Patent Description:
In today's speech and audio codecs it is state of the art to extract the spectral envelope of the speech or audio signal by Linear Prediction and further quantize and code a transformation of the Linear Prediction coefficients (LPC). Such transformations are e.g. the Line Spectral Frequencies (LSF) or Immittance Spectral Frequencies (ISF).

Vector Quantization (VQ) is usually preferred over scalar quantization for LPC quantization due to the increase of performance. However it was observed that an optimal LPC coding shows different scalar sensitivity for each frequency of the vector of LSFs or ISFs. As a direct consequence, using a classical Euclidean distance as metric in the quantization step will lead to a suboptimal system. It can be explained by the fact that the performance of a LPC quantization is usually measured by distance like Logarithmic Spectral Distance (LSD) or Weighted Logarithmic Spectral Distance (WLSD) which don't have a direct proportional relation with the Euclidean distance.

LSD is defined as the logarithm of the Euclidean distance of the spectral envelopes of original LPC coefficients and the quantized version of them. WLSD is a weighted version which takes into account that the low frequencies are perceptually more relevant than the high frequencies.

Both LSD and WLSD are too complex to be computed within a LPC quantization scheme.

Therefore most LPC coding schemes are using either the simple Euclidean distance or a weighted version of it (WED) defined as: <MAT> where lsfi is the parameter to be quantized and qlsfi is the quantized parameter, w are weights giving more distortion to certain coefficients and less to other.

Laroia et al. [<NUM>] presented a heuristic approach known as inverse harmonic mean to compute weights that give more importance to LSFs closed to formant regions. If two LSF parameters are close together the signal spectrum is expected to comprise a peak near that frequency. Hence an LSF that is close to one of its neighbors has a high scalar sensitivity and should be given a higher weight: <MAT>.

The first and the last weighting coefficients are calculated with this pseudo LSFs:
lsf<NUM> = <NUM> and lsfp+<NUM> = π, where p is the order of the LP model. The order is usually <NUM> for speech signal sampled at <NUM> and <NUM> for speech signal sampled at <NUM>.

Gardner and Rao [<NUM>] derived the individual scalar sensitivity for LSFs from a high-rate approximation (e.g. when using a VQ with <NUM> or more bits). In such a case the derived weights are optimal and minimize the LSD. The scalar weights form the diagonal of a so-called sensitivity matrix given by: <MAT>.

Where RA is the autocorrelation matrix of the impulse response of the synthesis filter <NUM>/A(z) derived from the original predictive coefficients of the LPC analysis. Jω(ω) is a Jacobian matrix transforming LSFs to LPC coefficients.

The main drawback of this solution is the computational complexity for computing the sensitivity matrix.

The ITU recommendation G. <NUM> [<NUM>] expands Gardner's approach by adding some psychoacoustic considerations. Instead of considering the matrix RA, it considers the impulse response of a perceptual weighted synthesis filter W(z): <MAT>.

Where WB(z) is an IIR filter approximating the Bark weighting filter given more importance to the low frequencies. The sensitivity matrix is then computed by replacing <NUM>/A(z) with W(z).

Although the weighting used in G. <NUM> is theoretically a near-optimal approach, it inherits from Gardner's approach a very high complexity. Today's audio codecs are standardized with a limitation in complexity and therefore the tradeoff of complexity and gain in perceptual quality is not satisfying with this approach.

The approach presented by Laroia et al. may yield suboptimal weights but it is of low complexity. The weights generated with this approach treat the whole frequency range equally although the human's ear sensitivity is highly nonlinear. Distortion in lower frequencies is much more audible than distortion in higher frequencies.

In "<NPL>) speech coders operating at low bit rates are disclosed. LPC coefficients are derived from the input signal through linear prediction analysis.

In "<NPL>) a method is disclosed in which the line spectral frequencies extracted from successive analysis are interlaced with each other.

<CIT> discloses a method and an apparatus for determining a weighting function for quantizing a linear predictive coding (LPC) coefficient. The weighting function determination apparatus may convert an LPC coefficient of a mid-subframe of an input signal to one of a immitance spectral frequency (ISF) coefficient and a line spectral frequency (LSF) coefficient, and may determine a weighting function associated with an importance of the ISF coefficient or the LSF coefficient based on the converted ISF coefficient or LSF coefficient.

