Classifications

• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
  • G10L19/032—Quantisation or dequantisation of spectral components
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L19/002—Dynamic bit allocation
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
  • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
  • G10L19/0204—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders using subband decomposition

Definitions

• the present invention relates generally to the perceptual coding of digital audio signals that uses analysis filters for encoding and synthesis filters for decoding.
• the present invention relates more particularly to the quantization of subband signals in perceptual coders that takes into account the spreading of quantization noise by the synthesis filters.
• Perceptual coding systems attempt to achieve these conflicting goals by using a process that encodes and quantizes the audio signals in a manner that uses larger spectral components within the audio signal to mask or render inaudible the resultant quantizing noise.
• a perceptual encoding process may be performed by a so called split -band encoder that applies a bank of analysis filters to the audio signal to obtain subband signals having bandwidths that are commensurate with the critical bands of the human auditory system, estimates the masking threshold of the audio signal by applying a perceptual model to the subband signals or to some other measure of audio signal spectral content, establishes a quantization resolution for quantizing each subband signal that is just small enough so that the resultant quantizing noise lies just below the estimated masking threshold of the audio signal, and generates an encoded signal by assembling the quantized subband signals into a form suitable for transmission or storage.
• a complementary perceptual decoding process may be performed by a split- band decoder that extracts the quantized subband signals from the encoded signal, obtains dequantized representations of the quantized subband signals, and applies a bank of synthesis filters to the dequantized representations to generate an audio signal that is, ideally, perceptually indistinguishable from the original audio signal
• the perceptual models that are often used to determine the quantization resolution generally assume that the quantization noise introduced into the quantized subband signals is substantially the same as the noise that results in the output signal obtained by applying a bank of synthesis filters to the quantized subband signals In general, this assumption is not true because the synthesis filters modify or spread the quantization noise spectrum As a consequence, quantization performed strictly according to the quantization resolutions obtained by applying these perceptual models usually results in audible noise in the output signal obtained from the synthesis filters
• the subband signals each comprise a group of one or more frequency-domain transform coefficients
• the synthesis filter noise-spreading property mentioned above is related to the fact that the complementary analysis and synthesis filters used in these coding systems do not implement ideal filters having a flat unitary-gain in the passband, zero- gain in the stopbands, and infinitely steep transitions between the stopbands and the passband As a consequence, the analysis filters provide only a distorted measure of the spectral content of an input audio signal Furthermore, some filters such as the quadrature mirror filter (QMF) and the time-domain aliasing cancellation (TDAC) transforms generate significant aliasing artifacts that further distort the spectral measure of the input signal In principle, these artifacts and deviations from perfect filters can be ignored because complementary pairs of analysis and synthesis filters can be used in which the synthesis filters are able to reverse the distortions of the analysis filter and perfectly reconstruct the original input signal Although perfect reconstruction is possible in principle, it is not achieved in practical coding systems because perfect reconstruction requires the synthesis filters to receive a precise representation of the subband signals generated by the analysis filters Instead, the
• a method or apparatus determines quantization resolutions for subband signals obtained from analysis filters applied to an input signal by generating a desired noise spectrum in response to the input signal and applying a synthesis-filter noise-spreading model to obtain estimated noise levels in subbands of an output signal obtained from synthesis filters
• the synthesis-filter noise-spreading model represents noise-spreading characteristics of the synthesis filters and the quantization resolutions are determined such that a comparison of the desired-noise spectrum with the estimated noise levels satisfies one or more comparison criteria
• the method may be embodied as a program of instructions on a medium that is readable by a device for execution by the device
• a medium conveys encoded information that comprises signal information that represents quantized components of subband signals generated by applying analysis filters to an input signal and control information that represents quantizing resolutions of the quantized subband signal components
• the quantizing resolutions are determined as summarized above
• an apparatus receives and decodes a
• Figs 1A and IB are block diagrams of split-band encoders
• Figs. 2A and 2B are block diagrams of split-band decoders
• Fig 3 is a schematic illustration of the frequency response for a hypothetical filter.
• Fig 4A is a schematic illustration of a perceptual masking threshold for a high-frequency spectral component as compared to the frequency response of Fig. 3.
• Fig 4B is a schematic illustration of a perceptual masking threshold for a medium- to low-frequency spectral component as compared to the frequency response of Fig. 3.
