WO2011000408A1

WO2011000408A1 - Audio coding

Info

Publication number: WO2011000408A1
Application number: PCT/EP2009/058165
Authority: WO
Inventors: Mikko Tapio Tammi; Lasse Juhani Laaksonen; Adriana Vasilache; Anssi Sakari RÄMÖ
Original assignee: Nokia Corporation
Priority date: 2009-06-30
Filing date: 2009-06-30
Publication date: 2011-01-06

Abstract

A method including: processing a selected subset of a higher series of samples forming a higher frequency spectral band of an audio signal and a lower series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the higher series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

Description

TITLE

Audio coding

FIELD OF THE INVENTION

Embodiments of the present invention relate to audio coding. In particular, they relate to coding high frequencies of an audio signal utilizing the low frequency content of the audio signal. BACKGROUND TO THE INVENTION

Audio encoding is commonly employed in apparatus for storing or transmitting a digital audio signal. A high compression ratio enables better storage capacity or more efficient transmission through a channel. However, it is also important to maintain the perceptual quality of the compressed signal.

There may be good correlation between a low frequency region and a higher frequency region of an audio signal. This may be utilized for example by using a bandwidth extension technique, which instead of encoding the signal of the high frequency region aims to model the high frequency region by using a copy of a signal at the low frequency region and adjusting the copied spectral envelope to match the high frequency region., Another example is spectral band replication (SBR) coding, which proposes that a higher frequency spectral band should not itself be

coded/decoded but should be replicated based on a pre-selected segment from a decoded lower frequency spectral band. However, these methods only try to maintain the overall shape of the spectral envelope at the high frequency region, whereas the fine structure of the original spectrum, which may be quite different is not considered.

An intermediate form between conventional spectral coding and bandwidth extension is to adaptively copy selected portions of lower frequency spectral band to model the higher frequency spectral band. WOO7072088 teaches dividing the higher frequency spectral band into smaller spectral sub bands. During encoding, systematic searches are used to find the portions of the larger lower frequency spectral band of the audio signal that are most similar to the smaller higher frequency spectral sub bands. A higher frequency spectral sub band can then be parametrically encoded by providing a parameter that identifies the most similar portion of the larger lower frequency spectral band. The searches are computationally intensive. At decoding, the provided parameter is used to replicate the appropriate portions of the lower frequency spectral band in the appropriate higher frequency spectral sub bands.

BRIEF DESCRIPTION OF VARIOUS EMBODIMENTS OF THE INVENTION

According to various, but not necessarily all, embodiments of the invention there is provided a method comprising: processing a selected subset of a higher series of samples forming a higher frequency spectral band of an audio signal and a lower series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the higher series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

The use of a selected subset of a higher series of samples forming a higher frequency spectral band of an audio signal advantageously reduces the processing load. According to various, but not necessarily all, embodiments of the invention there is provided a system comprising: an encoding apparatus configured to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying, using a parameter, a sub-series of the lower series of samples; and a decoding apparatus configured to replicate the series of samples forming the higher frequency spectral band using the sub-series of the lower series of samples identified by the parameter. According to various, but not necessarily all, embodiments of the invention there is provided an apparatus comprising: circuitry configured to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples. According to various, but not necessarily all, embodiments of the invention there is provided an apparatus comprising: processing means for processing a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

According to various, but not necessarily all, embodiments of the invention there is provided a computer program which when run on a processor enables the processor to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

According to various, but not necessarily all, embodiments of the invention there is provided a computer program which when run on a processor enables the processor to select a subset of a higher series of samples in the frequency domain that form a higher frequency spectral band of an audio signal; process the selected subset of the higher series of samples and a lower series of samples in the frequency domain forming a lower frequency spectral band of the audio signal to select a sub-series of the lower series of samples; and parametrically encode the higher series of samples by identifying the selected sub-series of the lower series of samples. According to various, but not necessarily all, embodiments of the invention there is provided a module comprising: circuitry configured to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

