Patent Description:
The present invention relates to a method for generating an intermediate filter definition signal, especially for the field of encoding, decoding, manipulating and filtering of audio signals, e.g. in the field of HRTF (head related transfer function).

It has been shown in [<NPL>], that a complex-exponential modulated filter bank is an excellent tool for spectral envelope adjustment of audio signals. One application of this feature is audio coding based on Spectral Band Replication (SBR). Other fruitful applications of a complex filter bank include frequency selective panning and spatialization for parametric stereo, see [<NPL>] and parametric multichannel coding, see [<NPL>]. In those applications the frequency resolution of the complex filter bank is further enhanced at low frequencies by means of sub-subband filtering. The combined hybrid filter bank hereby achieves a frequency resolution that enables the processing of spatial cues at a spectral resolution which closely follows the spectral resolution of the binaural auditory system.

In some applications, however, the resolution of the filter bank is still insufficient, in the sense that simple gain modifications in each subband do not suffice to truthfully model the action of a given filter. For binaural rendering of multi-channel audio by means of HRTF (head related transfer function) related filtering, the intricate phase characteristics of the filters are important for the perceived audio quality. It is of course possible to apply fast convolution methods based on the DFT (Discrete Fourier Transform) as a post-process to the multi-channel rendering, but if the rendering device already contains the signals in the subband domain of complex exponential modulated filter bank, there are significant advantages in terms of computational complexity and algorithmic integration in performing the HRTF derived filtering in the subband domain, which will be outlined in more detail later. Since HRTF's are different for each individual and the derived filters depend on virtual source and/or listener positions which can for instance be changed by control signals, user interfaces or by other description signals, it is also important to be able to efficiently convert a given HRTF related filter into subband domain filters.

<CIT> B discloses a filterbank structure which provides a flexible compromise between the conflicting goals of processing delay, filter sharpness, memory usage and band interaction. The filterbank has an adjustable number of bands and a stacking which provides for a selectable shift of band frequencies to one of two discrete sets of center frequencies. The width of the bands and hence the number of the bands is selected depending upon acceptable delay, memory usage, and processing speed required. The flexibility in terms of stacking of the bands provides twice the number of potential band edge placements, which is advantageous for hearing loss fitting, especially at low frequencies. The same filter coefficients can be used for analysis and synthesis, to reduce memory usage.

<CIT> discloses a method of matched parcelling into sub-bands suppressing or strongly limiting spectral aliasing comprising the steps of: - transforming a digital source signal into at least two distinct frequency sub-bands; - processing each of the frequency sub-bands by filtering incorporating the steps of defining a global filtering profile, determining a set of partial filtering profiles each associated with one of the sub-bands; and filtering each of the sub-bands according to the partial filtering profile associated with it; and - inversely transforming the filtered sub-bands, delivering a reconstructed filtered signal, the partial filtering profiles determined during the determination step being constrained in such a way that the said reconstructed filtered signal corresponds substantially to the direct filtering of the digital source signal according to the global filtering profile. In particular, these partial profiles are constrained in such a way that the spectral aliasing still present after filtering is substantially compensated.

The present invention is defined by a method for converting an impulse response signal according to independent claim <NUM> and a computer program according to independent claim <NUM>. Advantageous features are set out in the dependent claim.

The present invention will now be described by way of illustrative examples, not limiting the scope of the invention, with reference to the accompanying drawings, in which:.

The below-described embodiments are merely illustrative for the principles of the present invention of efficient filtering with a complex modulated filterbank. It is understood that modifications and variations of the arrangements and the details described herein will be apparent to others skilled in the art. It is the intent, therefore, to be limited only by the scope of the impending patent claims and not by the specific details presented by way of description and explanation of the embodiments herein.

In the following, objects with the same or similar functional properties are denoted with the same reference signs. Unless explicitly noted otherwise, the description with respect to objects with similar or equal functional properties can be exchanged with respect to each other.

<FIG> illustrates in the form of a system comprising embodiments of both a filter apparatus and a filter generator the processing of a digital audio signal by means of subband filtering according to the present invention. This signal path can for instance represent a part of a spatial audio rendering system where the input is a received audio channel and the output is a component of a signal to be played back at the right ear. The input signal (Digital audio signal or time domain input signal) is analyzed by the complex analysis bank <NUM> by means of filtering with a set of L analysis filters followed by downsampling of a factor L, wherein L is a positive integer, preferably larger than <NUM>. Typically the factor L is a power of <NUM>, preferably L = <NUM>. The analysis filters are usually obtained by a complex modulation of a prototype filter p(ν), wherein ν is a positive integer indicating an index in an array of data or an index of a value in a signal not downsampled by factor L. The output of the filter bank consists of L subband signals that are processed by a subband filtering <NUM>. This subband filtering consists of a combination of manipulations such as subband gain adjustment according to received control data and application of finite impulse response filters applied separately in each subband. The filter taps of the subband filters are obtained from an (inventive) filter converter <NUM> as an embodiment of a filter generator which takes as input a filter described by direct form filter taps, a frequency domain description or an impulse response (signal). The complex synthesis bank <NUM> reconstructs an output signal by means of upsampling by a factor L, filtering by L synthesis filters, summation of all the results, and extraction of the real part. The summation of all the results and the extraction of the real part can also be switched with respect to their order, as will be outlined more closely with respect to <FIG> and <FIG>.