• Fig 5 is a block diagram of components illustrating concepts underlying some aspects of the present invention
• Fig 6 is a schematic illustration of overlapping blocks of time-domain samples recovered by an inverse block transform and weighted by a synthesis window function
• Fig 7 is a geometrical illustration of an optimization problem that seeks an optimum quantization resolution
• Fig 8 is a graphical illustration of a smoothed power spectrum, a desired noise spectrum, and a quantizing noise spectrum for a hypothetical audio signal
• Fig 9 is a flowchart illustrating steps in a reiterative process for determining quantization resolutions
• Fig 10 is a graphic illustration of values of the members in a central row of a spreading matrix
• Fig 11 is a block diagram of an apparatus that may be used to carry out various aspects of the present invention.
• Fig 1 A illustrates one embodiment of a split -band encoder incorporating various aspects of the present invention in which a bank of analysis filters 12 is applied to a digital audio signal received from path 11 to generate frequency-subband signals along path 13
• the bank of analysis filters may be implemented in a wide variety of ways
• the bank of filters is implemented by weighting or modulating overlapped blocks of digital audio samples with an analysis window function and applying a particular Modified Discrete Cosine Transform (MDCT) to the window-weighted blocks
• MDCT Modified Discrete Cosine Transform
• TDAC Time- Domain Aliasing Cancellation
• desired noise level calculator 14 analyzes the digital audio signal received from path 11 to estimate the psychoacoustic masking threshold of the audio signal and to obtain a desired noise level in response thereto
• the desired noise level is established at a level that is substantially equal to the psychoacoustic masking threshold that is obtained using a good perceptual model such as those disclosed in Schroeder, Atal and Hall, "Optimizing Digital Speech Coders by Exploiting Masking Properties of the Human Ear," J Acoust Soc Am , December 1979, pp 1647-1652 and in U S patent 5,623,577
• a good perceptual model such as those disclosed in Schroeder, Atal and Hall, "Optimizing Digital Speech Coders by Exploiting Masking Properties of the Human Ear," J Acoust Soc Am , December 1979, pp 1647-1652 and in U S patent 5,623,577
• quantize resolution calculator 15 uses a noise-spreading model to determine the quantization resolutions to use for quantizing the subband signals and passes an indication of these quantization resolutions along path 16
• the noise- spreading model represents the noise-spreading characteristics of a bank of synthesis filters and is used to estimate the noise in an output signal that is obtained by applying the synthesis filters to the subband signals that are quantized according to the quantization resolutions
• Quantize resolution calculator 15 determines the quantization resolutions such that, according to the noise-spreading model, the output signal obtained from the synthesis filters has a level of noise resulting from the quantization that is substantially equal to the desired noise level
• Quantizer 17 quantizes the subband signals received from path 13 according to the quantization resolution information received from path 16 to generate quantized signals along path 18
• Quantizer 17 may be implemented by a variety of quantization functions using uniform or non-uniform step sizes including linear quantization, logarithmic quantization, Lloyd-Max quantization and vector quantization The resolution of the quantization provided by
• the number of quantization steps is varied by allocating a number of bits and selecting a quantizer with a corresponding number of steps
• Formatter 19 assembles the quantized signals into an encoded signal and passes the encoded signal along path 20 to be conveyed by transmission media such as baseband or modulated communication paths throughout the spectrum including from supersonic to ultraviolet frequencies, or storage media including those that convey information using essentially any magnetic or optical recording technology including magnetic tape, magnetic disk, and optical disc.
• an indication of the signal characteristics used by desired noise level calculator 14 is passed along path 21 and assembled into the encoded signal.
• neither path 21 nor the information passed along path 21 are needed because an indication of the quantization resolutions used to generate the quantized signals is assembled into the encoded signal.
• Formatter 19 may also use an entropy encoder or other form of lossless encoder to reduce the information capacity requirements of the encoded signal.
• Fig. IB illustrates another embodiment of a split-band encoder incorporating various aspects of the present invention that is similar to the embodiment discussed above. A few of the differences between these two embodiments are discussed here.
• a bank of analysis filters 12 is applied to a digital audio signal received from path 1 1 to generate frequency-subband signals along path 13 and to generate information representing the input signal spectral envelope along path 22.
• subband signal components may be represented in a block-floating-point (BFP) form in which the BFP exponents are essentially logarithmic scaling factors representing the peak component value in each subband.
