Technical Field The technology relates generally to coding of spectral data with a variable sized frequency segmentation of sub-bands. Background The coding of audio utilizes coding techniques that exploit various perceptual models of human hearing. For example, many weaker tones near strong ones are masked so they do not need to be coded. In traditional perceptual audio coding, this is exploited as adaptive quantization of different frequency data. Perceptually important frequency data are allocated more bits and thus finer quantization and vice versa. Perceptual coding, however, can be taken to a broader sense. For example, some parts of the spectrum can be coded with appropriately shaped noise. When taking this approach, the coded signal may not aim to render an exact or near exact version of the original. Rather the goal is to make it sound similar and pleasant when compared with the original. All these perceptual effects can be used to reduce the bit-rate needed for coding of audio signals. This is because some frequency components do not need to be accurately represented as present in the original signal, but can be either not coded or replaced with something that gives the same perceptual effect as in the original. Summary Frequency segmentation is important to the quality of encoding spectral data. Segmentation involves breaking the spectral data into units called sub-bands or vectors. A simple segmentation is to uniformly split the spectrum into a desired number of homogeneous segments or sub-bands. Homogeneous segmentation may be suboptimal. There may be regions of the spectrum that can be represented with larger sub-band sizes, and other regions are better represented with smaller sub-band sizes. Various features are described for providing spectral data intensity dependent segmentation. Finer segmentation is provided for regions of greater spectral variance and coarser segmentation is provided for more homogeneous regions. For example, a default segmentation is provided initially, and an optimization varies the segmentation based on an intensity of spectral data variance. By providing sub-band sizes that are variable, the opportunity is created to size sub-bands to improve coding efficiency. Often, sub-bands which have similar characteristics may be merged with very little effect on quality, whereas sub-bands with highly variable data may be better represented if a sub-band is split. Various methods are described for measuring tonality, energy, or shape of a sub-band. These various measurements are discussed in light of making decisions of when to split or merge sub-bands. However, smaller sub-bands require more sub-bands to represent the same spectral data. Thus, the smaller sub-band sizes require more bits to code the information. In cases when variable sub-band sizes are employed, a sub-band configuration is provided for efficient coding of the spectral data, while considering both the data required to code the sub-bands and the data required to send the sub-band configuration to a decoder. Spectral data is initially segmented into sub-bands. Optionally, an initial segmentation may be varied to produce an optimal segmentation. Two such initial or default segmentations are called a uniform split segmentation and a non-uniform split segmentation. The higher frequency sub-bands often have less variation to begin with, so fewer larger sub-bands can capture the scale and shape of the band. Additionally, the higher frequency sub-bands have less importance in the overall perceptual distortion because they have less energy and are perceptually less important. Although a default or initial segmentation is often sufficient for coding spectral data, there are signals which benefit from an optimized segmentation. Starting with a default segmentation (such as a uniform or non-uniform segmentation), sub-bands are split or merged to obtain an optimized segmentation. A decision is made to split a sub-band into two sub-bands, or to merge two sub-bands into one sub-band. A decision to split or merge can be based on various characteristics of the spectral data within an initial sub-band, such as a measurement of intensity of change over a sub-band. In one example, a decision is made to split or merge based on sub-band spectral data characteristics such as tonality or spectral flatness in a sub-band. In one such example, if the ratio of energy is similar between two sub-bands, and if at least one of the bands is non-tonal, then the two adjacent sub-bands are merged. This is because a single shape vector (e.g., codeword) and a scale factor will likely be sufficient to represent the two sub-bands. In another example, two sub-bands may be defined to have different shape if the shape match significantly improves when the sub-band is split. In one example, a shape match is considered better if the two split sub-bands have a much lower means-square Euclidean difference (MSE) match after the split, as compared to the match before the split. In another example, an algorithm is run repeatedly until no additional sub-bands are split or merged. It may be beneficial to tag sub-bands as split, merge, or original in order to reduce the chance of an infinite loop. For example, if a sub-band is marked as a split sub-band, then it will not be merged back with a sub-band it was split from. Additional features and advantages of the invention will be made apparent from the following detailed description of embodiments that proceeds with reference to the accompanying drawings. Brief Description of the Drawings Figures 1 and 2 are a block diagram of an audio encoder and decoder in which the present coding techniques may be incorporated. Figure 3 is a block diagram of a baseband coder and extended band coder implementing the efficient audio coding using modified codewords and or variable frequency segmentation that can be incorporated into the general audio encoder of Figure 1. Figure 4 is a flow diagram of encoding bands with the efficient audio coding using the extended band coder of Figure 3. Figure 5 is a block diagram of a baseband decoder, an extended band configuration decoder, and extended band decoder that can be incorporated into the general audio decoder of Figure 2. Figure 6 is a flow diagram of decoding bands with the efficient audio coding using the extended band decoder of Figure 5. Figure 7 is a graph representing a set of spectral coefficients. Figure 8 is a graph of a codeword and various linear and non-linear transformations of the codeword. Figure 9 is a graph of an exemplary vector that does not represent peaks distinctly. Figure 10 is a graph of Figure 9 with distinct peaks created via codeword modification by exponential transform. Figure 11 is a graph of a codeword as compared to the sub-band it is modeling. Figure 12 is a graph of a transformed sub-band codeword as compared to the sub-band it is modeling. Figure 13 is a graph of a codeword, a sub-band to be coded by the codeword, a scaled version of the codeword, and a modified version of the codeword. Figure 14 is a diagram of an exemplary series of split and merge sub-band size transformations. Figure 15 is a block diagram of a suitable computing environment for implementing the audio encoder/decoder of Figure 1 or 2. Detailed Description The following detailed description addresses audio encoder/decoder embodiments with audio encoding/decoding of audio spectral data using modification of codewords and/or modification of a default frequency segmentation. This audio encoding/decoding represents some frequency components using shaped noise, or shaped versions of other frequency components, or the combination of both. More particularly, some frequency bands are represented as a shaped version or transformation of other bands. This often allows a reduction in bit-rate at a given quality or an improvement in quality at a given bit-rate. Optionally, an initial sub-band