<NPL>, discloses LPC analysis and converting them to LSF. LSFs are vector quantized by weighting LSF coefficients of different order. The weights are determined as a combination of Inverse Harmonic Mean of neighbouring LSFs and correction factors depending on LSF order.

Thus, there is a need for improving encoding schemes.

An object of the present invention is to provide encoding schemes that allow for computational complexity of the algorithms and/or for an increased precision thereof while maintaining a good audio quality when decoding the encoded audio signal.

This object is achieved by an encoder according to claim <NUM>, an audio transmission system according to claim <NUM>, a method according to claim <NUM> and a computer program according to claim <NUM>.

The inventors have found out that by determining spectral weighting factors using a method comprising a low computational complexity and by at least partially correcting the obtained spectral weighting factors using precalculated correction information, the obtained corrected spectral weighting factors may allow for an encoding and decoding of the audio signal with a low computational effort while maintaining encoding precision and/or reduce reduced Line Spectral Distances (LSD).

According to an embodiment of the present invention, an encoder for encoding an audio signal comprises an analyzer for analyzing the audio signal and for determining analysis prediction coefficients from the audio signal. The encoder further comprises a converter configured for deriving converted prediction coefficients from the analysis prediction coefficients and a memory configured for storing a multitude of correction values. The encoder further comprises a calculator and a bitstream former. The calculator comprises a processor, a combiner and a quantizer, wherein the processor is configured for processing the converted predicted to obtain spectral weighting factors. The combiner is configured for combining the spectral weighting factors and the multitude of correction values to obtain corrected weighting factors. The quantizer is configured for quantizing the converted prediction coefficients using the corrected weighting factors to obtain a quantized representation of the converted prediction coefficients, for example, a value related to an entry of prediction coefficients in a database. The bitstream former is configured for forming an output signal based on an information related to the quantized representation of the converted prediction coefficients and based on the audio signal. An advantage of this embodiment is that the processor may obtain the spectral weighting factors by using methods and/or concepts comprising a low computational complexity. A possibly obtained error with respect to other concepts or methods may be corrected at least partially by applying the multitude of correction values. This allows for a reduced computational complexity of weight derivation when compared to a determination rule based on [<NUM>] and reduced LSDs when compared to a determination rule according to [<NUM>].

Further embodiments provide an encoder, wherein the combiner is configured for combining the spectral weighting factors, the multitude of correction values and a further information related to the input signal to obtain the corrected weighting factors. By using the further information related to the input signal a further enhancement of the obtained corrected weighting factors may be achieved while maintaining a low computational complexity, in particular when the further information related to the input signal is at least partially obtained during other encoding steps, such that the further information may be recycled.

Further embodiments provide an encoder, wherein the combiner is configured for cyclically, in every cycle, obtaining the corrected weighted factors. The calculator comprises a smoother configured for weightedly combining first quantized weighting factors obtained for a previous cycle and second quantized weighting factors obtained for a cycle following the previous cycle to obtain smoothed corrected weighting factors comprising a value between values of the first and the second quantized weighting factors. This allows for a reduction or a prevention of transition distortions, especially in a case when corrected weighting factors of two consecutive cycles are determined such that they comprise a large difference when compared to each.

Further embodiments provide an audio transmission system comprising an encoder and a decoder configured for receiving the output signal of the encoder or a signal derived thereof and for decoding the received signal to provide a synthesized audio signal, wherein the output signal of the encoder is transmitted via a transmission media, such as a wired media or a wireless media. An advantage of the audio transmission system is that the decoder may decode the output signal, the audio signal respectively, based on unchanged methods.

Further embodiments provide a method for determining the correction values for a first multitude of first weighting factors. Each weighting factor is adapted for weighting a portion of an audio signal, for example represented as a line spectral frequency or an immittance spectral frequency. The first multitude of first weighting factors is determined based on a first determination rule for each audio signal. A second multitude of second weighting factors is calculated for each audio signal of the set of audio signals based on a second determination rule. Each of the second multitude of weighting factors is related to a first weighting factor, i.e. a weighting factor may be determined for a portion of the audio signal based on the first determination rule and based on the second determination rule to obtain two results that may be different. A third multitude of distance values is calculated, the distance values having a value related to a distance between a first weighting factor and a second weighting factor, both related to the portion of the audio signal. A fourth multitude of correction values is calculated adapted to reduce the distance values when combined with the first weighting factors such that when the first weighting factors are combined with the fourth multitude of correction values a distance between the corrected first weighting factors is reduced when compared to the second weighting factors. This allows for computing the weighting factors based on a training data set one time based on the second determination rule comprising a high computational complexity and/or a high precision and another time based on the first determination rule which may comprise a lower computational complexity and may be a lower precision, wherein the lower precision and/or compensated or reduced at least partially by correction.