• the BFP exponents may be used as the input signal spectral envelope information.
• the bank of analysis filters may be implemented in a wide variety of ways as discussed above.
• Desired noise level calculator 14 analyzes the spectral envelope information received from path 22 to estimate the psychoacoustic masking threshold of the audio signal and to obtain a desired noise level in response thereto.
• quantize resolution calculator 15 uses a noise-spreading model as explained above to determine the quantization resolutions to use for quantizing the subband signals and passes an indication of these quantization resolutions along path 16.
• Quantizer 17 quantizes the subband signals received from path 13 according to the quantization resolution information received from path 16 to generate quantized signals along path 18
• Quantizer 17 may be implemented and controlled as discussed above
• Formatter 19 assembles the quantized signals received from path 18 and the spectral envelope information received from path 22 into an encoded signal and passes the encoded signal along path 20 as explained above
• Formatter 19 may also use an entropy encoder or other form of lossless encoder as discussed above
• Fig IB may be used in backward-adaptive coding systems because the information needed by the desired-noise-level calculator is conveyed in the encoded signal by the spectral envelope information No additional information is needed by a complementary decoder that incorporates counterpart components to desired noise level calculator 14 and quantize resolution calculator 15
• desired noise level calculator 14 provides a set of initial quantization resolutions and quantize resolution calculator 15 modifies one or more of these initial resolutions as necessary to carry out noise-spreading compensation according to the synthesis-filter noise-spreading model discussed above.
• An indication of these modifications is passed along path 23 and assembled into the encoded signal by formatter 19 By including this additional information, the encoded signal can be decoded without use of the synthesis-filter noise-spreading model
• Decoder Fig 2A illustrates one embodiment of a split-band decoder incorporating various aspects of the present invention in which deformatter 32 extracts quantized signals from an encoded signal received from path 31 and passes the quantized signals along path 33 Deformatter 32 may also use an entropy decoder or other form of lossless decoder as necessary to obtain the quantized signals
• deformatter 32 also extracts from the encoded signal an indication of the signal characteristics used by desired noise level calculator in a companion encoder and passes this indication to desired noise level calculator 34, which obtains the desired noise level in response thereto
• quantize resolution calculator 35 uses a noise-spreading model as explained above to determine the quantization resolutions that were used to generate the quantized signals and passes an indication of these resolutions along path 36
• Dequantizer 37 dequantizes the quantized signals received from path 33 according to the quantization resolution information received from path 36 and generates dequantized subband signals along path 38
• Dequantizer 37 may be implemented and controlled in a variety of ways as discussed above for quantization No particular dequantization function is critical in principle to the practice of the present invention but should be complementary to the quantization process used to generate the quantized subband signals
• a bank of synthesis filters 39 is applied to these dequantized subband signals to generate an output signal along path 40
• the bank of synthesis filters may be implemented in a wide variety of ways
• the bank of synthesis filters is implemented by applying an inverse MDCT, referred to as the inverse TDAC transform, to blocks of transform coefficients, weighting the signal samples obtained from the transform with a synthesis window function, and overlapping and adding samples in adjacent window-weighted blocks
• neither desired noise level calculator 34 nor quantize resolution calculator 35 are needed because deformatter 32 is able to extract quantization resolution information from the encoded signal and provide this information to quantizer 37.
• Fig. 2B illustrates another embodiment of a split-band decoder incorporating various aspects of the present invention that is similar to the embodiment discussed above A few of the differences between these two embodiments are discussed here
• Deformatter 32 extracts quantized signals from an encoded signal received from path 31 and passes the quantized signals along path 33, and extracts information representing the encoded signal spectral envelope and pass this information along path 42.
• Deformatter 32 may also use an entropy decoder or other form of lossless decoder as necessary to reverse any lossless coding used to generate the encoded signal
• Desired noise level calculator 34 analyzes the spectral envelope information received from path 42, which obtains the desired noise level in response thereto.