frequency configuration can be modified based on tonality, energy, or shape of the audio data. Brief Overview In the patent application, "Efficient coding of digital media spectral data using wide-sense perceptual similarity," U.S. Patent Application No. 10/882,801, filed June 29, 2004, an algorithm is provided which allows the coding of spectral data by representing certain portions of the spectral data as a scaled version of a code-vector, where the code-vector is chosen from either a fixed predetermined codebook (e.g., a noise codebook), or a codebook taken from a baseband (e.g., a baseband codebook). When the codebook is adaptively created, it can consist of previously encoded spectral data. Various optional features are described for modifying the code-vectors in the codebook according to some rules which allow the code-vector to better represent the data they are representing. The modification can consist of either a linear or non-linear transform, or representing the code-vector as a combination of two or more other original or modified code-vectors. In the case of a combination, the modification can be provided by taking portions of one code-vector and combining it with portions of other code-vectors. When using code-vector modification, bits have to be sent so that the decoder can apply the transformation to form a new code-vector. Despite the additional bits, codeword modification is still a more efficient coding to represent portions of the spectral data than actual waveform coding of that portion. The described technology relates to improving the quality of audio coding, and can also be applied to other coding of multimedia such as images, video, and voice. A perceptual improvement is available when coding audio, especially when the portion of the spectrum used to form the codebook (typically the lowband) has different characteristics than the portion being coded using that codebook (typically the highband). For example, if the lowband is "peaky" and thus has values which are far from the mean, and the highband is not, or vice-versa, then this technique can be used to better code the highband using the lowband as a codebook. A vector is a sub-band of spectral data. If sub-band sizes are variable for a given implementation, this provides the opportunity to size sub-bands to improve coding efficiency. Often, sub-bands which have similar characteristics may be merged with very little effect on quality, whereas sub-bands with highly variable data may be better represented if a sub-band is split. Various methods are described for measuring tonality, energy, or shape of a sub-band. These various measurements are discussed in light of making decisions of when to split or merge sub-bands. However, smaller (split) sub-bands require more sub-bands to represent the same spectral data. Thus, the smaller sub-band sizes require more bits to code the information. In cases when variable sub-band sizes are employed, a sub-band configuration is provided for efficient coding of the spectral data, while considering both the data required to code the sub-bands and the data required to send the sub-band configuration to a decoder. The following paragraphs proceed through more generalized examples to more specific examples. Generalized Audio Encoder and Decoder Figures 1 and 2 are block diagrams of a generalized audio encoder (100) and generalized audio decoder (200), in which the herein described techniques for audio encoding/decoding of audio spectral data using modification of codewords and/or modifications of an initial frequency segmentation. The relationships shown between modules within the encoder and decoder indicate the main flow of information in the encoder and decoder; other relationships are not shown for the sake of simplicity. Depending on implementation and the type of compression desired, modules of the encoder or decoder can be added, omitted, split into multiple modules, combined with other modules, and/or replaced with like modules. In alternative embodiments, encoders or decoders with different modules and/or other configurations of modules measure perceptual audio quality. Further details of an audio encoder/decoder in which the wide-sense perceptual similarity audio spectral data encoding/decoding can be incorporated are described in the following U.S. patent applications: U.S. Patent Application No. 10/882,801, filed 6/29/2004; U.S. Patent Application No. 10/020,708, filed 12/14/2001; U.S. Patent Application No. 10/016,918, filed 12/14/2001; U.S. Patent Application No. 10/017,702, filed 12/14/2001; U.S. Patent Application No. 10/017,861, filed 12/14/2001; and U.S. Patent Application No. 10/017,694, filed 12/14/2001. Exemplary Generalized Audio Encoder The generalized audio encoder (100) includes a frequency transformer (110), a multi-channel transformer (120), a perception modeler (130), a weighter (140), a quantizer (150), an entropy encoder (160), a rate/quality controller (170), and a bitstream multiplexer ["MUX"] (180). The encoder (100) receives a time series of input audio samples (105). For input with multiple channels (e.g., stereo mode), the encoder (100) processes channels independently, and can work with jointly coded channels following the multi-channel transformer (120). The encoder (100) compresses the audio samples (105) and multiplexes information produced by the various modules of the encoder (100) to output a bitstream (195) in a format such as Windows Media Audio ["WMA"] or Advanced Streaming Format ["ASF"]. Alternatively, the encoder (100) works with other input and/or output formats. The frequency transformer (110) receives the audio samples (105) and converts them into data in the frequency domain. The frequency transformer (110) splits the audio samples (105) into blocks, which can have variable size to allow variable temporal resolution. Small blocks allow for greater preservation of time detail at short but active transition segments in the input audio samples (105), but sacrifice some frequency resolution. In contrast, large blocks have better frequency resolution and worse time resolution, and usually allow for greater compression efficiency at longer and less active segments. Blocks can overlap to reduce perceptible discontinuities between blocks that could otherwise be introduced by later quantization. The frequency transformer (110) outputs blocks of frequency coefficient data to the multi-channel transformer (120) and outputs side information such as block sizes to the MUX (180). The frequency transformer (110) outputs both the frequency coefficient data and the side information to the perception modeler (130). The frequency transformer (110) partitions a frame of audio input samples (105) into overlapping sub-frame blocks with time-varying size and applies a time-varying MLT to the sub-frame blocks. Exemplary sub-frame sizes include 128, 256, 512, 1024, 2048, and 4096 samples. The MLT operates like a DCT modulated by a time window function, where the window function is time varying and depends on the sequence of sub-frame sizes. The MLT transforms a given overlapping block of samples x[n],0 becomes < 3.2, 2.2, 1.5, 1 >. In another example, the dynamic range or variance of a codeword is reduced (806) using exponentiation with an exponent less than one on each coefficient. Similarly, a codeword's variance is exaggerated (e.g., increased variance) using an exponent greater than one, not shown. For example, a codeword containing the coefficients < 1, 1, 2, 1, 4, 2, 1 > is raised to the power of 2 to create the codeword < 1, 1, 4, 1, 16, 4,1 >. In another example, the coefficients of a codeword <-\, 1, 2, 3 > (802) are negated < 1, -1, -2, -3 > (808). Of course, many other linear and nonlinear transformations (e.g., 806) can be performed on one or more codewords in order to provide a larger or more diverse universe or library for matching sub-bands, or other vectors. Additionally, one