Further embodiments provide a method in which the distance is reduced by adapting a polynomial, wherein polynomial coefficients relate to the correction values. Further embodiments provide a computer program.

Preferred embodiments of the present invention will be described in detail making reference to the accompanying figures in which:.

In the following description, a plurality of details is set forth to provide a more thorough explanation of embodiments of the present invention. However, it will be apparent to those skilled in the art that embodiments of the present invention may be practiced without these specific details. In other instances, well known structures and devices are shown in block diagram form rather than in detail in order to avoid obscuring embodiments of the present invention. In addition, features of the different embodiments described hereinafter may be combined with each other, unless specifically noted otherwise.

<FIG> shows a schematic block diagram of an encoder <NUM> for encoding an audio signal. The audio signal may be obtained by the encoder <NUM> as a sequence of frames <NUM> of the audio signal. The encoder <NUM> comprises an analyzer for analyzing the frame <NUM> and for determining analysis prediction coefficients <NUM> from the audio signal <NUM>. The analysis prediction coefficients (prediction coefficients) <NUM> may be obtained, for example, as linear prediction coefficients (LPC). Alternatively, also non-linear prediction coefficients may be obtained, wherein linear prediction coefficients may be obtained by utilizing less computational power and therefore may be obtained faster.

The encoder <NUM> comprises a converter <NUM> configured for deriving converted prediction coefficients <NUM> from the prediction coefficients <NUM>. The converter <NUM> may be configured for determining the converted prediction coefficients <NUM> to obtain, for example, Line Spectral Frequencies (LSF) and/or Immittance Spectral Frequencies (ISF). The converted prediction coefficients <NUM> may comprise a higher robustness with respect to quantization errors in a later quantization when compared to the prediction coefficients <NUM>. As quantization is usually performed non-linearly, quantizing linear prediction coefficients may lead to distortions of a decoded audio signal.

The encoder <NUM> comprises a calculator <NUM>. The calculator <NUM> comprises a processor <NUM> which is configured to process the converted prediction coefficients <NUM> to obtain spectral weighting factors <NUM>. The processor may be configured to calculate and/or to determine the weighting factors <NUM> based on one or more of a plurality of known determination rules such as an inverse harmonic mean (IHM) as it is known from [<NUM>] or according to a more complex approach as it is described in [<NUM>]. The International Telecommunication Union (ITU) Standard G. <NUM> describes a further approach of determining weighting factors by expanding the approach of [<NUM>] as it is described in [<NUM>]. Preferably the processor <NUM> is configured to determine the weighting factors <NUM> based on a determination rule comprising a low computational complexity. This may allow for a high throughput of encoded audio signals and/or a simple realization of the encoder <NUM> due to hardware that may consume less energy based on less computational efforts.

The calculator <NUM> comprises a combiner <NUM> configured for combining the spectral weighting factors <NUM> and a multitude of correction values <NUM> to obtain corrected weighting factors <NUM>. The multitude of correction values is provided from a memory <NUM> in which the correction values <NUM> are stored. The correction values <NUM> may be static or dynamic, i.e. the correction values <NUM> may be updated during operation of the encoder <NUM> or may remain unchanged during operation and/or may be only updated during a calibration procedure for calibrating the encoder <NUM>. Preferably, the memory <NUM> comprises static correction values <NUM>. The correction values <NUM> may be obtained, for example, by a precalculation procedure as it is described later on. Alternatively, the memory <NUM> may alternatively be comprised by the calculator <NUM> as it is indicated by the dotted lines.