BRIEF DESCRIPTION OF THE DRAWINGS For a better understanding of various examples of embodiments of the present invention reference will now be made by way of example only to the accompanying drawings in which:

Fig 1 schematically illustrates an audio encoding apparatus;

Fig 2 schematically illustrates a parametric coding block;

Fig 3A schematically illustrates an illustrative example of a higher series of samples;

Fig 3B schematically illustrates an illustrative example of a subset of the higher series of samples;

Fig 4 schematically illustrates a system comprising an audio encoding apparatus and an audio decoding apparatus;

Fig 5 schematically illustrates a controller;

Fig 6 schematically illustrates a computer readable physical medium; and

Fig 7 schematically illustrates a method of processing a selected subset of a higher series of samples and a lower series of samples to parametrically encode the higher series of samples by identifying a sub-series of the lower series of samples.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS OF THE INVENTION

Fig 1 schematically illustrates an audio encoding apparatus 2. The audio encoding apparatus 2 processes digital audio 3 to produce encoded data 5 that represents the digital audio using less information. The information content of the digital audio signal 3 is compressed to encoded data 5.

Fig 4 illustrates the audio encoding apparatus 2 in a system 8 that also comprises an audio decoding apparatus 4. The audio decoding apparatus 4 processes the encoded data 5 to produce digital audio 7. Although the digital audio 7 comprises less information than the original digital audio 3, the encoding and decoding processes are designed to maintain perceptually high quality audio. This may, for example, be achieved by using a psychoacoustic model for encoding/decoding a lower frequency spectral band of the digital audio and using a coding technique making use of the lower frequency spectral band for encoding/decoding a higher spectral band. Referring back to Fig 1 , the audio encoding apparatus 2 comprises: a transformer block 10 for converting the digital audio 3 from the time domain into the frequency domain, an audio coding block 12 for encoding a lower frequency spectral band of the digital audio; and one or more parametric coding blocks 14 for parametrically encoding one or more higher frequency spectral bands of the digital audio.

Transformer

The transformer 10 receives as input the time domain digital audio 3 and produces as output a series X of N samples representing the spectrum of the digital audio.

A lower series X_L (k) o1 the N samples k=1 , 2...L represents a lower frequency spectral band of the digital audio. One or more higher series X_H ^} (k) of the N samples, where j = 1 , ..., M, and where k=0, 1 , 2... n, represent one or more higher frequency spectral bands of the digital audio, ri_j may be a constant or some function of j.

The boundaries of the lower series X_L (k) and the one or more higher series X_H ^J (k) may overlap in some embodiments and not overlap in other embodiments. In the following described embodiments they do not overlap.

The boundaries of the one or more higher series X_H' (k) may overlap in some embodiments and not overlap in other embodiments. In the following described embodiments they do not overlap.

The size n_s of a higher series X_H' (k) of samples may be less than the size L of the lower series X_L (k) of samples e.g. n, < L for all j. The whole of the series X may be spanned by the lower series X_L (k) and the one

M

or more higher series X_H' (k) e.g. N= L + ^ n_} .

;=i The transformer block 10 may use a modified discrete cosine transform. Other tranforms which represent signal in frequency domain with real-valued coefficients, such as discrete sine transform, can be utilized as well. Audio coding

The audio encoding block 12 in this example may use a psychoacoustic model to encode the lower series of samples X_L (k) \o produce encoded audio 13. The encoded audio may be a component of the encoded data 5.