or more transforms may also be applied in combination to the codewords in order to provide greater diversity of available shapes. In one example, an encoder first determines a codeword in the baseband that is a closest match to a sub-band being encoded. For example, a least-means-square comparison of coefficients in the baseband can be used to determine a best match. For example, after comparing (708) to (710), the comparison moves one coefficient down the spectrum, one coefficient at a time, to obtain another codeword to compare to (710). Then when a closest match is found, in one example, the shape of the best match codeword is varied by non-linear transform to see if the match can be improved. For example, using an exponent transform on the coefficients of a best match codeword can provide refinement on the match. There are two methods to finding the best code-word match and exponent. In the first method, a best code-word is found typically using the Euclidean distance as the metric (MSE). After the best code-word is found, the best exponent is found. The best exponent is found using one of the following two methods. One method is to try all the exponents available and see which one gives the minimum Euclidean distance, the other method is to try exponents to see which exponent gives the best histogram or probability mass function (pmf) match. The pmf match can be computed using the second moment about the mean (the variance) for the pmf of the original vector and for each of the exponentiated vectors. The one with the closest match is chosen to be the best exponent. The second method of finding the best code-word and exponent match is to do an exhaustive search using many combinations of code-words and exponents. If, for example, X°5 provides a better comparison than X1 °, a sub-band is coded using the offset to that codeword in the baseband (712), along with a transformation (linear or non-linear) xp, where one or more bits indicating p=0.5 is sent to and applied at the decoder. In this example, the search proceeded with finding a codeword first, and then varying with a transform, but no such order is required in practice. In another example, an exhaustive search is performed along the baseband and/or other codebooks to find a best match. For example, a search is performed comprising an exhaustive search along the baseband of all combinations of (exponential transform (p=0.5, 1.0, 2.0), sign transform (+/-), direction (forward/reverse). Similarly, this exhaustive search may be performed along the noise codebook spectrum, or codewords. In general, a close match can be provided by determining a lowest variance between the sub-band being coded and the codeword and transformation selected to model a sub-band. An identifier or coded indication of the codeword and/or transform, along with other information such as a scale factor, is coded in the bitstream and provided to the encoder. Exemplary Multiple Codeword Coding In one example, two different codewords are utilized for providing a sub-band encoding. For example, given two codewords b and n of length u, are provided b = and n = < no, n1, ... nu > to better describe a sub-band being coded. Vector b may be from the baseband, any prior band, a noise codebook, or a library, and vector n may similarly be from any such source. A rule is provided for interleaving coefficients from each two or more codewords b and n, such that the decoder implicitly or explicitly knows which coefficient to take from the codewords b and n. The rule may be provided in the bitstream or may be known by the decoder implicitly. The rule and two or more vectors are used at the decoder to create the sub-based s = < n0, b1, n2, n3, b4, ... nu >. For example, a rule is established based on the order of the codewords sent, and a percentage value "a". The encoder delivers information in the order (b, n, a). The decoder translates the information into a requirement to take any coefficient from the first vector b if that coefficient is less than 'a' multiplied by the highest coefficient value M in vector b. Thus, if a coefficient bi is greater than a*M, then bi is in vector s, otherwise ni is in s. Another rule may require that in order for bi to be in vector s, it has to be part of a group of T adjacent coefficients with a value less than a*M. If a default value for 'a' is set, then 'a' does not need to be sent to the decoder, since it is implicit. Thus, a decoder can send two or more codeword identifiers, and optionally, a rule to decode which coefficients to take to create the sub-band. The encoder will also send scale factor information for codewords, and optionally if relevant, any other codeword transform information since b and/or n may be linearly or non-linearly transformed. Using two or more codewords b and n above, an encoder would send an identifier (e.g., a motion vector, codebook index, etc.) of the codewords, a rule (e.g., index to rulebook) or the rule will be implicitly known by both the encoder and decoder, any additional transform information (e.g., xp, p=0.5, assuming b or n also requires additional transform), and information about scale factors (e.g., st,, sn, etc.). Scale factor information may also be a scale factor and a ratio (e.g., Sb, Sb/sn, etc.). With one vector scale factor and a ratio, the decoder will have enough information to compute the other scale factor. Exemplary Enhancement of Baseband Under certain conditions, such as low bitrate applications, the baseband itself may not be well coded (e.g., several consecutive or intermingled zero coefficients). In one such example, the baseband represents peaks of intensity well, but does not well represent subtle variances at coefficients representing lower intensities between peaks. In such a case, the peaks of a codeword from the baseband itself are selected as a first vector (e.g., b), and the zero coefficients, or very low relative coefficients are replaced with a second vector (e.g., n) that more closely resembles the low energy between peaks. Thus, the two codeword method can be used on the baseband or sub-band of the baseband, to provide baseband enhancement. As before, the rule used for selecting from the first, or second vector, may be explicit and sent to the decoder, or implicit. In some cases the second vector may best be provided via a noise codeword. Exemplary Transformations A baseband, previous band or other codebook provides a library of consecutive coefficients, each coefficient potentially serving as the first coefficient in a series of consecutive coefficients that may serve as a codeword. A best match codeword in the library is identified and sent to a decoder, along with a scale factor, and is used by the decoder to create a sub-band in the extended sub-band. Optionally, one or more codewords in the library are transformed to provide a larger universe of available codewords to find a best match for a shape being coded. In mathematics, a universe of linear and non-linear transformations exists for shapes, vectors, and matrices. For example, a vector can be reversed, negated across an axis, and shape can be otherwise altered with linear and non-linear transformations such as by applying root functions, exponents, etc. A search is performed on the library of codewords, including applying one or more linear or non-linear transforms on the codewords, and a closest match codeword is identified, along with any transform. An identifier of a best match, codeword, a scale factor, and a transform identifier is sent to a decoder. A decoder receives the information and reconstructs a sub-band in the extended band. Optionally, an encoder selects two or more codewords that together best represents a sub-band being coded and/or enhanced. A rule is used to select or interleave individual coefficient positions in the sub-band being coded. The rule is implicit or explicit. The sub-band being coded may be in the extended