• quantize resolution calculator 35 uses a noise-spreading model as explained above to determine the quantization resolutions that were used to generate the quantized signals and passes an indication of these resolutions along path 36
• Dequantizer 37 dequantizes the quantized signals received from path 33 according to the quantization resolution information received from path 36 and generates dequantized subband signals along path 38
• Dequantizer 37 may be implemented and controlled as discussed above
• a bank of synthesis filters 39 is applied to the dequantized subband signals and the spectral envelope information to generate an output signal along path 40
• desired noise level calculator 34 provides a set of initial quantization resolutions and one or more modifications to these initial resolutions are obtained from the encoded signal by deformatter 32 These modifications may be applied to the initial quantization resolutions to provide noise-spreading compensation B.
• the quantization process in many perceptual coding systems determines the quantization resolution to use for quantizing a subband signal from the difference between the amplitude of the subband signal and the level of an estimated psychoacoustic masking threshold within that subband
• An implicit assumption in this process is that the quantization noise for one transform coefficient is independent of the quantization noise for other neighboring transform coefficients Generally, this assumption is not true because of the noise-spreading characteristics of the synthesis filters The degree of noise spreading is affected by the spectral selectivity of the synthesis filters
• the analysis and synthesis filters used in coding systems do not provide ideal passbands
• a schematic illustration of the frequency response for a hypothetical synthesis filter is shown in Fig 3
• the response shown in the figure is a frequency-domain representation of a hypothetical output signal obtained from the synthesis filter in response to an input signal having a single spectral component at frequency fo
• the main lobe 23 of the frequency response that is centered at frequency o is the filter passband
• This spectral selectivity may be controlled by varying a number of factors including the length of the inverse transform and the shape of the synthesis window function
• the width of the passband can often be traded off against the level of attenuation provided in the stopbands
• the attenuation in the stopbands is also reduced
• the spectral selectivity can also be increased by increasing the length of the transform, however, the use of longer transforms is not always possible In broadcast and other production applications that require real-time playback of the decoded signal, for example, a short length transform must be used to satisfy coding delay limitations
• the noise- spreading characteristics of synthesis filters is particularly serious in such coding systems Additional considerations for low-delay coding systems is discussed in U S patent 5,222,189
• noise-spreading is usually more serious for medium to low frequencies because the critical bands of the human auditory system are narrower at lower frequencies
• Each critical band corresponds to the masking threshold for a spectral component within that band and represents the range of frequencies over which a dominant spectral component can likely mask other smaller spectral components like quantization noise
• the masking threshold can become narrower than the frequency selectivity of the synthesis filter This means it is more likely the synthesis filter will spread noise resulting from the quantization of a spectral component outside the masking threshold of that spectral component
• Fig 4A provides a schematic illustration of a perceptual masking threshold 25 for a high-frequency spectral component at frequency /o as compared to the filter frequency response illustrated in Fig 3
• masking threshold 25 for the high- frequency spectral component at frequency /o is wide enough to completely cover the synthesis filter response This suggests that a relatively large amount of noise resulting from the quantization of the high-frequency spectral component at frequency o that is spread by the synthesis filter is likely to be masked by the spectral component
• Fig 4B provides a schematic illustration of a perceptual masking threshold 27 for a medium- to low-frequency spectral component at frequency fi as compared to the filter frequency response illustrated in Fig 3
• the low-frequency side of masking threshold 27 for the lower-frequency spectral component at frequency / 0 does not cover the synthesis filter response This suggests that only a relatively small amount of noise resulting from the quantization of the lower-frequency spectral component at frequency ⁇ that is spread by the synthesis filter is likely to be masked by the spectral component
• analysis filter 52 represents a bank of analysis filters in a split-band encoder that generates transform coefficients constituting a frequency- domain representation of the audio signal received from path 51
• Quantizing noise 53 represents a process that injects quantization noise into the frequency-domain representation obtained from analysis filter 52
• Synthesis transform 54 and overlap- add 55 collectively represent a bank of synthesis filters in a split-band decoder
• Synthesis transform 54 obtains a time-domain representation from the quantized frequency-domain representation of the audio signal
• overlap-add 55 overlaps adjacent blocks of samples in the time-domain representation obtained from synthesis transform 54 and adds corresponding samples in the overlapped blocks