<FIG> shows a complex analysis bank <NUM> in more detail. The complex analysis bank <NUM> comprises a plurality of L intermediate analysis filters <NUM> for each subband to be output by complex analysis bank <NUM>. To be more precise, each of the L intermediate analysis filters <NUM> is connected in parallel to a node <NUM> to which the time domain input signal to be processed is provided. Each of the intermediate analysis filters <NUM> is adapted for filtering the input signal of the complex analysis bank <NUM> with respect to a center frequency of each subband. According to the center frequencies of the different subbands, each subband is labeled by a subband index or index n, wherein n is a non-negative integer, typically in the range from <NUM> to L-<NUM>. The intermediate analysis filters <NUM> of the complex analysis bank <NUM> can be derived from a prototype filter p(ν) by a complex modulation according to the subband index n of the subband to which the intermediate analysis filter <NUM> is applied. More details concerning the complex modulation of a prototype filter are explained below.

Either directly by the intermediate analysis filters <NUM> or by an optional downsampler <NUM> (denoted by dotted line in <FIG>) the sampling frequency of the signal output by the intermediate analysis filter bank <NUM> is reduced by a factor L. As mentioned before, the downsamplers <NUM> supplied to each subband signal output by the corresponding intermediate analysis filters <NUM> are optional as, depending on the concrete implementation, the downsampling can also be carried out in the frame work of the intermediate analysis filters <NUM>. In principle, downsampling the signal output by the intermediate analysis filters <NUM> is not required. Nevertheless, the presence of the explicit or implicit downsamplers <NUM> is a preferred option as the amount of data provided by the complex analysis bank <NUM> would alternatively be raised by a factor of L, leading to a significant redundancy of data.

<FIG> illustrates a possible solution for a complex synthesis bank <NUM>. The complex synthesis bank <NUM> comprises L intermediate synthesis filters to which the L subband signals from the subband filtering <NUM> are provided to. Depending on the concrete implementation of the complex synthesis bank <NUM> prior to the filtering in the frame work of the intermediate synthesis filters <NUM>, the subband signals are upsampled by L upsampler <NUM>, which reconstruct the sampled frequency of the subband signals by increasing the sampling frequency by a factor of L. In other words, the optional upsampler <NUM> reconstruct or reform the subband signals provided to the upsampler <NUM> in such a way that the information contained in each of the subband signals is retained while the sampling frequency is increased by a factor of L. Nevertheless, as already explained in the context of <FIG>, the upsamplers <NUM> are optional components, as the upsampling can also be carried out in the frame work of the intermediate synthesis filters <NUM>. Hence, the step of upsampling the subband signals carried out by the upsampler <NUM> can be simultaneously processed in the frame work of the intermediate synthesis filers <NUM>. If, however, the downsamplers <NUM> are neither explicitly nor implicitly implemented, the upsamplers <NUM> do not have to be implemented explicitly or implicitly.

The intermediate synthesis filters <NUM> are connected via an output to an adder <NUM> which sums up the filtered subband signals output by the L intermediate synthesis filters <NUM>. The adder <NUM> is further connected to a real part extractor <NUM>, which extracts or forms a real valued signal or rather a (real valued) time domain output signal based on the complex valued signal provided by the adder <NUM>. The real part extractor <NUM> can perform this task for instance by extracting the real part of a complex valued signal provided by the adder <NUM>, by calculating the absolute value of the complex valued signal provided by the adder <NUM> or by another method that forms a real valued output signal based on a complex valued input signal. In the case of the system shown in <FIG>, the signal output by the real part extractor <NUM> is the time domain output signal output by the embodiment of the inventive filter apparatus.

The second possible solution for a complex synthesis bank <NUM> shown in <FIG> differs from the first possible solution shown in <FIG> only concerning the real part extractors <NUM> and the adder <NUM>. To be more precise, the outputs of the intermediate synthesis filters <NUM> are connected separately from each subband to a real part extractor <NUM> extracting or forming a real valued signal based on the complex valued signal output by the intermediate synthesis filters <NUM>. The real part extractor <NUM> are then connected to the adder <NUM>, which sums up the L real valued signals derived from the L filtered subband signals to form the real valued output signal provided by the adder <NUM>, which in the case of the system shown in <FIG> is the time domain output signal.

<FIG> shows the subband filtering <NUM> and its interplay with the filter converter <NUM> in more details. The subband filtering <NUM> comprises a plurality of intermediate filters <NUM>, wherein one intermediate filter <NUM> is provided for each complex valued subband signal provided to the subband filtering <NUM>. Hence, the subband filtering <NUM> comprises L intermediate filters <NUM>.

The filter converter <NUM> is connected to each of the intermediate filters <NUM>. As a consequence, the filter converter <NUM> is capable of providing the filter taps for each of the intermediate filters <NUM> of the subband filtering <NUM>. More details concerning the filtering done by the intermediate filters <NUM> will be explained in the further course of the application. Hence, the filters taps provided to the different intermediate filters <NUM> and output by the filter converter <NUM> form the intermediate filter definition signal.

Furthermore, it should be noted that the embodiments, solutions and implementations can comprise additional and/or optional delays for delaying any of the signals or a subset of signals, which have been omitted in <FIG> for the sake of simplicity. Also in the <FIG> optional delays have been omitted for the sake of simplicity. Nevertheless, delays or delayers can be comprised in elements shown (e.g. filters) or added as optional elements in all embodiments depending on their concrete implementation.

<FIG> illustrates the processing of a digital audio signal by means of direct form filtering <NUM>. If the same filter is given as input to the filter converter <NUM> of <FIG> and the direct filtering <NUM>, a design goal for the filter converter <NUM> is that the digital audio output of <NUM> should be perceptually (or audibly) indistinguishable from the digital audio output of the direct filtering <NUM>, if the digital audio inputs to the complex analysis bank <NUM> and the direct filtering <NUM> are identical and the processing in the direct filtering <NUM> consists of pure stationary subband filtering.