band, or may be a sub-band in the baseband being enhanced. The two or more codewords being used may be from a baseband or any other codebook, and one or more of the codewords may be transferred linearly or non-linearly. Exemplary Envelope Matching A signal called "an envelope" (e.g., Env(i)) is generated by running a weighted average on the input signal x(i) (e.g., audio, video, etc.) as follows: (Equation Removed) where w(j) is a weighting function (presently a triangle shape) and L is the number of neighborhood coefficients to be considered in the weighted analysis. Previously, and example of an exhaustive search was discussed using an input universe of codewords, exponent transformation (0.5, 1.0, 2.0), coefficient negation (sign +/-) and codeword coefficient direction (forward, reverse). Instead a best 'Q' number of codewords are first selected (combinations of codeword, exponent, sign, and/or direction) are selected using a Euclidean distance between the envelopes of the sub-band being coded, and the codeword. The original unquantized versions of the codewords may be useful to measure the envelope Euclidean distance. From these Q closest candidates determined based on Euclidean distance, a best match is selected. Optionally, after envelopes are considered, a method (such as previously described codeword comparison methods) may return to examine which of the Q candidates best fit. Exemplary Codeword Modification Given a codebook consisting of code vectors, a modification of the code-vectors in the codebook is proposed such that they better represent the vector being coded. The codebook/codeword modification can consist of any combination of one or more of the following transformations. • Linear transform applied to a code-vector. • Non-linear transform applied to a code-vector. • Combining more than one code-vector to obtain a new code-vector (the vectors being combined can come from the same codebook, different codebooks, or be random). • Combining a code-vector with a base coding. The information relating to which transformation, if any, is used and which code-vectors are used in the transformation is either sent to the decoder in the bitstream or computed at the decoder using knowledge that it already has (data that it has already decoded). A vector is typically a certain band of spectral coefficients which are to be coded. Three examples in particular are given for codeword modifications: (1) exponentiation applied to each component of the vector (non-linear transform), (2) combining of two (or more) vectors to form a new-vector, where each of the two vectors is used to represent portions of the vector which have different characteristics, and (3) combining a code-vector with a base coding. In the following discussion, v will be used to represent the vector to be coded, x will be the code vector or codeword being used to code v, and y will be the modified code vector. Vector v will be coded using an approximation v' = Sx, where S is a scale factor. The scale factor used is a quantized version of the ratio of power between v and x, (Equation Removed) where Q(.) is quantization, and ||.|| represents the norm, which is the power in the vector. A quantized version of the power in the original vector is sent. The decoder computes the scale factor to use by dividing by power in the code-vector. Exemplary Non-linear Transformation A first example consists of applying an exponent to each component in the code-vector. Table 3 provides a non-linear transformation of a series of coefficients in a codeword. Table 3 (Table Removed) In this example, each coefficient in a codeword (code-vector) is raised to the power of exponent two (x2). In such an example, if the shape of the transformed codeword is a best fit for a vector to be coded, then the encoder will provide an identification of the codeword and the transformation leading to a best match. The exponent can be sent to the decoder using a fixed number of bits, or can be sent from a codebook of exponents, or can be implicitly calculated at the decoder using previously seen data. For example, for an L dimensional vector, let the components of the 'i'th code-vector in a codebook be Xi[0], Xi[l], ..., Xi[L-l]. Then, the exponentiation applies an exponent 'p' to modify the vector to get a new vector yi, (Equation Removed) where 'j' is the component index. This non-linear transformation allows a code vector which has peaks to be used to code a vector which does not by using a value of p which is less than 1. Similarly, it allows a non-peaky code-vector to be used to represent one with peaks by using p > 1. Figure 9 is a graph of an exemplary vector that does not represent peaks distinctly. Figure 10 is a graph of Figure 9 with distinct peaks created by exponential transform. As an example, see Figure 9 and Figure 10. In Figure 9, a vector which is fairly random and is shown has no distinct peaks. When an exponent p=5 is applied, then Figure 10 represents the desired peaks better. Similarly, if the original code-vector was that shown in Figure 10, then an exponent p= 1/5=0.2, would provide Figure 9. The scale factor of course is recomputed since the norm (or energy) in the codevector has changed during the transformation from x to y. In particular, S=Q(||v||)/||y|| is now used for the scale factor. The actual scale factor that is sent Q(||v||) is not changed with the exponent, but the decoder has to compute a different scale factor due to the change in the power in the code-vector. A codeword may have several exponents applied to it, each providing different results. The method used to calculate the best exponent is to find an exponent such that the histogram (or probability mass function (pmf)) of the values over the code-vector best match that of the actual vector. In order to do this, a variance of the symbol values for both the vector and the code-vector is computed using exponentiation. For example suppose the set of possible exponents is pk, where k is used to index the set of possible exponents, k=0,l,....,P-l. Then the normalized second moment about the mean for the codevector resulting from each of possible exponents is computed (Vt), and compared to the actual vector (V). (Equation Removed) The best exponent is chosen to minimize the difference between Vk and V, and is given by Pb, where b is defined as: (Equation Removed) As previously stated, a best match exponent can also be found using an exhaustive search. Exemplary Codeword Modification Via Combining Another transformation combines multiple vectors to form a new code-vector. This is essentially a multistage coding, where at each stage a match is found which best matches the most important portion of the vector not yet coded. As an example for two vectors, we first find the best match and then see which portion of the vector is being coded well. This segmentation can be explicitly sent, but this may take too many bits. Therefore, the segmentation is implicitly provided, in one example, by indicating which portion of the vector to use. The remaining portion is then represented using either a random code-vector, or another code-vector from a codebook which represents the remaining components better. Let x be a first code vector, and let w be a second code vector. Let the set T specify the portion of the vector which is considered to be coded using the first code-vector. The cardinality of set T will be between 0 & L, i.e. it will have between 0 and L elements which represent the indices of the vector which are considered to be coded using this first code-vector. A rule is provided for figuring out which components are well represented by the first vector and the rule can use metrics, such as, determining if a potential coefficient is larger than a certain percentage of the maximum coefficient in the first vector. Thus, for any coefficient in the first vector that is within a percentage of the highest coefficient