The audio encoding block 12 may also decode the encoded audio 13 to produce a synthesized lower series X_L(k) which represents the lower series of samples X_L (k) available at a decoding apparatus 4. The synthesized lower series X₁(Jk) may be psycho-acoustically equivalent to the lower series of samples X_L (k) . In some embodiments the synthesized lower series X_L(Jk) may be psycho-acoustically as similar as possible to the lower series of samples X_L (k) , given the constraints imposed for example to bit-rate of encoded data, processing resources used by the encoding process, etc. Coding higher frequencies

The parametric coding blocks 14, parametrically encode the higher frequency spectral bands x_H ^] (k) of the digital audio. The output of each of the parametric coding blocks 14, is a set of parameters representing the higher frequency band 15,. The parameters representing the higher frequency band15,. may be components of the encoded data 5. An example of a parametric coding block 14 is schematically illustrated in Fig 2.

One input to the coding block 14, is the higher series X_H ^J (k) of samples representing the higher frequency spectral band j of the digital audio.

Another input to the coding block 14, is the lower series of samples representing the lower frequency spectral band of the digital audio. The input lower series of samples may be in some embodiments the original lower series of samples X_L (k) . In other embodiments it may be the synthesized lower series of samples X_L (k) . Let us assume for the purpose of the description of this example that the lower series of samples representing the lower frequency spectral band of the digital audio is the synthesized lower series of samples X₁(Jc) .

Referring to Fig 2, the parametric coding block 14, may comprise a subset selection block 20 for selecting a subset X_H ^] (Jc) of the higher series of samples X_H ^] (Jc) and a sub-series selection block 22 for selecting a sub-series of the lower series of samples X_L(Jc) that is suitable for coding the higher series of samples X_H ^} (k) .

The selection of a subset X_H' (Jc) of the higher series of samples x_H' (k) and the use of that subset x_H ^] (Jc) in determining the sub-series of the lower series of samples X_L(k) significantly reduces the number of calculations required compared to if , instead of using a subset X^ (Jc) of the higher series of samples X_H ³ (k) , the whole higher series of samples x_H' (k) is used to determine the sub-series of the lower series of samples X_L(k) .

Subset selection Fig 3A schematically illustrates an illustrative example of a higher series of samples X _H (Jc) . The samples are plotted on an x-y co-ordinate system with k plotted on the x-axis and the amplitude of the sample X_H' (Jc) plotted on the y axis.

Fig 3B schematically illustrates an illustrative example of a subset X^ (Jc) of the higher series of samples. The samples are plotted on an x-y co-ordinate system with k plotted on the x-axis and the amplitude of the sample X_H ^] (Jc) plotted on the y axis.

It will be noted that for some values of k, the sample X_H' (Jc) is the same as the sample Xj₁ (k) and that for other different values of k the sample X_H ^] (Jc) is null valued. A null value results in either it being ignored in a future calculation of a similarity cost function, or in it being economically processed in the similarity cost function . The subset selection block 20 selects a subset X_H ^J (k) of a higher series of samples X_H ³ (Jk) by, for example, selecting the values of k for which the sample Xh (Jc) is null valued.

Many different methodologies may be used for the selection of the subset Xh (Jc) of a higher series of samples X_H' (Jc) .

For example, the methodology used to produce the subset Xh (Jc) of a higher series of samples Xh (k) illustrated in Fig 3B maintains the h, samples with biggest absolute values, and sets all the other values to zero. The value of h_s may be selected independently for every one of the different higher series of samples, or the same value can be used for all the different higher series of samples. As a result, the high amplitude spectral peaks in the spectrum are maintained which retains the most perceptually important information and also the information that is most influential in the similarity cost function. Discontinuities or gaps are introduced into the continuous spectral band represented by the higher series of samples X_H ^] (Jc) .

Other different methodologies may be used.

For example, the subset selection block 20 may select a subset X_H ^J (k) of a higher series of samples by including psycho-acoustically significant samples and excluding psycho-acoustically insignificant samples. For example, the subset selection block 20 may select a subset Xh (Jc) Oi a higher series of samples based upon the amplitudes of the higher series of samples. It may for example, select the Z1 highest values, or the highest Z2% of values. It may for example use a statistical model to select the subset Xh (Jc) . For example it may select those samples with an amplitude greater than Z3 standard deviations from the mean amplitude.