• Analysis filter 56 is a theoretical construct that is used to explain some principles of the present invention
• the bank of analysis filters 52 is implemented by suitable analysis window functions and the TDAC MDCT and is applied to a sequence of blocks of audio signal samples that are received from path 51 to generate subband signals in the form of a sequence of blocks of transform coefficients
• X m (k) transform coefficient k in transform coefficient block m
• w A (ri) analysis window function at point n
• x m ( ⁇ ) signal sample n in signal sample block m
• « 0 a transform phase term required for aliasing cancellation
• k 0 a term which, for this particular TDAC transform, is equal to Vi, and
• Synthesis transform 54 is implemented by the TDAC inverse MDCT and suitable synthesis window functions, and is applied to the sequence of blocks of quantized transform coefficients to generate a sequence of blocks of time-domain samples This may be expressed as
• Overlap-add 55 recovers a replica of the audio signal samples received from path 51 by applying a synthesis window function to each block of time-domain samples that is obtained from synthesis transform 54, overlapping the windowed blocks and adding corresponding time-domain samples in the overlapped blocks
• the gain profile of a sequence of overlapping windowed blocks is shown in Fig 6
• Curve 41 illustrates the gain profile of a synthesis window function that is used to modulate a block of time-domain samples that is coextensive with line 44
• curves 42 and 43 illustrate the gain profiles of synthesis window functions that are used to modulate blocks of time-domain samples that are coextensive with lines 45 and 46, respectively
• Signal samples representing a replica of the original audio signal samples within the interval illustrated by line 45 are obtained from the overlap-add process by adding the corresponding time-domain samples in the overlapping windowed blocks 41, 42 and 43 This may be expressed as
• analysis and synthesis window functions should be selected to satisfy those constraints necessary to provide aliasing cancellation See the Princen paper cited above Additional information pertaining to analysis and synthesis window functions may be obtained from U S patent 5,222,189 and from international patent application number PCT/US 98/20751 filed October 17, 1998
• the bank of analysis filters 56 may be implemented by essentially any type of analysis filter For purposes of illustration, this bank of analysis filters is implemented by a rectangular analysis window function and the TDAC MDCT discussed above for analysis filters 52 The bank of analysis filters 56 is applied to the replica signal samples to obtain a hypothetical frequency-domain representation of the replica signal, which is passed along path 57 The frequency-domain representation is used as a basis for an analytical expression of the noise-spreading characteristics of the synthesis filters.
• the representation may be expressed as follows
• the hypothetical frequency-domain representation obtained from analysis filter 56 for this perfect reconstruction may be expressed as
• the quantization noise I m (k) for the various transform coefficients k are statistically independent.
• the quantization noise I m (k) for various coefficient blocks m are statistically independent.
• the quantization noise I m (k) in a respective coefficient block m have a mean that is equal to zero and have variances that are equal in consecutive coefficient blocks.
• the first two assumptions are true for the coefficients obtained from the transforms generally used in audio coding systems.
• the third assumption is true for blocks of transform coefficients representing a stationary signal and is justified for quasi- stationary passages of music that are not quantized well by known perceptual coding systems and methods. In highly non- stationary passages for which the third assumption is not justified, errors caused by this assumption are generally benign and can be ignored. 3.
• a process for quantization that takes proper account of synthesis filter noise spreading may be developed from an analytical expression of the relationship between the noise spectrum of the output signal obtained from the synthesis filter and the noise spectrum of the quantized input signal provided to the synthesis filter.
• This analytical expression or "spreading matrix" will now be described.
• First the expression for x m ( ) in equation 3 is substituted into equation 4, and the resulting expression for y m ( ⁇ ) is then substituted into equation 5 to obtain an expression for the hypothetical frequency-domain representation of the synthesis filter output signal in terms of the quantized transform coefficients, as follows
• O m (k) quantization noise in the synthesis filter output signal at frequency k
• I m (k) X m (k) - X m (k) for 0 ⁇ k ⁇ 2M, as may be seen from equation 2
• the matrices A, B and C have odd symmetry These properties may be used to show that
• equation 14 can be simplified to
• N 0m (k) ⁇ W(k,q)-N lm (q) ⁇ N(k) ⁇ o ⁇ 0 ⁇ k ⁇ M (16)
• the search for gain factor values that provide an optimal solution can be framed as a linearly constrained optimization problem that seeks to minimize the cost of the compensation
• the cost is equal to one bit per transform coefficient for each -6.02 dB the quantizing noise spectrum is changed
• gain factor g(l) is set equal to 0.25
• N/, m (l) of the quantizing noise spectrum is changed by -12 04 dB with respect to N(l) of the desired noise spectrum
• the desired quantization noise spectrum shown in equation 18 can be conveniently represented as
• the cost of compensation varies inversely with the logarithm of each gain factor.