in the first vector, that coefficient will be taken from the first vector, else, that codeword coefficient is taken from the second codeword. Let M be the maximum value in the first code vector x. Then the set T can be defined using the following: (Equation Removed) > where 'a' is some constant between 0 & 1. For example, if a=0, then any non-0 value is considered to belong to the set T of coded vectors. If a=l-e, then only the maximum value itself is considered to be coded, if e is taken to be sufficiently small. Then given the set T, a set N is the complimentary and remaining set taken from vector w, as follows: (Equation Removed) Thus, a coefficient of x[j] is taken from x or w depending on the value of aM. Note that N or T can be further split using other similar rules to get more than two vectors. Given T & N as the sets of indices coded using the first codevector (x) and second codevector (w) respectively, a new vector y is defined: (Equation Removed) where Sx and Sw are the scale factors for x and w, respectively. Since a scale factor for the entire code-vector is typically sent, which represents a quantized version of the power in the entire vector being coded, a ratio between the two scale factors (SW/SX) in addition to the scale factor for the entire code-vector needs to be sent in this case. In general, if a vector is created using 'm' codevectors, then 'm' scale factors would have to be sent including the one for the entire vector. For example, for the two vector case, note that, (Equation Removed) Suppose vt and vn are defined as the two vectors, then their power may be defined as, (Equation Removed) where |T| and |N| are the cardinality of the two sets (the number of elements). Given the values for ||v|| (the total power in the vector), and ||vn|| (the power in the second component of the vector), a decoder can compute, (Equation Removed) Thus, if a quantized version of the power in set N is sent (Q(||vn||), and the total power is sent Q(||v||), it is sufficient information for the decoder. It is important to note that, by using the code-vector x itself to perform the segmentation, the encoder avoids having to send any information relating to segmenting because the coefficient selected from each vector x and w is implicit in the rules (e.g., x[j] > aM). Even in cases when the code-vector index or motion vector corresponding to x is not sent (it is a random code-vector), segmentation of sets T and N can be matched between encoder and decoder by using a random vector with the state of the random vector generator being deterministic based upon information that both the encoder and decoder have. For example, the random vector can be determined by using some combination of the least significant bits (LSB) of data that has been coded and sent to the decoder (such as in the encoded baseband) and then using that to seed a pseudo-random number generator. This way the segmentation can be implicitly controlled even if the actual code-vector is not sent. This transformation by combining two vectors allows better representation of the vector that is to be coded. The vector w can be from a codebook and an index can be sent to represent it, or it can be random, in which case no additional information needs to be sent. Note that in the example given above, the segmentation is implicit since it is done using a comparison rule on the coefficients (e.g., x[j] > aM) using vector x, so no information regarding the segmentation needs to be sent. This transformation is useful when the vector to be coded has two different distributions. Figure 11 is a graph of a codeword as compared to the sub-band it is modeling. In this example (1100), the code-vector has been chosen to best match the peaks in the vector. However, although the peaks are matched well, the rest of the vector does not have similar power. The remaining portion of the code-vector has much less power relative to the peaks than the actual vector does. This results in noticeable compression artifacts. However, when the portion of v that is well coded by the code-vector is selected out of the first vector and then a second code-vector is applied to the remaining portion, a much better result is obtained. Figure 12 is a graph of a transformed codeword as compared to the sub-band it is modeling. The modeled sub-band is modeled by a codeword created from two codewords. Figure 13 is a graph of a codeword, a sub-band to be coded by the codeword, a scaled version of the codeword, and a modified version of the codeword. Exemplary Codeword Modification Via Selective Operations An alternate version of the multi codevectors (e.g., multi-codewords) adds the first codevector rather than replacing it for certain selected coefficients. This can be done applying the following equation: (Equation Removed) Exemplary Enhancement of the Baseband In this example, a code-vector is combined with a base coding. This is similar to the two vector (or multi vector) approach, except that the first vector x is both the vector being coded and is itself used as one of the two vectors to encode itself. For example, a base coding is modified to include those coefficients where the base coding is working well and better coefficients are taken from the second vector, as before. For each vector (sub-band) that is coded, if a base coding already exists, this base coding then is the first code-vector in the multi-vector scheme, where it is segmented into regions T & N (or more regions). The segmentation (e.g., coefficient selection) can be provided using the same techniques as in the multi code-vector approach. For example, for each base coding, if there are any coefficients with a value of 0, all of these will then go into set N which are then coded by an enhancement layer (e.g., second vector). Such a method can be used to fill in large spectral holes which often result from coding at very low bitrates. Modifications can include not filling in holes or 'zero' coefficients unless they are larger than some threshold, where the threshold can be defined to be a certain number of Hertz (Hz) or coefficients (multiple zero coefficients). There can also be limitations on not filling of holes that are below a certain frequency. These limitations modify the implicit segmentation rules given above (e.g., x[j] > aM, etc.). For example, if a threshold 'T' on a minimum size of a spectral hole is provided, then this essentially changes the definition of set N to the following: (Equation Removed) for some K between 0,...,T-1. So in order for x[j] to be in set N, it has to be part of a group of T consecutive coefficients, all of which have a value less than or equal to (aM). This can be computed in two steps, first computing for each coefficient whether its value is less than the threshold, and then grouping them together to see if they meet the 'consecutive' requirements. For a true spectral hole of size T, a=0. Other conditions such as minimum frequency constraints add the additional constraint that in order to belong to Set N, j > Tminfreq. The above rule provides a filter that requires that multiple coefficients in a row (e.g., T consecutive coefficients) satisfy the condition x[j] < aM, before the rule signals replacing the coefficients with values from the second vector. Another modification that may need to be made is due to the fact that base coding also codes the channels after applying a channel transform. Thus, after a channel transform the base coding and enhancement coding might have different channel groupings. So, instead of just looking at the base coding for the particular channel upon which the enhancements is applied, the segmentation might look at more than the base coding channel. This again modifies the segmentation constraint. For example, suppose channels 0 and 1 are jointly coded. Then the rule to apply the enhancement is changed to the following. In order to apply the enhancement, the