For example, the subset selection block 20 may select a subset X_H ^] (Jc) of a higher series of samples based upon the maxima in the amplitudes of the higher series of samples. It may for example, select the Z1 highest maxima, or the highest Z2% maxima.

In the methodology described with reference to Fig 3A, the subset selection block 20 uses a criteria to select specific samples e.g. h_s samples with biggest absolute values and then applies the selection to only those specific samples e.g. maintains only the h_j samples. In an alternative methodology, the subset selection block 20 uses a criteria to select specific samples (e.g. h, samples with biggest absolute values) and then selects, for inclusion in the subset X_H ^} (k) , not only those specific samples but also one or more of the samples adjacent those specific samples.

Due to complexity limitations it is possible that for example in total Y non-zero

M

samples can be selected in total to all M higher band series, i.e. ∑h_} = Y. It is possible to analyze the perceptual importance of every higher series X_H ^] (k) and allocate the number of non-zero samples h_s based on that. For example, the number of non-zero samples h, for the higher series X_H ^} (k) can be defined by h_} = h_} __mm + p_] , where h_{j mιn} is the minimum number of non-zero components allocated to x_H ^] (k) in any case and p_s has been selected using some suitable

M

perceptual criteria such that∑h_} __mn +_Pj = γ.

The subset selection block 20 may use a predetermined methodology for selecting the subset. Alternatively, the subset selection block 20 may select which one of a plurality of different methodologies are used. Processing

The sub-series selection block 22 processes the selected subset x_H ^} {k) o\ a higher series of samples and the lower series of samples x_L(k) to parametrically encode the higher series of samples X_H' (k) by identifying a sub-series of the lower series of samples. The sub-series selection block 22 determines a similarity cost function S(d), that is dependent upon the selected subset Xj₁ (K) and a putative sub-series X_L (k + d) of the lower series of samples, for each one of a plurality of putative sub-series of the lower series.

It selects the best sub-series Xj (k) = X_L (k + d) by choosing the putative sub-series

X_L (k + d) of the lower series having the best similarity cost function S(d). It identifies the position of the selected putative sub-series X_L (k + d) within the lower series using a parameter (d).

An example of a suitable method 30 is illustrated in Fig 7.

At block 32, the subset Xj₁ (Ic) of the higher series of samples Xj₁ (K) is obtained from the subset selection block 20.

At block 34, the lower series of samples X_L(k) is obtained from, in the example of Fig 1 , the decoder block 12.

At block 36, initialization of the search loop occurs, d is set to 0. S_max is set to zero. dmax is set to zero.

The value d determines the putative sub-series X_L (k + d) of the lower series of samples X_L(k) . At block 40, a similarity cost function S(d) that is dependent upon the selected subset Xj_j (k) and the current putative sub-series X_L (k + d) of the lower series of samples is determined.

One example of a similarity cost function is the inverse of the Euclidian distance, another example is the normalized correlation. Equation (1 A) expresses an example of the similarity cost function as a cross-correlation.

. Equation (1 B) expresses another example of the similarity cost function as a normalized cross-correlation.

In (1 A) ri_j is the length of the /^h sub band.

The similarity cost function is a function of X^ ik) as opposed to being a function of

X_H ^] (k)

In this example, the similarity cost function, comprises processing of each of the samples in the selected subset X_H ^} (k) with the respective corresponding sample in the putative sub-series X_L (k + d) .

At block 42, if the current putative sub-series X_L (k + d) of the lower series has a better similarity cost function S(d) than the current value of S_max , then the method moves to block 44 otherwise it moves to block 46.

At block 44, the current best sub-series X[ (k) = X_L (k + J_1113x ) is updated by setting d_max(j)= d and S_max = S(d). The method then moves to block 46. At block 46, if the search has completed (d=D), the method moves to block 48. Otherwise the method moves to block 38, where d is incremented by one. and a new current putative sub-series X_L (k + d) is defined for the search loop.