In the embodiment of the system shown in <FIG> the filter input to the filter converter <NUM> is given as a filter definition signal, which can for instance comprise the filter taps of a corresponding time domain filter, a frequency domain description (amplitude/frequency characteristic and/or phase/frequency characteristic) or an impulse response signal of the appropriate filter.

In the case of the direct filtering <NUM> the same filter definition signal can in principle be used. Depending on the concrete implementation and the filter definition signal, the filtering can be carried out by direct application of the filter taps in the frame work a digital filter, by a discrete Fourier transform along with a transfer function or another frequency domain description or by means of convolution with the impulse response signal.

<FIG> illustrates a preferred embodiment of a filter converter <NUM> according to the present invention as an embodiment of a filter generator. The filter is assumed to be given by its impulse response. Viewing this impulse response as a discrete time signal, it is analyzed by an L-band complex analysis (filter) bank <NUM>. The resulting subband signal outputs are then exactly the impulse responses of filters to be applied separately in each subband in the subband filtering <NUM>. In the preferred embodiment shown in <FIG>, the filter definition signal provided to the filter converter <NUM> and its complex analysis bank or complex analysis filter bank <NUM> is the impulse response signal indicative of the amplitude/frequency characteristic of a filter, which is to be transferred into the subband domain. Hence, the output of the complex analysis (filter) bank <NUM> of each of the L subbands represents the impulse response of the intermediate filters comprised in the subband filtering <NUM>.

The complex analysis bank <NUM> is in principle derived from the analysis bank <NUM> but it has a different prototype filter and a slightly different modulation structure, the details of which will be outlined in the following description. The same fast algorithms that are used for an implementation of the complex analysis bank <NUM> can be reused for complex analysis bank <NUM>, leading to a very fast and very efficient conversion process.

Moreover, the length of the prototype filter q(v) can be designed to be only a fraction of the length of the prototype filter p(v). Due to the downsampling by a factor L, the length of subband filters are also a factor of L smaller than the sum of the lengths of the given time domain filter and the prototype filter q(v). The computational effort is thus reduced in comparison to the direct form filtering <NUM> by approximately a factor of L/<NUM>. The offset factor of <NUM> is due to the replacement of real filtering with complex filtering. Another offset is the computational cost of the complex analysis and synthesis banks <NUM> and <NUM>. For efficient implementations this cost is comparable to the cost of rather short FIR filters, and hence negligible, as outlined before. Moreover, this offset of the reduction in computational cost does not exist for systems that already employs these two filter banks <NUM> and <NUM>.

<FIG> illustrates an example of a given filter impulse response <NUM>. It consists of <NUM> (= <NUM>·<NUM>) nonzero taps. In other words, the impulse response <NUM> shown in <FIG> comprises <NUM> non-vanishing values.

In the present application, a non-vanishing tap or value is a tap or a value which is ideally not equal to zero. Nevertheless, due to implementation restraints in the frame work of this application a non-vanishing value or tap is a real valued or complex valued tap or value with an absolute value which is larger than a predetermined threshold, e.g. <NUM>-s or <NUM>-s, wherein s is a positive integer depending on the requirements of a concrete implementation. In digital systems this threshold is preferably defined in the binary system (basis <NUM>), wherein the integer s has a predetermined value depending on the specifics of the implementation. Typically, the value s is <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or <NUM>.

The impulse response <NUM> of the system of <FIG> is indistinguishable from this given impulse response at the resolution of the image, in a case where a L = <NUM> band filterbank with a prototype filter of length <NUM> (= <NUM>·<NUM>) is applied and a prototype filter of length <NUM> (= <NUM>·<NUM>) is used for the filter converter <NUM> of <FIG>. The corresponding intermediate subband filters have only <NUM> (= <NUM>+<NUM>-<NUM>) taps each, as will be explained later.

<FIG> illustrates the impulse response <NUM> of the system of <FIG> with a <NUM> band filterbank, in a special case corresponding to prior art usage for envelope adjustment and equalization. In this case, the subband filters or rather intermediate filters <NUM> are all of one tap only, so a constant complex gain is applied to each subband. For each subband, the corresponding gain is chosen to be equal to the complex frequency response of the filter of <FIG> evaluated at the center frequency of the particular subband. As it can be seen from the result, there are severe pre-echo artefact and there will be a significant perceptual difference between the application of this filter response compared to the target impulse response <NUM> of <FIG>.

<FIG> illustrates the magnitude response <NUM> of the filter of <FIG>. The frequency scale of <FIG> is adjusted to the resolution of a <NUM> band filter bank ( L = <NUM>).

<FIG> illustrates the magnitude response <NUM> of the filter underlying the impulse response <NUM> shown in <FIG>. As it can be seen, the usage of only one gain per subband results in a poor approximation to the desired frequency response. The main reason for this is the fast variation of the target phase spectrum. In fact, this prior art method is better suited at modeling linear phase responses.

<FIG> finally compares the performance of an embodiment of the present invention and of the prior art method of complex gain adjustment of subbands. The dotted curve is a redrawing of the target magnitude response <NUM> of <FIG>. The dashed curve <NUM> is the magnitude response of the difference between the complex frequency responses of the target filter and its approximation by the prior art method. The solid curve <NUM> is the magnitude response of the difference between the complex frequency responses of the target filter and its approximation by the method taught by the present invention with the parameters as discussed during the description of <FIG>. As it can be seen, the error of the prior art method is small only at the <NUM> midpoints of filter bank subbands whereas the inventive method leads to an approximation quality in the <NUM> dB range. It should be pointed out that this is also the level of performance one measures when comparing the output of the inventive system to the output of the reference system for an arbitrary input signal.