• the total cost of compensation in this two-dimensional example is proportional to -log g(0) - log g(l)
• the constant of proportionality is assumed herein to be equal to one
• the goal of the optimization problem is to minimize the cost of compensation under the constraints imposed by expressions 19a,
• the first step in framing quantization as a linear optimization problem is to replace each N(j) ⁇ W( ⁇ , j) term in expressions 19a and 19b with an element D(i,j) of a matrix D All elements in matrix D are known to be positive because each element represents the product of two positive quantities The results of this replacement may be expressed as
• the optimization problem expressed in this manner can be illustrated geometrically in a g(0)-g(l) coordinate space as shown in Fig 7
• the region 60 of possible solutions to the optimization problem is restricted to a unit square in quadrant I of the coordinate space that has sides corresponding to the minimum and maximum values permitted for the two gain factors as shown in expression 21c
• the region on the side of straight line 61 that includes the origin represents the portion of the space that satisfies the inequality in expression 21a
• the region on the side of straight line 62 that includes the origin represents the portion of space that satisfies the inequality in expression 21b
• Solution space 66 represented by the intersection of these three regions, is the portion of the (0)- (l) coordinate space in which the solution for the optimization problem may be found that satisfies all of the conditions imposed by expressions 21a, 21b and 21c
• the boundary of solution space 66 is shown with a wide line that, in this example, forms an irregular quadrilateral with sides congruent with portions of the g(0) and g
• hyperbolic line 63 represents a contour for some cost of compensation K ⁇ and hyperbolic line 64 represents a contour for another cost of compensation that is higher than As the cost of compensation approaches infinity, the corresponding constant-cost contour approaches the two coordinate axes
• the goal of the optimization problem is to find a minimum- cost solution that satisfies expressions 21a, 21b and 21c
• the optimum solution may be obtained by finding the lowest-cost hyperbolic contour that intersects the solution space In the example shown in Fig 7, the optimum solution occurs at the point of tangency between hyperbolic contour 64 and the boundary of solution space 66 b)
• Higher Dimensions Practical perceptual coding systems and methods utilize filters that require the quantization process to solve an optimization problem that has many more dimensions than two This problem can be stated as finding the set of gain factors ⁇ g(k) ⁇ within the solution space that satisfies the inequalities
• the region of possible solutions is limited to a hypercube having vertices with coordinates corresponding to gain factors having values equal to either zero or one
• the solution space for the optimization problem is that portion of the hypercube that is between the coordinate axes and the hyperplanes closest to the origin
• the optimum minimum-cost solution is found at the point of tangency between a hyperbolic constant-cost hypersurface and the boundary of the solution space
• a substantially optimum set of quantization resolutions may be obtained in a reiterative process such as that shown in Fig 9
• Step 81 obtains a set of initial quantization resolutions and step 82 applies a synthesis-filter spreading model to the initial resolutions to calculate the resultant noise levels
• Step 83 compares the calculated resultant noise levels with the desired noise levels If the results of the comparison are not acceptable, step 84 modifies the quantization resolutions appropriately and step 82 applies the noise- spreading model to the modified resolutions For example, if the calculated resultant noise level for a signal component is too low, the quantization resolution for one or more signal components is made more coarse If the calculated resultant noise level for a signal component is too high, the quantization resolution for one or more signal components is made more fine This process continues until the results of the comparison performed in step 83 are acceptable Subsequently, step 85 quantizes signal components according to the quantization resolutions that provided the acceptable comparison
• any set of initial quantization resolutions may be used, however, processing efficiency is generally improved by choosing initial resolutions that are close to the optimum values
• One convenient choice for the initial resolutions are those resolutions that correspond to the desired noise levels
• a quantization process may be carried out by a bit-allocation process that performs the following steps
• bit allocation process continues by defining the unit hypercube according to expression 24 Find the intersection of the regions in hyperspace that satisfy the inequalities of expression 23 This may be accomplished more efficiently by including only the hyperplanes defined by the rows in matrix D that are closest to the origin The distance d for each hyperplane can be determined from
• One hyperplane may be closest to the origin in part of the hyperspace and one or more other hyperplanes may be closest to the origin in other parts of the hyperspace
• Select an initial compensation cost K Determine whether the constant-cost hyperbolic hypersurface for cost K intersects the solution hyperspace determined in step 5 If the hyperbolic hypersurface for cost K is tangent to the boundary of the solution hyperspace, the bit allocation is complete
• the number of additional bits required for each transform coefficient X(k) to provide an optimum compensation for noise spreading is obtained from the negative logarithm of the respective gain factor For example, in one embodiment the bit allocation for each coefficient is
• a first simplified process uses a metric function to estimate the total noise level for each transform coefficient X(k) one at a time, starting with the lowest- frequency transform coefficient X(0), and determines whether noise spreading causes the total noise for that coefficient to exceed the desired noise level N(k). If the estimate indicates the total noise level for the current coefficient X(k) does not exceed the desired noise level, the process continues with the next higher-frequency transform coefficient.