At block 48, the position of the selected putative sub-series X_L (k + Cl_102x ) within the lower series is identified using the parameter d_max(j)

The range of allowed d values (number of search loops) can be quite large (for example 256 different values) and thus a large number of S(d) values are computed in the loop of Fig 7. The numerator of (1A) & (1 B), requires n_} multiplications as well as n_} - \ additions for every d. Thus the numerator of (1A) & (1 B) is a source of complexity. With the proposed method as the subset Xj₁ (k) is of size h_s only h_s multiplications and h_j - 1 additions are needed in the denominator of (1A) & (1 B) for every d, as all the other multiplications are known to be zero.

As an example, if the higher series of samples X_H ³ (k) has 100 samples but the subset x_H ^J (k) has 10 samples, and there are 150 different delay values d, the total complexity of the correlation computation in the numerator of Equation (1A) or (1 B) reduces from 15000 multiplications and 14850 additions to 1500 multiplications and 1350 additions, which is significant reduction. The reduced subset X_H ^} (Jk) , decreases significantly the complexity required by correlation calculation (equation (1A) or (1 B)). The reduced subset may be achieved by including in the calculation of the similarity cost function only the perceptually most important spectral components. In the similarity cost function S(d) defined at Equation (1A) or (1 B), the current putative sub-series X_L (k + d) and the subset X_H ^] (Jk) of the higher series of samples are derived from the same frame of digital audio 3. In other implementations, the search for the putative sub-series X_L (k + d) that best matches the subset x_H ^] (Jc) of the higher series of samples may range across multiple audio frames.

In the described implementation, the size of the higher series of samples and the size of the lower series of samples are predetermined. In other implementations the size of first series and/or the size of the second series may be dynamically varied.

Scaling Referring back to Fig 2, in this example, the most similar match X[(k) = X_L (k + d_max ) may be scaled using two scaling factors a_λ{j) and Ct₂(J). The first scaling factor Gr₁(Z) may be determined in the scaling parameter block 24. The second scaling factor ct₂(j) may be determined in the scaling parameter block 26. The first scaling factor a_λ(j) is dependent upon the selected subset x_H ^] (k) o\ the higher series of samples. The first scaling factor is a function of X_H ^} (k) as opposed to being a function of X_H ^] (Jc)

The first scaling factor operates on the linear domain to match the high amplitude peaks in the spectrum:

Equation (2) expresses an example of a suitable first scaling factor as a normalized cross-correlation.

Notice that a_λ(j) can get both positive and negative values.

The numerator of Equation (1 A) or (1 B) and Equation (2) are the same. The denominators of Equation (1A) or (1 B) and Equation (2) are related. The numerator and/or the denominator calculated for S(d_max) in Equation (1A) may be re-used to calculate the first scaling factor.

Alternatively, the first scaling factor may be computed as a function of X_H ^} (k) instead of a function of X_H ^] (k) , for example as shown in equation (3).

The second scaling factor σ₂(y) operates on the logarithmic domain and is used to provide better match with the energy and the logarithmic domain shape. The second scaling factor is a function of the whole of the higher series of samples X_H ^} (Jc) .

as opposed to being a function of the subset X_H ^] (k) .

Equation (4) expresses an example of a suitable second scaling factor:

where ^M _/ = max(log₁₀ ( OL_x (j)X [ (k) )) _.

The overall synthesized sub band x_H' (k) is then obtained as

where ζ{k) is -1 if Cc₁(J)XKk) is negative and otherwise 1 . The output of each of the parametric coding blocks 14, is a set of parameters representing the higher frequency band 15,. The parameters representing the higher frequency band 15, include the parameter d_max(j) which identifies a sub-series of the lower series of samples X_L(k) suitable for producing the higher series of samples

X_H ¹ (Jk) , and the scaling factors a_λ(j), Ct₂(J).