As the comparison of the two curves <NUM> and <NUM> in <FIG> shows, an embodiment of an inventive filter apparatus, an embodiment of a filter generator and a system comprising both embodiments offer a significant advantage concerning the quality of the manipulation of an input signal. The significant difference concerning the quality of filtering (or manipulation) of the input signal outlined above is a consequence of the fact that at least one of the intermediate filters <NUM> has an impulse response with two or more non-vanishing values. In other words, at least one of the intermediate filters <NUM> comprises at least two non-vanishing filter taps. Furthermore, it is important to note that the number of subbands L processed by an embodiment of a filter apparatus is larger or at least equal to <NUM>. Nevertheless, the number of subbands L is significantly smaller than the number of frequency bands required for a comparable quality in the case of a Fourier transform-based filtering combined with a filter mainly described by an amplitude/frequency characteristic and/or a phase/frequency characteristic as the transfer function of the filter.

Due to the fact that the impulse response of the intermediate filters <NUM> are significantly shorter than the impulse response of the underlying filter characteristic in the time domain, the computations with respect to each subband can be carried out significantly faster. Furthermore, as the different subband signals can be processed independently, both an embodiment of the filter apparatus as well as an embodiment of the filter generator <NUM> can process the respective input signals highly efficiently in a fast and a parallel manner. Hence, the processing of both a digital audio input as an input signal as well as an impulse response indicative of a filter characteristic can be carried out highly efficiently in a parallel fashion. As outlined earlier, an embodiment of an inventive filter apparatus as well as an embodiment of an inventive filter generator combine the advantages of both a direct processing of audio signals in the time domain leading to a very high quality and using a combination of a Fourier transform along with a transfer function in the frequency domain offering a high efficiency as each frequency band is only multiplied with a (complex or real valued) tap in the process of filtering the signal.

On the other hand, the disadvantages of both, purely processing the input signals in the time domain, which leads to an enormous computation effort, and those of a Fourier transform, can be significantly reduced and suppressed to a level that the output of an embodiment of a filter apparatus is perceptually indistinguishable from the quality of a direct processing in the time domain.

These two advantages offer a great flexibility for filtering digital signals with varying filtering characteristics. This is especially important in the field of HRTF, as HRTF-related filters usually have a very long impulse response. Hence, an embodiment of an inventive filter apparatus comprising a complex analysis filter bank <NUM>, a plurality of intermediate filters <NUM> in the subband filtering <NUM> and a complex synthesis filter bank <NUM> offers especially in the field of HRTF-related applications significant computational advantages due to the possible parallel processing of subband signals.

Embodiments of a filter generator and embodiments of systems comprising both a filter apparatus and a filter generator offer furthermore the advantage that filters can easily be adapted to specific environments, parameters or other specific needs of the application at hand. Especially in terms of HRTF-related applications, an embodiment of such a system can be used in head-tracking applications, in which several sources of sounds and noises as well as the position of the listener vary over time. Such an embodiment of a system comprising a filter apparatus and a filter generator therefore offer a highly efficient and flexible way to present an audio impression of a three dimensional arrangement of sound sources with respect to a varying position and orientation of a hypothetical listener via headphones or other head-related sound systems (stereo sound systems).

As this last example illustrates, an embodiment of an inventive filter apparatus along with an inventive filter generator offers not only a highly efficient system for audio manipulation with an excellent quality but also a very flexible way to introduce altering audio impressions in an efficient way.

In the following, let <MAT> be the discrete time Fourier transform of a discrete time signal z(v). As before, ν is an integer indicating an index or a time index of a time signal, while ω = <NUM>π · f is the circular frequency associated to the frequency f, π is the circular number (π = <NUM>. ) and <MAT> is the imaginary unit.

The complex exponential modulated L-band filterbank is defined from a real valued prototype filter p(v) of finite length. For the computations below it will be assumed by extension with zeros that the prototype filter is defined for all integers v. Given a real valued discrete time signal x(v) the analysis filter bank <NUM> applies, as already explained, the complex modulated prototype filters followed by downsampling by a factor L in order to output the subband signals, <MAT> for each subband index n = <NUM>,<NUM>,. ,L-<NUM> , and integer time index k. The time index k differs from the time index ν with respect to the fact that k refers to the downsampled signals, whereas the integer ν withers to signals with the full sample frequency.

Given complex valued subband signals dn(k), the synthesis filter bank <NUM> applies filtering followed by upsampling by a factor of L and a real value extraction in order to output the real valued signals, as already explained, to obtain the output signal <MAT>.

In the equations (<NUM>) and (<NUM>) θ and Ψ represent (constant) phase factors for filtering the real valued discrete time signal x(v) into complex valued subband signal and for reconstructing real valued output samples y(v) from complex valued subband signals dn(k). It is well known that a prototype filter and fixed phase factors θ and Ψ can be chosen to give perfect reconstruction, y(v) = x(v), in the case where dn(k)=cn(k), that is when the subband signals are unaltered. In practice, the perfect reconstruction property will hold true up to a delay (and/or a sign change), but in the computations that follow, this detail will be ignored by allowing the use of an acausal prototype filter. The present invention is applicable to the pseudo QMF type of design as taught by <CIT> "Aliasing reduction using complex exponential modulated filter banks". Here the prototype filter is symmetric p(-v) = p(v) , and its discrete time Fourier transform P(ω) essentially vanishes outside the interval |ω|≤π/L. The perfect reconstruction is also replaced by a near-perfect reconstruction property. For the derivation that follows it will be assumed for simplicity that both perfect reconstruction holds and that P(ω) = <NUM> for π/L<|ω|≤π.