• the coefficient that makes the largest contribution to the noise level of coefficient X(k) is identified and the gain factor g(k) for that coefficient is set to a prescribed value, say -144 dB which in one embodiment represents a compensation of 24 bits.
• the metric function is used to estimate the total noise level for coefficient X(k) that results with the adjusted bit allocation. If the estimated noise level still exceeds the desired noise level N(k), the coefficient making the next largest contribution to the noise level of coefficient X(k) is identified, its gain factor is set to the prescribed value, and the metric function is used again to estimate the new noise level. This continues until the estimated noise level is reduced to a level at or below the desired noise level.
• a main for-loop constitutes the remainder of the Compensate routine and carries out the compensation process for each of the low-frequency coefficients of interest
• the Null function is invoked to initialize an array S to an empty or null state
• the variable metric is assigned an estimate of the noise level for the current coefficient k by invoking the function Sum to calculate the sum
• the function Max is invoked to determine the coefficient k_max that makes the largest contribution to the noise for coefficient k This is accomplished by finding the index i that corresponds to the maximum value for the product W[k, i] * g[i] * N[i] for i from 0 to M2-1 This range for the index i includes all transform coefficients for the system If desired, processing efficiency can be improved by limiting the search for the maximum product to a narrower range of coefficients This range can be determined empirically When the maximum contributor is found, the gain factor for k_max is assigned a prescribed value max_correction that corresponds to some maximum amount of compensation In one embodiment, the maximum amount of compensation is -144 dB, which corresponds to 24 bits After invoking the function Union to add k_max to the array S, an estimate of the noise level is calculated again using the revised gain factor for k_max and is assigned to the variable metric The while-
• Each gain factor for the coefficients represented in array S is set to the tentative value g_new if the tentative value is less than the current value of the respective gain factor
• the main for-loop in the compensation process continues with the next transform coefficient until all coefficients of interest have been processed
• One variation attains a significant reduction in computational complexity by recognizing that a few elements in a typical spreading matrix array W are significantly larger than all other elements, and that good performance can be realized even when many of these smaller elements are set to zero
• Fig 10 illustrates the values of the elements in the center row of a hypothetical spreading matrix
• the dominant value in the center corresponds to the element on the main diagonal of the matrix Elements on and near the main diagonal have values that are significantly larger than those elements that are away from the main diagonal
• This characteristic allows the spreading matrix to be represented reasonably well by a sparse diagonal-band array and the values for LI and L2 in the program fragment discussed above can be reduced to cover only the non-zero elements of the array
• This characteristic also reduces the range over which a search is made for maximum contributors
• Another variation improves processing efficiency by eliminating the while- loop in the embodiment discussed above Efficiency is improved by eliminating a reiterative process in which the maximum noise contributor is determined and a tentative new value for the gain factors is calculated
• An embodiment of this variation is shown in the following program fragment
• the main for-loop constitutes the remainder of the routine and carries out the compensation process for each of the low-frequency coefficients of interest
• the variable metric is assigned a value estimating the noise level for the current coefficient k as described above
• the bit allocation for one or more transform coefficients in increased to account for noise spreading by finding the largest contributor k_max to the estimated noise and by applying a predetermined amount of correction to transform coefficient k_max and a few neighboring coefficients
• the maximum contributor is determined by invoking the function Max, as described above, and the predetermined corrections are applied by reducing the values of the gain factors for coefficients -LI to L2 by multiplying each gain factor by a respective value in the array comp
• the gain factor g[k_max] may be reduced to indicate a 2-bit increase in allocation
• the gain factors g[k_max-l] and g[k_max+l] may be reduced to indicate a 1 5-bit increase in allocation
• the gain factors g[k_max-2] and g[k_max+2] may be reduced to indicate a 1-bit increase in allocation
• the degree of predefined correction may be determined empirically for each application
• est_noise LogAdd( est_noise, contrib [j-k+L] ), //add log values ⁇
• a second simplified process provides noise-spreading compensation in two steps
• the first step determines an initial amount of compensation by taking each respective transform coefficient X(k) one at a time, starting with the lowest- frequency coefficient X(0), identifying the neighboring coefficients X(j) that make individual contributions to the estimated noise level of the respective coefficient that exceed the desired noise level N(k) for that coefficient, and determining the initial amount of compensation for those neighboring coefficients X(j) such that their respective individual contributions are reduced to the desired noise level
• the second step reiteratively refines the compensation to bring the total noise contribution for each respective transform coefficient to the desired noise level.