spectral hole has to be present in both the baseband coded channels since both the coded channels contribute to both the actual channels. Exemplary Optimization of Segmentation of Sub-bands Good frequency segmentation is important to the quality of encoding spectral data. Segmentation involves breaking the spectral data into units called sub-bands or vectors. A simple segmentation is to uniformly split the spectrum into a desired number of homogeneous segments or sub-bands. Homogeneous segmentation may be suboptimal. There may be regions of the spectrum that can be represented with larger sub-band sizes, and other regions are better represented with smaller sub-band sizes. Various features are described for providing spectral data intensity dependent segmentation. Finer segmentation is provided for regions of greater spectral variance and coarser segmentation is provided for more homogeneous regions. For example, a default or initial segmentation is provided initially, and an optimization or subsequent configuration varies the segmentation based on an intensity of spectral data variance. Exemplary Default Segmentation Spectral data is initially segmented into sub-bands. Optionally, an initial segmentation may be varied to produce an optimal or subsequent segmentation. Two such initial or default segmentations are called a uniform split segmentation and a non-uniform split configuration. These or other sub-band configurations can be provided initially or by default. Optionally, the initial or default configuration may be reconfigured to provide a subsequent sub-band configuration. Given spectral data of L spectral coefficients, a uniform split segmentation of M sub-bands of data is identified with the following equation: (Equation Removed) For example, if the L spectral coefficients are labeled as points as 0, 1, ..., L-l, then the M sub-bands start at the s[j] coefficients in the spectral data. Thus, the 'j'th sub-band has coefficients from s[j] to s[j+l]-l, j=0,l,...,M-l, with a sub-band size of s[j+l]-s[j] coefficients. The non-uniform split segmentation is done in a similar way, except that sub-band multipliers are provided. A sub-band multiplier is defined for each of the M sub-bands, a[j], j=0, 1, ..., M-l. Further, a cumulative sub-band multiplier is provide as follows: (Equation Removed) The starting point for the sub-bands in the non-uniform split configuration case is defined as: (Equation Removed) Again, the 'j'th sub-band includes coefficients from s[j] to s[j+l]-l, where j = 0, 1,..., M-l, with a sub-band size of s[j+l] - s[j] coefficients. The non-uniform configuration has sub-band sizes which increase with frequency, but it can be any configuration. Further, if desirable, it can be predetermined, so that no additional information needs to be sent to describe it. For the default non-uniform case, an example of sub-band multipliers is provided as follows: a = {1, 1, 2, 2, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, ...} Thus, the default non-uniform band-size multiplier is a split configuration where the band sizes are monotonically non-decreasing (the first few sub-bands are smaller, and the higher frequency sub-bands are larger). The higher frequency sub-bands often have less variation to begin with, so fewer larger sub-bands can capture the scale and shape of the band. Additionally, the higher frequency sub-bands have less importance in the overall perceptual distortion because they have less energy and are perceptually less important to human ears. Notice that the uniform split can also be explained using sub-band multipliers, except that a[j] = 1 for all j. Although a default or initial segmentation is often sufficient for coding spectral data, and in fact the non-uniform scheme can handle a large percentage of cases, there are signals which benefit from an optimized segmentation. For such signals, a segmentation is defined that is similar to the non-uniform case, except that the band multipliers are arbitrary instead of fixed. The arbitrary band multipliers reflect the splits and merges of sub-bands. In one example, an encoder signals the decoder with a first bit indicating whether the segmentation is fixed (e.g., default) or variable (e.g., optimized or altered). A second bit is provided for signaling whether the initial segmentation is uniform split or an non-uniform split. Exemplary Optimized Segmentation Starting with a default segmentation (such as a uniform or non-uniform segmentation), sub-bands are split or merged to obtain an optimized or subsequent segmentation. A decision is made to split a sub-band into two sub-bands, or to merge two sub-bands into one sub-band. A decision to split or merge can be based on various characteristics of the spectral data within an initial sub-band, such as a measurement of intensity of change over a sub-band. In one example, a decision is made to split or merge based on sub-band spectral data characteristics such as tonality or spectral flatness in a sub-band. In one such example, if the ratio of energy is similar between two sub-bands, and if at least one of the bands is non-tonal, then the two adjacent sub-bands are merged. This is because a single shape vector (e.g., codeword) and a scale factor will likely be sufficient to represent the two sub-bands. One example of such a ratio of energy is provided as follows: (Equation Removed) (Tonality0 < T \\ Tonality, < T) max(£0,£,) In this example, Eo is the energy in sub-band 0, E1 is the energy in an adjacent sub-band 1, 'a' is a constant threshold value (typically in the range 0 < a < 1) and T is a tonality comparison metric. The tonality measure (e.g., Tonality o) in a sub-band can be obtained using various methods analyzing the spectrum. Similarly, if splitting a single sub-band into two sub-bands creates two sub-bands with dissimilar energy, then the split should be made. Or, if splitting a sub-band creates two sub-bands that are strongly tonal with different shape characteristics, then the sub-band should be split. For example, such a condition is defined as follows: (Equation Removed) (1 + b) || (Tonality„ > T & & Tonality, > T & & Different shape) where 'b' is a constant greater than zero. For example, two sub-bands may be defined to have different shape if the shape match significantly improves when the sub-band is split. In one example, a shape match is considered better if the two split sub-bands have a much lower means-square Euclidean difference (MSE) match after the split, as compared to the match before the split. For example, a sub-band is compared to a plural codewords to determine a best match codeword for the single sub-band. Then the sub-band is split into two bands, each sub-band compared to (half) codewords to find a best match for each split sub-band. The MSE of the two sub-bands matches is compared to the MSE of the single sub-band match, and a significantly improved match indicates a improvement worth the extra overhead of encoding a split. For example, if an MSE improves by 20% or more, the split is considered efficient. In this example, although not required, the shape match becomes relevant if both the split sub-bands are tonal. In one example, an algorithm is run repeatedly until no additional sub-bands are split or merged in a present iteration. It may be beneficial to tag sub-bands as split, merge, or original in order to reduce the chance of an infinite loop. For example, if a sub-band is marked as a split sub-band, then it will not be merged back with a sub-band it was split from. A block which is marked as merged, will not be split into the same configuration. Various metrics are utilized for computing tonality, energy, or different shape. A motion vector and a scale metric may be used to encode an extended sub-band. If by splitting a sub-band into two sub-bands creates a significantly different energy in the scale factor (e.g., > (1 + b), where b is 0.2 - 0.5), then