The audio decoding apparatus 4 processes the encoded data 5 to produce digital audio 7. The encoded data 5 comprises encoded audio 13 (encoding the lower series of samples X_L (k) ) and the parameters representing the higher frequency band 15,.

The decoding apparatus 4 is configured to decode the encoded audio 13 to produce the lower series of samples X_L (k) . The decoding apparatus 4 is configured to replicate the higher series of samples X_H ^] (Jk) forming the higher frequency spectral band using the sub-series X_L(Jk) o\ the lower series of samples identified by the parameter d_max(j).

Referring to Figs 1 and 2, each of the parametric coding block 14_{1 s} 14₂....14_M, may be provided as a distinct block or a single block may be reused with different inputs as the respective parametric coding blocks 14_{1 s} 14₂....14_M. A block may be a hardware block such as circuitry. A block may be a software block implemented via computer code.

Referring to Fig 2, the subset selection block 20 and the sub series selection block may be implemented by a single hardware block or by a single software block. Alternatively, the subset selection block 20 and the sub series selection block may be implemented using distinct hardware blocks and/or software blocks. A hardware block comprises circuitry. Referring to Fig 2, the scaling parameter blocks 24, 26 are optional. When present, one or more of the scaling parameter blocks may be integrated with the sub series selection block 22 or may be integrated into a single block. A software block or software blocks, a hardware block or hardware blocks and a mixture of software block(s) and hardware blocks may be provided by the apparatus 2. Examples of apparatus include modules, consumer devices, portable devices, personal devices, audio recorders, audio players, multimedia devices etc.

The apparatus 2 may comprise: circuitry 22 configured to process a selected subset of a series of samples X_H ^] (k) forming a higher frequency spectral band of an audio signal and a series X_L (k) oi samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples X_H ^] (Jc) forming the higher frequency spectral band by identifying a sub-series X_L(k) of the lower series of samples using a parameter d_max(j)..

Fig 5 schematically illustrates a controller 50 suitable for use in an encoding apparatus 2 and/or a decoding apparatus.

Implementation of a controller can be in hardware alone ( a circuit, a processor...), have certain aspects in software including firmware alone or can be a combination of hardware and software (including firmware). A controller may be implemented using instructions that enable hardware

functionality, for example, by using executable computer program instructions in a general-purpose or special-purpose processor that may be stored on a computer readable storage medium (disk, memory etc) to be executed by such a processor. The controller 50 illustrated in Fig 5 comprises a processor 52 and a memory 54.

The processor 52 is configured to read from and write to the memory 54. The processor 52 may also comprise an output interface 53 via which data and/or commands are output by the processor 52 and an input interface 55 via which data and/or commands are input to the processor 52.

The memory 54 stores a computer program 56 comprising computer program instructions that, when loaded into the processor 52, control the operation of the encoding apparatus 2 and/or decoding apparatus 4. The computer program instructions 56 provide the logic and routines that enables the apparatus to perform the methods illustrated in Figs 1 to 4 and 7. The processor 52 by reading the memory 54 is able to load and execute the computer program 56. The computer program may arrive at the apparatus via any suitable delivery mechanism 58. The delivery mechanism 58 may be, for example, a computer- readable physical storage medium as illustrated in Fig 6, a computer program product, a memory device, a record medium such as a CD-ROM or DVD, an article of manufacture that tangibly embodies the computer program 56. The delivery mechanism may be a signal configured to reliably transfer the computer program 56.

The apparatus may propagate or transmit the computer program 56 as a computer data signal. Although the memory 54 is illustrated as a single component it may be implemented as one or more separate components some or all of which may be

integrated/removable and/or may provide permanent/semi-permanent/

dynamic/cached storage. References to 'computer-readable storage medium', 'computer program product', 'tangibly embodied computer program' etc. or a 'controller', 'computer', 'processor' etc. should be understood to encompass not only computers having different architectures such as single /multi- processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field- programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other devices. References to computer program,

instructions, code etc. should be understood to encompass software for a

programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc.