• the routine Compensate is provided with the array W and the array N as described above
• An array compN of compensation values is initialized from the array N of desired noise and a variable compOK is initialized so that the following while-loop executes at least once
• the while-loop constitutes the remainder of the Compensate routine and carries out the compensation process in two steps
• the loop first initializes the variable so that the while-loop will terminate unless excessive level noise is calculated in the second step
• the portion of the routine that performs the first step initializes an array tempN of temporary calculations and executes a for-loop in which the noise contributions to each coefficient k is examined one at a time
• a nested for-loop is used to calculate the estimated noise contribution W[k, j] * tempN[j] and determine if it is the maximum contribution calculated thus far If not, the nested loop continues with the next coefficient j If this estimated noise contribution is the largest level calculated thus far, the variables
• the portion of the routine that performs the second step calculates an estimate of the total noise for each coefficient k and compares this estimate with the desired noise level N[k] If the estimate exceeds the desired noise level, compensation compN[k] for the respective coefficient k is reduced by the same amount the desired noise level is exceeded by the estimated total noise
• the variable compOK is set so that the first and second steps are performed again
• this routine requires lower computational resources because the for-loop that identifies the maximum contributor max_contrib to the noise for a given coefficient j examines a narrow band of neighboring coefficients on either side of coefficient j from j-Ll to j+L2, excluding the coefficient j itself, rather than examine the entire spectrum as is done in the program fragment discussed above.
• Fig. 11 is a block diagram of device 90 that may be used to implement various aspects of the present invention.
• DSP 92 provides computing resources.
• RAM 93 is system random access memory (RAM).
• ROM 94 represents some form of persistent storage such as read only memory (ROM) for storing programs needed to operate device 90 and to carry out various aspects of the present invention.
• I/O control 95 represents interface circuitry to receive and transmit audio signals by way of communication channel 96.
• Analog-to-digital converters and digital-to-analog converters may be included in I/O control 95 as desired to receive and/or transmit analog audio signals.
• bus 91 which may represent more than one physical bus; however, a bus architecture is not required to implement the present invention.
• additional components may be included for interfacing to devices such as a keyboard or mouse and a display, and for controlling a storage device having a storage medium such as magnetic tape or disk, or an optical medium.
• the storage medium may be used to record programs of instructions for operating systems, utilities and applications, and may include embodiments of programs that implement various aspects of the present invention.
• the functions required to practice various aspects of the present invention can be performed by components that are implemented in a wide variety of ways including discrete logic components, one or more ASICs and/or program-controlled processors. The manner in which these components are implemented is not important to the present invention.
• Software implementations of the present invention may be conveyed by a variety machine readable media such as baseband or modulated communication paths throughout the spectrum including from supersonic to ultraviolet frequencies, or storage media including those that convey information using essentially any magnetic or optical recording technology including magnetic tape, magnetic disk, and optical disc.
• machine readable media such as baseband or modulated communication paths throughout the spectrum including from supersonic to ultraviolet frequencies, or storage media including those that convey information using essentially any magnetic or optical recording technology including magnetic tape, magnetic disk, and optical disc.
• Various aspects can also be implemented in various components of computer system 90 by processing circuitry such as ASICs, general-purpose integrated circuits, microprocessors controlled by programs embodied in various forms of read-only memory (ROM) or RAM, and other techniques.

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