prediction filter  672  and synthesis shaping filter  670 . 
         [0128]    The analysis shaping filter  668  inputs the input signal x HP (n) to a short-term analysis shaping filter (the first short term shaping block  680 ), which uses the short-term analysis shaping coefficients a shape,ana  to create a short-term analysis shaping signal s short,ana (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 
                   short 
                   , 
                   ana 
                 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 16 
               
                
               
                 
                   
                     x 
                     HP 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       a 
                       
                         shape 
                         , 
                         ana 
                       
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0129]    The short-term analysis shaping signal is subtracted from the input signal x HP (n) at the third subtraction stage  678  to create an analysis shaping residual signal f ana (n). The analysis shaping residual signal is input to a long-term analysis shaping filter (the first long-term shaping block  676 ) which uses the long-term shaping coefficients b shape,ana  to create a long-term analysis shaping signal s long,ana (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 
                   long 
                   , 
                   ana 
                 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   
                     - 
                     2 
                   
                 
                 2 
               
                
               
                 
                   
                     f 
                     ana 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       lag 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       b 
                       
                         shape 
                         , 
                         ana 
                       
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0130]    The short-term and long-term analysis shaping signals are added together at the third addition stage  674  to create the analysis shaping filter output signal. 
         [0131]    The synthesis shaping filter inputs  670  the quantized output signal y(n) to a short-term shaping filter (the second short-term shaping block  688 ), which uses the short-term synthesis shaping coefficients a shape,syn  to create a short-term synthesis shaping signal s short,syn (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 
                   short 
                   , 
                   syn 
                 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 16 
               
                
               
                 
                   y 
                    
                   
                     ( 
                     
                       n 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       a 
                       
                         shape 
                         , 
                         syn 
                       
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0132]    The short-term synthesis shaping signal is subtracted from the quantized output signal y(n) at the fourth subtraction stage  686  to create an synthesis shaping residual signal f syn (n). The synthesis shaping residual signal is input to a long-term synthesis shaping filter (the second long-term shaping block  684 ) which uses the long-term shaping coefficients b shape,syn  to create a long-term synthesis shaping signal s long,syn (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 s 
                 
                   long 
                   , 
                   syn 
                 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   
                     - 
                     2 
                   
                 
                 2 
               
                
               
                 
                   
                     f 
                     syn 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       lag 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       b 
                       
                         shape 
                         , 
                         syn 
                       
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0133]    The short-term and long-term synthesis shaping signals are added together at the fourth addition stage  682  to create the synthesis shaping filter output signal. 
         [0134]    The prediction filter  672  inputs the quantized output signal y(n) to a short-term predictor (the short term prediction block  696 ), which uses the quantized LPC coefficients a Q  to create a short-term prediction signal p short (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 p 
                 short 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 16 
               
                
               
                 
                   y 
                    
                   
                     ( 
                     
                       n 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       a 
                       Q 
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0135]    The short-term prediction signal is subtracted from the quantized output signal y(n) at the fifth subtraction stage  694  to create an LPC excitation signal e LPC (n): 
         [0000]    
       
         
           
             
               
                 e 
                 LPC 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 
                   y 
                    
                   
                     ( 
                     n 
                     ) 
                   
                 
                 - 
                 
                   
                     p 
                     short 
                   
                    
                   
                     ( 
                     n 
                     ) 
                   
                 
               
               = 
               
                 
                   y 
                    
                   
                     ( 
                     n 
                     ) 
                   
                 
                 - 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     16 
                   
                    
                   
                     
                       y 
                        
                       
                         ( 
                         
                           n 
                           - 
                           i 
                         
                         ) 
                       
                     
                      
                     
                       
                         
                           a 
                           Q 
                         
                          
                         
                           ( 
                           i 
                           ) 
                         
                       
                       . 
                     
                   
                 
               
             
           
         
       
     
         [0136]    The LPC excitation signal is input to a long-term predictor (long term prediction block  692 ) which uses the quantized long-term prediction coefficients b Q  to create a long-term prediction signal p long (n), according to the formula: 
         [0000]    
       
         
           
             
               
                 p 
                 long 
               
                
               
                 ( 
                 n 
                 ) 
               
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   
                     - 
                     2 
                   
                 
                 2 
               
                
               
                 
                   
                     e 
                     LPC 
                   
                    
                   
                     ( 
                     
                       n 
                       - 
                       lag 
                       - 
                       i 
                     
                     ) 
                   
                 
                  
                 
                   
                     
                       b 
                       Q 
                     
                      
                     
                       ( 
                       i 
                       ) 
                     
                   
                   . 
                 