Although a coding apparatus 2 and a decoding apparatus 4 have been described, it should be appreciated that a single apparatus may have the functionality to act as the coding apparatus and/or the decoding apparatus 4. As used here 'module' refers to a unit or apparatus that excludes certain

parts/components that would be added by an end manufacturer or a user.

The blocks illustrated in the Figs may represent steps in a method and/or sections of code in the computer program 56. The illustration of a particular order to the blocks does not necessarily imply that there is a required or preferred order for the blocks and the order and arrangement of the block may be varied. Furthermore, it may be possible for some steps to be omitted. Although embodiments of the present invention have been described in the preceding paragraphs with reference to various examples, it should be appreciated that modifications to the examples given can be made without departing from the scope of the invention as claimed.. Features described in the preceding description may be used in combinations other than the combinations explicitly described.

Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not.

Although features have been described with reference to certain embodiments, those features may also be present in other embodiments whether described or not.

Whilst endeavoring in the foregoing specification to draw attention to those features of the invention believed to be of particular importance it should be understood that the Applicant claims protection in respect of any patentable feature or combination of features hereinbefore referred to and/or shown in the drawings whether or not particular emphasis has been placed thereon. I/we claim:

Claims

1. A method comprising:

processing a selected subset of a higher series of samples forming a higher frequency spectral band of an audio signal and a lower series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the higher series of samples forming the higher frequency spectral band by identifying a sub- series of the lower series of samples.

2. A method as claimed in claim 1 , further comprising:

creating the selected subset by selecting a subset of a higher series of samples in the frequency domain that form a higher frequency spectral band of an audio signal; processing the selected subset of the higher series of samples and a lower series of samples in the frequency domain forming a lower frequency spectral band of the audio signal to select a sub-series of the lower series of samples; and

parametrically encoding the higher series of samples by identifying the selected sub- series of the lower series of samples.

3. A method as claimed in claim ^"l or 2, wherein the higher series has fewer samples that lower series.

4. A method as claimed in claim ^"l or 2, wherein the higher series and the lower series are non-overlapping

5. A method as claimed in any preceding claim, further comprising, for each one of different multiple higher series of samples forming different higher frequency spectral bands,

processing a selected subset for each one of multiple higher series of samples with the lower series of samples to parametrically encode each of the multiple higher series of samples by identifying, for each higher series of samples, a sub-series of the lower series of samples.

6. A method as claimed in any preceding claim, further comprising:

selecting a subset for each one of multiple different higher series of samples; processing each of the selected subsets and the first series of samples to select multiple sub-series of the first series of samples; and

parametrically encoding the multiple second series of samples by identifying the multiple selected sub-series of the first series of samples.

7. A method as claims in claims 5 or 6, wherein each of the multiple higher series has fewer samples that the lower series.

8. A method as claimed in claim 5, 6 or 7, wherein the multiple different higher series are non-overlapping.

9. A method as claimed in any preceding claim further comprising: creating the higher series of samples and the lower series of samples using a modified discrete cosine transform to transform a digital audio signal.

10. A method as claimed in any preceding claim further comprising psycho-acoustic encoding of the lower series of samples.

1 1 . A method as claimed in any preceding claim further comprising psycho-acoustic encoding and then decoding the lower series of samples before processing the selected subset of a higher series of samples and the lower series of samples to parametrically encode the higher series of samples by identifying a sub-series of the lower series of samples.

12. A method as claimed in any preceding claim, further comprising selecting a subset of a higher series of samples by modifying selected samples within the higher series of samples to have zero value.