The calculator <NUM> comprises a quantizer <NUM> configured for quantizing the converted prediction coefficients <NUM> using the corrected weighting factors <NUM>. The quantizer <NUM> is configured to output a quantized representation <NUM> of the converted prediction coefficients <NUM>. The quantizer <NUM> may be a linear quantizer, a non-linear quantizer such as a logarithmic quantizer or a vector-like quantizer, a vector quantizer respectively. A vector-like quantizer may be configured to quantize a plurality pf portions of the corrected weighting factors <NUM> to a plurality of quantized values (portions). The quantizer <NUM> may be configured for weighting the converted prediction coefficients <NUM> with the corrected weighting factors <NUM>. The quantizer may further be configured for determining a distance of the weighted converted prediction coefficients <NUM> to entries of a database of the quantizer <NUM> and to select a code word (representation) that is related to an entry in the database wherein the entry may comprise a lowest distance to the weighted converted prediction coefficients <NUM>. Such a procedure is exemplarily described later on. The quantizer <NUM> may be a stochastic Vector Quantizer (VQ). Alternatively, the quantizer <NUM> may also be configured for applying other Vector Quantizers like Lattice VQ or any scaler quantizer. Alternatively, the quantizer <NUM> may also be configured to apply a linear or logarithmic quantization.

The quantized representation <NUM> of the converted prediction coefficients <NUM>, i.e. the code word, is provided to a bitstream former <NUM> of the encoder <NUM>. The encoder <NUM> may comprise an audio processing unit <NUM> configured for processing some or all of the audio information of the audio signal <NUM> and/or further information. Audio processing unit <NUM> is configured for providing audio data <NUM> such as a voiced signal information or an unvoiced signal information to the bitstream former <NUM>. The bitstream former <NUM> is configured for forming an output signal (bitstream) <NUM> based on the quantized representation <NUM> of the converted prediction coefficients <NUM> and based on the audio information <NUM>, which is based on the audio signal <NUM>.

An advantage of the encoder <NUM> is that the processor <NUM> may be configured to obtain, i.e. to calculate, the weighting factors <NUM> by using a determination rule that comprises a low computational complexity. The correction values <NUM> may be obtained by, when expressed in a simplified manner, comparing a set of weighting factors obtained by a (reference) determination rule with a high computational complexity but therefore comprising a high precision and/or a good audio quality and/or a low LSD with weighting factors obtained by the determination rule executed by the processor <NUM>. This may be done for a multitude of audio signals, wherein for each of the audio signals a number of weighting factors is obtained based on both determination rules. For each audio signal, the obtained results may be compared to obtain an information related to a mismatch or an error. The information related to the mismatch or the error may be summed up and/or averaged with respect to the multitude of audio signals to obtain an information related to an average error that is made by the processor <NUM> with respect to the reference determination rule when executing the determination rule with the lower computational complexity. The obtained information related to the average error and/or mismatch may be represented in the correction values <NUM> such that the weighting factors <NUM> may be combined with the correction values <NUM> by the combiner to reduce or compensate the average error. This allows for reducing or almost compensating the error of the weighting factors <NUM> when compared to the reference determination rule used offline while still allowing for a less complex determination of the weighting factors <NUM>.

<FIG> shows a schematic block diagram of a modified calculator <NUM>'. The calculator <NUM>' comprises a processor <NUM>' configured for calculating inverse harmonic mean (IHM) weights from the LSF <NUM>', which represent the converted prediction coefficients. The calculator <NUM>' comprises a combiner <NUM>' which, when compared to the combiner <NUM>, is configured for combining the IHM weights <NUM>' of the processor <NUM>', the correction values <NUM> and a further information <NUM> of the audio signal <NUM> indicated as "reflection coefficients", wherein the further information <NUM> is not limited thereto. The further information may be an interim result of other encoding steps, for example, the reflection coefficients <NUM> may be obtained by the analyzer <NUM> during determining the prediction coefficients <NUM> as it is described in <FIG>. Linear prediction coefficients may be determined by the analyzer <NUM> when executing a determination rule according to the Levinson-Durbin algorithm in which reflection algorithms are determined. An information related to the power spectrum may also be obtained during calculating the prediction coefficients <NUM>. A possible implementation of the combiner <NUM>' is described later on. Alternatively, or in addition, the further information <NUM> may be combined with the weights <NUM> or <NUM>' and the correction parameters <NUM>, for example, information related to a power spectrum of the audio signal <NUM>. The further information <NUM> allows for further reducing a difference between weights <NUM> or <NUM>' determined by the calculator <NUM> or <NUM>' and the reference weights. An increase of computational complexity may only have minor effects as the further information <NUM> may already be determined by other components such as the analyzer <NUM> during other steps of the audio encoding.