Moreover, the phase factors are assumed to satisfy the condition that Ψ-θ is equal to an integer multiple of <NUM>L.

In a critically sampled filter bank, the alteration of subband signals prior to synthesis usually leads to the introduction of aliasing artifacts. This is overcome here due to the fact that an oversampling by a factor two is introduced by using complex valued signals. Although the total sampling rate of the subband samples is identical to the sampling rate of the discrete time input signal, the input signal is real valued and the subband samples are complex valued. As it will be outlined below, the absence of alias opens the door for efficient time invariant signal processing.

Consider the modification of subband filtering <NUM> of each subband signal obtained by filtering the analysis samples cn(k) from the complex analysis bank <NUM> with a filter with impulse response gn(k) prior to the synthesis (<NUM>) performed by the complex synthesis (filter) bank <NUM> <MAT>.

Elementary computations show that given the assumptions on the frequency response of the prototype filter, the resulting effect on the reconstructed time signal is that of a discrete time filtering <MAT> where <MAT> Here, <MAT> is the discrete time Fourier transform of the filter applied in subband n for n ≥ <NUM> and <MAT> where * denotes complex conjugation. Observe here that the special case Gn(ω) = <NUM> leads to G(ω) = <NUM> in (<NUM>) due to the assumed special design of the prototype p(v), which implies <MAT>.

Another case of interest is Gn(ω) = exp(-iω) which leads to G(ω) = exp(-iLω), so that y(v) = x(v - L).

Let H(ω) be a given filter (e.g. transfer function) with real valued impulse response h(v). This data is considered as input to the filter converter <NUM>. In view of (<NUM>) and (<NUM>), a trivial choice for the subband filters which result in the desired response G(ω)=H(ω) is given by <MAT>.

The drawback of this formula is that although H(ω) is a smooth function of ω, the periodized segment of it defined by (<NUM>) will exhibit jumps and the impulse response of the subband filters will be unnecessarily long. The prior art usage of the complex pseudo QMF bank for equalization or envelope adjustment consists of applying a single gain gn in each subband, which results in the transfer function <MAT> with the extension <MAT> for n<<NUM> defined in accordance with (<NUM>). In view of (<NUM>), one achieves <MAT> and the transfer function is interpolated between those frequencies. For target filter responses H(ω) that vary slowly as a function of the frequency ω, a first method of approximating the filter is therefore obtained by choosing <MAT>.

An example of the resulting quality of this procedure is given in <FIG> and <FIG>.

According to an embodiment of the present invention a filter generator or a filter converter <NUM> is used to teach to convert the filter (defined by its impulse response) h(v) into intermediate subband filters <NUM> by means of the second analysis filter bank <NUM> which employs real valued prototype filter q(v), <MAT>.

In terms of Fourier transforms this reads <MAT>.

The advantage of this procedure is that any given filter h(v) can be efficiently transformed into intermediate subband filter responses. If q(v) has KQ·L taps, a time domain filter h(v) of KH·L taps is converted into subband domain filters (<NUM>) with KH +KQ -<NUM> taps, wherein KH and KQ are positive integers. With respect to the exemplary numbers given in the context of the description of <FIG>, KH and KQ are equal to <NUM> and with a prototype filter length and an impulse response corresponding to a length of <NUM> · <NUM> = <NUM> (L = <NUM>) each. Hence, each intermediate subband filter <NUM> has an impulse response length of only <NUM> + <NUM> - <NUM> = <NUM> taps each.

Insertion of (<NUM>) into (<NUM>) yields <MAT>.

Hence, the condition for G(ω)=H(ω) to hold is that <MAT> where δ[l]=<NUM> for l=<NUM> and δ[l]=<NUM> for l≠<NUM>. A simple solution to (<NUM>) is given by the brick wall filter <MAT>.

This prototype filter corresponds to the choice (<NUM>) and has the disadvantage of having an infinite and slowly decaying impulse response q(v). Instead, the present invention teaches to solve (<NUM>) approximately (e.g. in the least-square sense) with a finite impulse response filter q(v). The time domain equivalent of (<NUM>) is the system of linear equations for n=<NUM>,<NUM>,. ,L-<NUM> and for all integers k, <MAT> where <MAT> is the autocorrelation of p(v). For any given support length the system of linear equations (<NUM>) can be solved in the least squares sense for a prototype filter q(v). It is desirable to use a support significantly shorter than that of the original filter bank prototype filter p(v), and in that case the linear system (<NUM>) is over-determined. A given quality of approximation can also be traded for other desirable properties via joint optimization. One example of such a property is a low pass type of frequency response Q(ω).

In the following the determination of a multi-slot QMF representation (subband domain) of the HRTF filters is described. The filter conversion from the time domain into the complex QMF subband domain is performed by an FIR filter in the filter converter <NUM> of <FIG>. To be more precise, the following description outlines a method for implementing a given FIR filter h(v) of length Nh in the complex QMF subband domain. The principle of the operation is illustrated in <FIG> in the case of a system also comprising an embodiment of an inventive filter apparatus.

The subband filtering itself is carried out by a set of or a plurality of intermediate filters <NUM> inside the subband filtering <NUM>. To be more precise, the subband filtering consist of the separate application of one complex valued FIR intermediate filter gn(l) for each QMF subband with an index n = <NUM>,<NUM>,. In other words, in the following description special references will be made to embodiments with L = <NUM> different subband signals. Nevertheless, this specific number of subband signals is not essential and the appropriate equations will also be given in a more general form.