               
             
           
         
       
     
         [0137]    The short-term and long-term prediction signals are added together at the fifth addition stage  690  to create the prediction filter output signal. 
         [0138]    The LSF indices, LTP indices, quantization gains indices, pitch lags, and excitation quantization indices are each arithmetically encoded and multiplexed by the arithmetic encoder  518  to create the payload bitstream. The arithmetic encoder  518  uses a look-up table with probability values for each index. The look-up tables are created by running a database of speech training signals and measuring frequencies of each of the index values. The frequencies are translated into probabilities through a normalization step. 
         [0139]    A predictive speech decoder  700  for use in decoding such a signal is now discussed in relation to  FIGS. 7   a  and  7   b.    
         [0140]    The decoder  700  comprises an arithmetic decoding and dequantizing block  702 , an excitation generation block  704 , an LTP synthesis filter  706 , and an LPC synthesis filter  708 . The arithmetic decoding and dequantizing block has an input arranged to receive an encoded bitstream from an input device such as a wired modem or wireless transceiver, and has outputs coupled to inputs of each of the excitation generation block  704 , LTP synthesis filter  706  and LPC synthesis filter  708 . The excitation generation block  704  has an output coupled to an input of the LTP synthesis filter  706 , and the LTP synthesis filter  706  has an output connected to an input of the LPC synthesis filter  708 . The LPC synthesis filter has an output arranged to provide a decoded output for supply to an output device such as a speaker or headphones. 
         [0141]    At the arithmetic decoding and dequantizing block  702 , the arithmetically encoded bitstream is demultiplexed and decoded to create LSF indices, LTP indices, quantization gains indices, pitch lags and a signal of excitation quantization indices. The LSF indices are converted to quantized LSFs by adding the codebook vectors of the ten stages of the MSVQ. The quantized LSFs are transformed to quantized LPC coefficients. The LTP indices are converted to quantized LTP coefficients. The gains indices are converted to quantization gains, through look ups in the gain quantization codebook. 
         [0142]    The quantization indices are input to the excitation generator  704  which generates an excitation signal. The excitation quantization indices are multiplied with the quantized quantization gain to produce the excitation signal e(n). 
         [0143]    The excitation signal e(n) is input to the LTP synthesis filter  706  to create the LPC excitation signal e LPC (n). Here, the output of a long term predictor  710  in the LTP synthesis filter  708  is added to the excitation signal, which creates the LPC excitation signal e LPC (n) according to: 
         [0000]    
       
         
           
             
               
                 
                   e 
                   LPC 
                 
                  
                 
                   ( 
                   n 
                   ) 
                 
               
               = 
               
                 
                   e 
                    
                   
                     ( 
                     n 
                     ) 
                   
                 
                 + 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       
                         - 
                         2 
                       
                     
                     2 
                   
                    
                   
                     
                       e 
                        
                       
                         ( 
                         
                           n 
                           - 
                           lag 
                           - 
                           i 
                         
                         ) 
                       
                     
                      
                     
                       
                         b 
                         Q 
                       
                        
                       
                         ( 
                         i 
                         ) 
                       
                     
                   
                 
               
             
             , 
           
         
       
     
         [0000]    using the pitch lag and quantized LTP coefficients b Q . 
         [0144]    The LPC excitation signal is input to the LPC synthesis filter  708 , in one or more embodiments, a strictly causal MA filter controlled by the pitch lag and quantized LTP coefficients, to create the decoded speech signal y(n). Here, the output of a short term predictor  712  in the LPC synthesis filter  708  is added to the LPC excitation signal, which creates the quantized output signal according to: 
         [0000]    
       
         
           
             
               
                 y 
                  
                 
                   ( 
                   n 
                   ) 
                 
               
               = 
               
                 
                   
                     e 
                     LPC 
                   
                    
                   
                     ( 
                     n 
                     ) 
                   
                 
                 + 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     16 
                   
                    
                   
                     
                       
                         e 
                         LPC 
                       
                        
                       
                         ( 
                         
                           n 
                           - 
                           i 
                         
                         ) 
                       
                     
                      
                     
                       
                         a 
                         Q 
                       
                        
                       
                         ( 
                         i 
                         ) 
                       
                     
                   
                 
               
             
             , 
           
         
       
     
         [0000]    using the quantized LPC coefficients a Q . 
         [0145]    The encoder  500  and decoder  700  are, in one or more embodiments, implemented in software, such that each of the components  502  to  518 ,  652  to  696 , and  702  to  712  comprise modules of software stored on one or more memory devices and executed on a processor. An example application of the described embodiments is to encode speech for transmission over a packet-based network such as the Internet, using a peer-to-peer (P2P) system implemented over the Internet, for example as part of a live call such as a Voice over IP (VoIP) call. In this case, the encoder  500  and decoder  700  are, in one or more embodiments, implemented in client application software executed on end-user terminals of two users communicating over the P2P system. 
         [0146]    It will be appreciated that the above embodiments are described only by way of example. For instance, some or all of the modules of the encoder and/or decoder could be implemented in dedicated hardware units. Further, the various embodiments are not limited to use in a client application, but could be used for any other speech-related purpose such as cellular mobile telephony. Further, instead of a user input device like a microphone, the input speech signal could be received by the encoder from some other source such as a storage device and potentially be transcoded from some other form by the encoder; and/or instead of a user output device such as a speaker or headphones, the output signal from the decoder could be sent to another source such as a storage device and potentially be transcoded into some other form by the decoder. Other applications and configurations may be apparent to the person skilled in the art given the disclosure herein.

Technology Classification (CPC): 6