the sub-band can be split. In one example, tonality is computed in the fast fourier transform (FFT) domain. For example, an input signal is divided into fixed blocks of 256 samples, and the FFT is run on three adjacent FFT blocks. A time average is performed on three adjacent FFTs outputs to get a time averaged FFT output for the current block. A median filter is run over the three time averaged FFT outputs to get a baseline. If a coefficient is above a certain threshold above the baseline, then the coefficient is classified as tonal, and the percentage that it is above the baseline is a measure of the tonality. If the coefficient is below the threshold, then it is not tonal and the measure of tonality is 0. The tonality for a particular time frequency tile is found by mapping the dimensions of the tile to the FFT blocks and accumulating the tonality measure over the block. The threshold that a coefficient has to be over the baseline can be defined to be either an absolute threshold, a ratio relative to the baseline, or a ratio relative to the variance of the baseline. For example, if the coefficient is above one local standard deviation from the baseline (median filtered, time averaged), it can be classified as being tonal. In such a case, the corresponding translated sub-band in the MLT representing the tonal FFT blocks is labeled tonal, and may be split. The discussion is concerned with the magnitude of the FFT as opposed to the phase. With respect to the MSE metric on different shapes, a metric of much lower MSE may vary substantially on the bit rate. For example, with higher bit rates, if the MSE goes down by approximately 20%, then a split determination may make sense. However, at lower bit rates the split decision may occur at a 50% lower MSE. Exemplary Variable Band Multiplier and Coding After sub-bands are split and or merged, the ratio between the original smallest sub-band size and the new smallest sub-band size is computed. A ratio is defined as minRatioBandsize = max(l, original smallest sub-band size / new smallest sub-band size). Then, the optimized sub-band with the smallest size (e.g., number of coefficients in the sub-band) is assigned a sub-band multiplier of 1, and the other sub-band sizes have a band multiplier set as round(this sub-band size / smallest sub-band size). Thus, sub-band multipliers are integers greater than or equal to 1, and minRatioBandsize is also an integer greater than or equal to 1. The sub-band multipliers are coded by essentially coding a difference between the expected sub-band multiplier and the optimized sub-band multiplier using a table-less variable length code. A difference of 0 is coded with 1 bit, a difference which is one of the 15 smallest possible differences excluding 0 are coded with 5 bits, and the rest of the differences are coded using a table-less code. As an example, consider the following case where the sub-band sizes for a default non-uniform case are given as shown in Table 4. Table 4 (Table Removed) Assume further, that after splitting/merging, the following optimized sub-band configuration is created as shown in Table 5. Table 5 (Table Removed) Figure 14 is a diagram of an exemplary series of sub-band size transformations. For example, the sub-band sizes in Table 5 can be attained from the Table 4 via the transformations of Figure 14. Using the above formula for minRatioBandSize = max(l, 4/2) = 2, the minimum ratio sub-band size of 2 is provided, and the values for band size multipliers can be obtained as shown in Table 6. Table 6 (Table Removed) A method is used to calculate the expected sub-band multiplier. First, assume that blocks which are not split or merged should have the default band size multiplier (expected band size multiplier = = actual band size multiplier). This saves bits since only changes from the expected band size multiplier need to be encoded. Further, the smaller the modification is from the default band configuration, fewer bits are needed to encode the configuration. Otherwise, the expected band multiplier is computed at the decoder using the following logic. • See which sub-band in the default configuration we are currently decoding by looking at the starting point of the actual band and comparing with the starting and ending points of the bands in the default band configuration. • The expected band multiplier is calculated by taking the number of coefficients left within the band in the default configuration and dividing by the smallest block (sub-band) size in the actual configuration. For example, let Sd[j] be the starting position of the 'j'th band in the default band configuration, let sa[j] be the starting position of the 'j'th band in the actual band configuration, let md be the minimum band size in the default case, and let ma be the minimum band size in the actual case. Then, calculate the following, (Equation Removed) where 'r' is the minRatioBandSize, and a[j] is the band multiplier for the 'j'th band. To calculate the expected multiplier for the 'j'th band, first compute '!', the index of the default band configuration which contains the starting position of the actual band. Then, compute aexpected[j] to be the expected multiplier of the 'j'th band. This can be computed as follows, (Equation Removed) Note that if a band is not split or merged, then the expected band multiplier will be the same as the actual one. Also, so long as sd[i+l] is the same as sa[j+l], then the expected band multiplier will be the same as the actual one. Continuing with the example, a default sub-band configuration is shown in Table 7. Table 7 (Table Removed) The actual or optimized sub-bands as they map to the default band configuration is shown in Table 8. Table 8 (Table Removed) The Default Band Index is the value of T for a given j. Coefficients Left is Sd[i+l] - Sa[j]- The Expected Band Multiplier is aexpeted[j] and Band Multiplier is a[j]. Again, note that any sub-band which is not split or merged will always have a difference of 0. The coding will code the "Difference" value for each sub-band and the minRatioBandSize (V) for the configuration using a variable length code for each. The use of minRatioBandSize allows coding a band configuration in which the smallest bands are smaller than the bands in the default configuration. Computing Environment Figure 15 illustrates a generalized example of a suitable computing environment (1500) in which the illustrative embodiments may be implemented. The computing environment (1500) is not intended to suggest any limitation as to scope of use or functionality of the invention, as the present invention may be implemented in diverse general-purpose or special-purpose computing environments. With reference to Figure 15, the computing environment (1500) includes at least one processing unit (1510) and memory (1520). In Figure 15, this most basic configuration (1530) is included within a dashed line. The processing unit (1510) executes computer-executable instructions and may be a real or a virtual processor. In a multi-processing system, multiple processing units execute computer-executable instructions to increase processing power. The memory (1520) may be volatile memory (e.g., registers, cache, RAM), non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or some combination of the two. The memory (1520) stores software (1580) implementing an audio encoder and or decoder. A computing environment may have additional features. For example, the computing environment (1500) includes storage (1540), one or more input devices (1550), one or more output devices (1560), and one or more communication connections (1570). An interconnection mechanism (not shown) such as a bus, controller, or network interconnects the components of the computing environment (1500). Typically, operating system software (not shown) provides an operating environment for other software executing in the computing environment (1500), and coordinates activities of the components of the computing environment (1500). The storage (1540) may be removable or non-removable, and includes magnetic disks, magnetic tapes or cassettes, CD-ROMs, CD-RWs, DVDs, or any other medium which can be used to store information and which can be accessed within the computing environment (1500). The storage (1540) stores instructions for the software (1580) implementing the audio encoder and or decoder. The input device(s) (1550) may be a touch input device such as a keyboard, mouse, pen, or trackball, a voice input device, a scanning device, or another device that provides input to the computing environment (1500). For audio, the input device(s) (1550) may be a sound card or similar device that accepts audio input in analog or digital form. The output device(s) (1560) may be a display, printer, speaker, or another device that provides output from the computing environment (1500). The communication connection(s) (1570) enable communication over a communication medium to another computing entity. The communication medium conveys information such as computer-executable instructions, compressed audio or video information, or other data in a modulated data signal. A modulated data signal is a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired or wireless techniques implemented with an electrical, optical, RF, infrared, acoustic, or other carrier. The invention can be described in the general context of computer-readable media. Computer-readable media are any available media that can be accessed within a computing environment. By way of example, and not limitation, with the computing environment (1500), computer-readable media include memory (1520), storage (1540), communication media, and combinations of any of the above. The invention can be described in the general context of computer-executable instructions, such as those included in program modules, being executed in a computing environment on a target real or virtual processor. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or split between program modules as desired in various embodiments. Computer-executable instructions for program modules may be executed within a local or distributed computing environment. For the sake of presentation, the detailed description uses terms like "determine," "get," "adjust," and "apply" to describe computer operations in a computing environment. These terms are high-level abstractions for operations performed by a computer, and should not be confused with acts performed by a human being. The actual computer operations corresponding to these terms vary depending on implementation. In view of the many possible embodiments to which the principles of our invention may be applied, we claim as our invention all such embodiments as may come within the scope and spirit of the following claims and equivalents thereto. We claim: 1. An audio encoding method, comprising: transforming an audio signal into spectral data (320); coding a baseband portion of the spectral data (340); in an extended band portion of the spectral data, determining characteristics of spectral data (360); coding an altered configuration of sub-bands (360) comprising data indicating individual sub-bands in the extended band altered from an initial configuration. 2. The audio encoding method of claim 1 wherein the spectral data comprises coefficients in a transform domain and the altered configuration comprises difference values for sub-bands altered in size from the initial or default configuration. 3. The audio encoding method of claim 1 wherein the initial configuration is a uniform split configuration or a non-uniform split configuration. 4. The audio encoding method of claim 2 wherein a first bit is provided for coding whether a band configuration is default or optimized, and a second bit is provided for coding whether the initial configuration is the uniform split configuration or the non- uniform split configuration. 5. The audio encoding method of claim 1 wherein the altered configuration comprise sub-band multipliers that reflect the relative ratio of a sub-band size to the smallest sub- band size. 6. The audio encoding method of claim 1 wherein the altered configuration comprises sub-band multipliers reflecting splits and merges of sub-bands from the initial configuration. 7. The audio encoding method of claim 1 wherein characteristics of spectral data comprise a measure of at least one of tonality, energy, or shape. 8. The audio encoding method of claim 1 wherein the initial configuration is altered at least in part based on tonality, and the method further comprises: transforming the audio signal into fast fourier transform blocks; time averaging adjacent fast fourier transform blocks; determining a median filtered value by median filtering the time averaged adjacent fast fourier transform blocks; comparing the time averaged adjacent fast fourier transform blocks to the median filtered value to obtain a tonality number; determining a corresponding sub-band related to the adjacent fast fourier transform blocks; and assigning a tonal characteristic to the corresponding sub-band if the tonality number is above a threshold which can be represented by an absolute number, a given percentage of the median filtered value, or a percentage of a local standard deviation of the median filtered value. 9. The audio encoding method of claim 8 wherein the tonal characteristic is at least one of the factors used to determine whether or not to split or merge the corresponding sub-band. 10. The audio encoding method of claim 1 wherein a ratio of energy in adjacent sub-bands is at least partially determinative of whether or not to alter the initial configuration. 11. The audio encoding method of claim 1 wherein sub-band shape differentiation is at least partially determinative of whether or not to split a sub-band. 12. The audio encoding method of claim 1 wherein a decision to split an individual sub-band into two sub-bands is at least partially made when the two split sub-bands have a means-square Euclidean difference that is lower than the individual sub-band by a threshold amount. 13. The audio encoding method of claim 1 wherein coding the altered configuration further comprises coding a minimum ratio sub-band size. 14. An output bit-stream created using the method of claim 1. 15. A decoder decoding an output of claim 1. 16. An audio decoding method, comprising: decoding an encoded baseband (540); decoding an encoded extended band comprising, receiving data comprising a minimum ratio sub-band size and an altered configuration (545), determining a smallest sub-band size in the altered configuration by dividing the smallest sub-band size in the default configuration by the minimum ratio sub-band size (545), and determining the actual sub-band multiplier by adding an expected sub-band multiplier to a coded difference value (545). 17. The audio decoding method of claim 16 wherein the initial configuration is a non- uniform split configuration. 18. The audio decoding method of claim 16 wherein for a second sub-band, data received indicates no alteration from the initial configuration and the second sub-band is decoded according to the initial configuration. 19. An audio encoder comprising: a transformer (320) for transforming an audio signal into spectral data; a base coder (340) for coding a baseband portion of the spectral data; an extended band coder (350, 360) for, configuring variable sized sub-bands based on characteristics of spectral data in an extended band (360), coding difference values indicating how individual sub-bands differ in size from an initial configuration (360), coding a minimum ratio sub-band size (360), and coding sub-bands in the extended band (350). 20. The audio encoder of claim 19 wherein difference values are determined at least in part by sub-band split or merge from the initial configuration.