One of the key components of the system shown in <FIG> is the filter converter <NUM>, which converts the given time domain FIR filter h(v) into the complex subband domain filters gn(l). The filter converter <NUM> comprises a complex analysis bank <NUM> similar to the QMF analysis bank <NUM>. The prototype filter of the complex analysis filter bank <NUM> of the filter converter <NUM> q(v) of length <NUM> (= <NUM>·<NUM>) for the specific case of L = <NUM> subband signals are created by solving in the least square sense the over determined system of the equation (<NUM>). The filter coefficients q(v) or rather the relations they fulfill will be described in more detail for the case of L = <NUM> subbands signals later on.

To be more accurate in terms of mathematical description, an extension with zeros in the time domain FIR filter is defined by <MAT>.

The resulting intermediate subband domain filters are based on equation (<NUM>) and can be expressed in the general case as <MAT> wherein l<NUM> and ν<NUM> are delays, l is an integer indicating an index of the filter taps and Nq (= NQ) is the length of the impulse response of the prototype filter q(ν).

It should be noted, that in the frame work of the present application under an equation being based on an equation an introduction of additional delays (cf. l<NUM> and ν<NUM>) factors, additional coefficients and an introduction of a window function or another simple function is understood.

In the case L = <NUM>, the expression for the subband domain filters or intermediate filters <NUM> becomes <MAT>.

These subdomain filters have a length Lq = Kh + <NUM>, where <MAT> and Nh is the length of the impulse response h(ν) of the filter characteristics to be transferred into the subband domain.

In this case, the integer n = <NUM>, <NUM>,. , <NUM> is once again the index of a subband and l = <NUM>, <NUM>,. , (Kh+<NUM>) is an integer indicating taps of the resulting intermediate filters <NUM>.

The extra addend of (-<NUM>) in equation (<NUM>) as compared to equation (<NUM>) is there, because equation (<NUM>) was developed without any regard to casualty of filters. Real implementations will cause always introduce delays. Hence, depending on the concrete implementation, additional delayers or delays can be implemented in the embodiments shown in <FIG> and <FIG>, which have been omitted for the sake of simplicity in Figures mentioned.

As outlined earlier, in many cases the system of linear equations (<NUM>) is over determined. Nevertheless, it can be solved or approximated in the least square sense with respect to the prototype filter coefficients q(ν). Solving the system of linear equations (<NUM>) in the least square sense, leads to the filter taps of the prototype filter q(v) to fulfill the following relations for integers ν from <NUM> to <NUM>: <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

To be more precise, the filter coefficients q(v) obey the following relations: <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

Even more accurately, the filter coefficients q(v) can be expressed by the following equations for the integer ν in the range between <NUM> and <NUM>, wherein according to the requirements and specifications of special implementations, the prototype filter coefficients may deviate from the following equations either individually or from the maximum absolute value typically by <NUM>%, <NUM>% or <NUM>% and preferably by <NUM>% or <NUM>%: <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT> <MAT>.

Hence, the present invention relates to the application of an arbitrary filter to a signal which is available in the transform domain of a complex exponential modulated filter bank, when this filter bank is designed to give virtually alias free performance of operations like equalization, spectral envelope adjustment, frequency selective panning, or frequency selective spatialization of audio signals. The present invention permits to efficiently transform a given finite impulse response (FIR) filter in the time domain into a set of shorter FIR filters, to be applied with one filter for each subband of the filter bank.

The present invention also teaches how to convert a given discrete time domain filter into to a set of subband domain filters. The result is that any given filter can be implemented with a high degree of accuracy in the subband domain of a complex exponential modulated filter bank. In a preferred embodiment, the filter converter consists of a second complex exponential modulated analysis filter bank. For the special case of filters that implement a pure delay, the methods of the present invention coincides with that of <CIT> "Advanced processing based on a complex-exponential modulated filterbank and adaptive time framing".

Furthermore, the present invention comprises the following features:.

Whereas the above derivation is based on complex modulated filter banks, a note can be made here for the critically sampled real representation obtained by a cosine modulated filter bank defined by taking the real part of the subband samples (<NUM>) for an appropriate phase factor θ. In this case it is no longer feasible to use the in-band subband filtering method (<NUM>) to obtain a good approximation to a given filter. However, due to the assumptions made on the prototype filter response, a generalization to a multiband filter of the type <MAT> will be applicable, (with obvious modifications for the first and last subbands). Due to the critical sampling there is much less freedom in the construction of the filter mask <MAT>. One has to do the following, which is obvious for those skilled in the art. For each m=<NUM>,<NUM>,. ,L-<NUM>, use the elementary subband signal dn(k)=δ[n-m]δ[k] as input to the real synthesis bank, and filter the resulting output y(v) with the filter h(v) to get the filtered synthesis waveform z(v). Now use this filtered waveform as input to the real analysis bank. The resulting subband signal carries the coefficients of the masks <MAT> for n+r =m. Some reduction in work necessary for the filter is obtained by observing that the three cases m=3κ+ε for ε=<NUM>,<NUM>,<NUM> can be processed in parallel by feeding the first synthesis bank with all the corresponding elementary subband signals for each case. Thus the real valued filter converter comprises three real synthesis and three real analysis bank operations. This parallel computation represents an implementation short cut for real valued filter converter for the case of a QMF band with good side lope suppression.

<FIG> illustrates an embodiment of an inventive filter apparatus for filtering a time domain input signal of an inventive filter apparatus to obtain a time domain output signal. As already mentioned in the context of <FIG>, the filter apparatus of <FIG> comprises a complex analysis filter bank <NUM>, a subband filtering <NUM> and a complex synthesis filter bank <NUM>, which outputs the time domain output signal.