13. A method as claimed in any preceding claim, further comprising selecting a subset of a higher series of samples by including psycho-acoustically significant samples and excluding psycho-acoustically insignificant samples

14. A method as claimed in any preceding claim, further comprising selecting a subset of a higher series of samples based upon the amplitudes of the higher series of samples

15. A method as claimed in claim 14, further comprising selecting a subset of a higher series of samples based upon the maxima in the amplitudes of the higher series of samples

16. A method as claimed in any preceding claim, further comprising selecting a subset of a higher series of samples based upon a statistical model

17. A method as claimed in any preceding claim, further comprising selecting a subset of a higher series of samples by selecting one of a plurality of different methodologies for determining a subset of a higher series of samples

18. A method as claimed in any preceding claim, wherein processing the selected subset and the lower series of samples to parametrically encode the higher series of samples by identifying a sub-series of the lower series of samples comprises: determining a similarity cost function, that is dependent upon the selected subset and a putative sub-series of the lower series of samples, for each one of a plurality of putative sub-series of the lower series;

selecting the putative sub-series of the lower series having the best similarity cost function; and

identifying the position of the selected putative sub-series within the lower series using a parameter.

19. A method as claimed in claim 18, wherein the similarity cost function, comprises processing of each of the samples in the selected subset with the respective corresponding sample in the putative sub-series.

20. A method as claimed in claim 18 or 19, wherein the similarity cost function, comprises correlation of the selected subset and the putative sub-series.

21 . A method as claimed in claim 20 wherein at least part of the correlation result for the selected putative sub-series is re-used to calculate a scaling factor.

22. A method as claimed in any preceding claim, wherein a first scaling factor is dependent upon the selected subset of the higher series of samples. .

23. A method as claimed in any one of claims 1 to 21 , wherein a first scaling factor is dependent upon the whole of the selected subset of the higher series of samples.

24. A method as claimed in claim 22 or 23, wherein a second scaling factor is dependent upon the whole of the higher series of samples.

25. A computer program which when run on a processor enables the processor to perform the method of any one of claims 1 to 24.

26. An apparatus configured to perform the method of any one of claims 1 to 24.

27. A module configured to perform the method of any one of claims 1 to 24.

28. A system comprising:

an encoding apparatus configured to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying, using a parameter, a sub-series of the lower series of samples; and

a decoding apparatus configured to replicate the series of samples forming the higher frequency spectral band using the sub-series of the lower series of samples identified by the parameter.

29. A system as claimed in claim 28, wherein the decoding apparatus is configured to decode data received from the encoding apparatus to produce the lower series of samples from which the sub-series of the lower series of samples is obtained.

30. Apparatus comprising:

circuitry configured to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

31 . An apparatus as claimed in claim 30, further comprising: circuitry configured to select a subset of a higher series of samples in the frequency domain that form a higher frequency spectral band of an audio signal;

32. Apparatus comprising:

processing means for processing a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

33. An apparatus as claimed in claim 32 further comprising:

means for selecting a subset of a higher series of samples in the frequency domain that form a higher frequency spectral band of an audio signal;

means for processing the selected subset of the higher series of samples and a lower series of samples in the frequency domain forming a lower frequency spectral band of the audio signal to select a sub-series of the lower series of samples; and means for parametrically encoding the higher series of samples by identifying the selected sub-series of the lower series of samples.

34. A computer program which when run on a processor enables the processor to process a selected subset of a series of samples forming a higher frequency spectral band of an audio signal and a series of samples forming a lower frequency spectral band of the audio signal to parametrically encode the series of samples forming the higher frequency spectral band by identifying a sub-series of the lower series of samples.

35. A computer program which when run on a processor enables the processor to select a subset of a higher series of samples in the frequency domain that form a higher frequency spectral band of an audio signal;

process the selected subset of the higher series of samples and a lower series of samples in the frequency domain forming a lower frequency spectral band of the audio signal to select a sub-series of the lower series of samples; and parametrically encode the higher series of samples by identifying the selected sub- series of the lower series of samples.

36. A computer readable physical medium tangibly embodying the computer program as claimed in claim 34 or 35.

37. A module comprising