While <FIG> shows a system comprising an embodiment of an inventive filter apparatus along with an embodiment of a filter generator <NUM>, the filter apparatus shown in <FIG> comprises only as an option a filter converter <NUM>, which provides the subband filtering <NUM> with the intermediate filter definition signal, for instance in the form of the filter taps or the impulse response for each of the intermediate filters <NUM> of the subband filtering <NUM>. The filter apparatus shown in <FIG>, comprises additional optional components, which can provide the subband filtering <NUM> with the filter taps for the plurality of intermediate filters <NUM> of the subband filtering <NUM>.

As an example, the filter taps can also be taken from an optional data base <NUM>, which is connected to the subband filtering <NUM>. In one embodiment, the data base <NUM> comprises the complex valued filter taps of the intermediate filters <NUM>. The data base can be implemented as a memory system, for instance in the form of a non-volatile memory system or volatile memory system depending on the concrete implementation. Hence, memory solutions for the data base <NUM> comprise ROM (ROM = read only memory), RAM (RAM = random access memory), flash memory, magnetical memory, optical memory or other memory systems.

Depending on the concrete implementation, a processor or a CPU (CPU = central processing unit) <NUM> can access the data base and provide the filter taps to the subband filtering <NUM> or can also access the data base to provide the corresponding filter taps to the intermediate filters of the subband filtering <NUM>. Hence, such an embodiment comprises a data base <NUM> from which the filter taps for the subband filtering <NUM> can be taken.

In a further embodiment of an inventive filter apparatus, which is also depicted as an option in <FIG>, the CPU <NUM> is capable of on-line calculating the filter taps. In such an embodiment, the CPU <NUM> accesses the data base <NUM> according to a set of parameters provided by the user and/or according to a set of parameters, which are based on further circumstances, reads one or more sets of filter taps for the intermediate filters of the subband filtering <NUM> and calculates, optionally accompanied by an interpolation scheme or another estimation scheme, the desired intermediate filter taps and provides them to the subband filtering <NUM>. In a further embodiment, the CPU <NUM> or another processor or computer system provides the filter taps of the intermediate filters <NUM> to the subband filtering <NUM> without accessing a data base <NUM>. In such an embodiment, the CPU <NUM> or another processor calculates the filter taps and provides them to the subband filtering <NUM>. Examples for such an embodiment will be explained more closely with respect to <FIG>.

In a further embodiment depicted in <FIG>, the CPU <NUM> accesses a further data base <NUM>, reads one or more filter definition signals (e.g. in the form of impulse response signals corresponding to filter characteristic in the time domain), calculates an effective filter definition signal, for instance an appropriate impulse response, and provides the results of this computation to the filter converter <NUM>. In this embodiment, the filter converter <NUM> then provides the subband filtering <NUM> with the appropriate filter taps for the intermediate filters <NUM>. Hence, in this embodiment, the filter converter <NUM> generates the effective subband filters or intermediate filters applied to each individual subband filters of each individual subband signal inside the subband filtering <NUM> leading to a filtering effect audibly indistinguishable from a corresponding filter applied to the time domain input signal (input signal). As consequence, this embodiment is also capable of on-line calculating the filter taps via the filter converter <NUM>.

An example might for instance be a device, which calculates the taps of the intermediate filters <NUM> of the subband filtering <NUM> according to a set of parameters for instance provided by the user, wherein the parameter basis is so large, that an effective predetermination of the filter taps, optionally accompanied by some sort of interpolation scheme, would not lead to the desired results.

A more concrete application comes for instance of the field of dynamic chance of HRTF filters in one domain to be converted to the subband or QMF domain. As mentioned before, this is for instance relevant in applications involving a head-tracker in which the data base <NUM> is an HRTF data base comprising the time impulse responses of the HRTF filters. As the HRTF filters usually have very long impulse responses, the use of such a scheme is especially interesting, as the taps for the intermediate filters <NUM> or the QMF taps are complex. Storing the data base in this domain would roughly double the memory requirements compared to the memory requirement of storing the impulse responses in the time domain. However, the advantage of the reduced memory requirement can also be employed without having a CPU <NUM> which calculates the impulse response provided to the filter converter <NUM>. Instead, the data base <NUM> can be simply be prompted to output the corresponding definition signal, which might be an impulse response in the time domain to the filter converter <NUM>.

In <FIG>, an amplitude/frequency characteristic <NUM> is illustrated in the frequency domain. In some applications, as explained before, the filter coefficients or filter taps are the intermediate filters <NUM> of the subband filtering <NUM> can be stored in the data base like the data base <NUM> of <FIG>. Alternatively or additionally, for some applications, the filter taps of the intermediate filters can also be calculated by the CPU <NUM> of <FIG>. In the case of a special effect filtering or a lower quality signal processing, in which aliasing effects might become tolerable (at least to some extend), the filter taps of the intermediate filters <NUM> after subband filtering <NUM> can be estimated without a filter converter <NUM> or another embodiment of a filter generator. Possible applications especially comprise voice transmission over low quality lines, like telephones or small band radio communications. Hence, in these applications a determination of the filter taps corresponding the transfer function <NUM> of <FIG> or another amplitude/frequency characteristic into several subbands <NUM> with different subband frequencies can be carried out without employing an inventive filter converter.

<FIG> shows an embodiment of an inventive filter converter <NUM>. As previously outlined in the context of <FIG>, the filter converter <NUM> comprises a complex analysis filter bank <NUM> to which a (real valued) impulse response signal indicative of an amplitude/frequency filter characteristic can be supplied via an input 104a and via an optional switch <NUM>. As outlined before, the complex analysis filter bank <NUM> converts the impulse response signal into a plurality of complex valued subband signals and the intermediate filter definition signal output at an output 104b of the filter converter. As indicated in <FIG> and <FIG>, the output 104b of the filter converter <NUM> can be connected to a subband filtering <NUM>.

As already mentioned earlier, each of the complex valued subband signals of the complex modulated filter bank <NUM> corresponds to an impulse response for one of the intermediate filters <NUM> for a subband signal in the subband filtering <NUM> shown in <FIG> and <FIG>. Typically, the complex valued subband signals are significantly shorter than the impulse response signal of the filter characteristic provided at the input 104a in the time domain. Furthermore, typically at least one of the complex valued subband signals output at the output 104b comprises at least two different non-vanishing values. Especially the last feature distinguishes the output of the filter converter <NUM> from a simple gain adjustment in the frame work of filtering using a direct Fourier transform procedure.

If, however, the filter converter <NUM> is not provided with an impulse response signal indicative of an amplitude/frequency filter characteristic, but a filter definition signal, which comprises at least one of an amplitude/frequency filter characteristic, a phase/frequency filter characteristic or the filter taps in the time domain or another domain of a filter, the filter converter <NUM> comprises an impulse response generator <NUM> for converting the filter definition signal into the impulse response signal, which is then provided via the optional switch <NUM> to the complex analysis filter bank <NUM>. In a concrete implementation, the impulse response generator <NUM> can for instance calculate the impulse response signal provided to the complex analysis filter bank <NUM> by superposition of real valued oscillations (Fourier synthesis), wherein the amplitude characteristics and the phase characteristics of the intended filter to be transferred into the complex subband domain are regarded as defined by the definition signal provided to the input 104c. In other words, if at least one of an amplitude/frequency characteristic and a phase/frequency characteristic is applied to the impulse response generator <NUM>, an impulse response signal can be computed by the impulse response generator <NUM> by supposition of (harmonic) oscillations considering the amplitude and phase relations as defined by the filter definition signal.

Possible applications of both embodiments of the filter apparatus and the filter generator and especially in the field of high quality audio coding and decoding.

Recent developments in audio coding have provided means to obtain a multi-channel signal impression over stereo headphones. This is commonly done by downmixing a multi-channel signal to stereo using the original multi-channel signal and HRTF filters. It has been shown in prior art that the parametric multi-channel audio decoder can be combined with a binaural downmix algorithm making it possible to render a multi-channel signal over headphones without the need for first re-creating the multi-channel signal from the transmitted downmix signal, and subsequently downmixing it again by means of the HRTF filters. However, this requires that the parameters for recreating the multi-channel signal (e.g. IID, CLD parameters) are combined with the HRTF filters, which in turn requires a parameterization of the HRTF filters. This requirement for a parameterization of the HRTF filters imposes high limitation on the system, since HRTF filters can be long and thus very hard to correctly model with a parametric approach. This limitation makes it impossible to use long HRTF filters for combined parametric multi-channel and binaural downmix decoders. The crucial algorithmic component required to obtain a proper combination of multi-channel parameters and HRTF filters is to have access to a representation of the given HRTF filters in the subband domain assumed by the spatial parameters. This is exactly what is offered by the embodiments of the present invention. Once this representation is available, the HRTF filters can be combined into <NUM>N filters as a function of the parametric multi-channel representation. This gives a significant advantage in terms of computational complexity over the method that first recreates the M channels and then applies <NUM>M filtering operations.

An example of a different application of the method employed by embodiments of the current invention is the efficient compensation for non-perfect audio rendering devices for audio content coded in the MPEG HE-AAC format [ISO/IEC <NUM>-<NUM>:<NUM>/AMD1:<NUM>]. Such advanced filtering steps, possibly including cross talk cancellation, can be applied directly in the subband domain prior to the time domain synthesis.

Other developments in audio coding has made methods available to recreate a multi-channel representation of an audio signal based on a stereo (or mono) signal and corresponding control data. These methods differ substantially from older matrix based solution such as Dolby® Prologic, since additional control data is transmitted to control the re-creation, also referred to as up-mix, of the surround channels based on the transmitted mono or stereo channels.

Hence, such a parametric multi-channel audio decoder, e.g. MPEG Surround reconstructs N channels based on M transmitted channels, where N>M, and the additional control data. The additional control data represents a significantly lower data rate than that required for transmission of all N channels, making the coding very efficient while at the same time ensuring compatibility with both M channel devices and N channel devices.

These parametric surround coding methods usually comprise a parameterization of the surround signal based on Channel Level Difference (CLD) and Inter-channel coherence/cross-correlation (ICC). These parameters describe power ratios and correlation between channel pairs in the up-mix process. Further Channel Prediction Coefficients (CPC) are also used in prior art to predict intermediate or output channels during the up-mix procedure.

Claim 1:
Method for converting an impulse response signal indicative of the amplitude/frequency filter characteristic in the time domain into a filter definition signal, comprising:
filtering the impulse response signal indicative of the amplitude/frequency filter characteristic in the time domain to obtain a plurality of complex valued subband signals forming the filter definition signal,
wherein each complex valued subband signal of the plurality of complex valued subband signals corresponds to an impulse response for a subband filter (<NUM>) for a subband signal to be filtered by the subband filter (<NUM>),
wherein at least one of the complex valued subband signals of the plurality of complex valued subband signals comprises at least two different non-vanishing values,
wherein each complex valued subband signal of the plurality of complex valued subband signals is shorter than the impulse response signal indicative of the amplitude/frequency filter characteristic in the time domain,
wherein the impulse response signal indicative of the amplitude/frequency filter characteristic in the time domain is a time domain FIR (Finite Impulse Response) filter, and wherein the method for converting comprises performing an extension with zeroes of the time domain FIR filter,
wherein L complex valued subband signals are output as the plurality of complex valued subband signals, wherein L is equal to <NUM>.