The calculator <NUM>' further comprises a smoother <NUM> configured for receiving corrected weighting factors <NUM>' from the combiner <NUM>' and an optional information <NUM> (control flag) allowing for controlling operation (ON-/OFF-state) of the smoother <NUM>. The control flag <NUM> may be obtained, for example, from the analyzer indicating that smoothing is to be performed in order to reduce harsh transitions. The smoother <NUM> is configured for combining corrected weighting factors <NUM>' and corrected weighting factors <NUM>‴ which are a delayed representation of corrected weighting factors determined for a previous frame or sub-frame of the audio signal, i.e. corrected weighting factors determined in a previous cycle in the ON-state. The smoother <NUM> may be implemented as an infinite impulse response (IIR) filter. Therefore, the calculator <NUM>' comprises a delay block <NUM> configured for receiving and delaying corrected weighting factors <NUM>" provided by the smoother <NUM> in a first cycle and to provide those weights as the corrected weighting factors <NUM>‴ in a following cycle.

The delay block <NUM> may be implemented, for example, as a delay filter or as a memory configured for storing the received corrected weighting factors <NUM>". The smoother <NUM> is configured for weightedly combining the received corrected weighting factors <NUM>' and the received corrected weighting factors <NUM>‴ from the past. For example, the (present) corrected weighting factors <NUM>' may comprise a share of <NUM>%, <NUM>%, <NUM>% or any other value in the smoothed corrected weighting factors <NUM>", wherein the (past) weighting factors <NUM>‴ may comprise a share of (<NUM>-share of corrected weighting factors <NUM>'). This allows for avoiding harsh transitions between subsequent audio frames when the audio signal, i.e. two subsequent frames thereof, result in different corrected weighting factors which would lead to distortions in a decoded audio signal. In the OFF-state, the smoother <NUM> is configured for forwarding the corrected weighting factors <NUM>'. Alternatively or in addition, smoothing may allow for an increased audio quality for audio signals comprising a high level of periodicity.

Alternatively, the smoother <NUM> may be configured to additionally combine corrected weighted factors of more previous cycles. Alternatively or in addition, the converted prediction coefficients <NUM>' may also be the Immittance Spectral Frequencies.

A weighting factor wi may be obtained, for example, based on the inverse harmonic mean (IHM). A determination rule may be based on a form: <MAT> wherein wi denotes a determined weight <NUM>' with index i, LSFi denotes a line spectral frequency with index i. The index i corresponds to a number of spectral weighting factors obtained and may be equal to a number of prediction coefficients determined by the analyzer. The number of prediction coefficients and therefore the number of converted coefficients may be, for example, <NUM>. Alternatively, the number may also be <NUM> or <NUM>. Alternatively, the number of converted coefficients may also be lower than the number of prediction coefficients, for example, if the converted coefficients <NUM> are determined as Immittance Spectral Frequencies which may comprise a lower number when compared to the number of prediction coefficients.

In other words, <FIG> details the processing done in the weight's derivation step executed by the converter <NUM>. First the IHM weights are computed from the LSFs. According to one embodiment, an LPC order of <NUM> is used for a signal sampled at <NUM>. That means that the LSFs are bounded between <NUM> and <NUM>. According to a further embodiment, the LPC is of order <NUM> and the signal is sampled at <NUM>. In that case, the LSFs are bounded between <NUM> and <NUM>. According to a further embodiment, the signal is sampled at <NUM>, which may be called a narrow band sampling. The IHM weights may then be combined with further information, e.g. related to some of the reflection coefficients, within a polynomial for which the coefficients are optimized offline during a training phase. Finally, the obtained weights can be smoothed by the previous set of weights in certain cases, for example for stationary signals. According to an embodiment, the smoothing is never performed. According to other embodiments, it is performed only when the input frame is classified as being voiced, i.e. signal detected as being highly periodic.

In the following, reference will be made to details of correcting the derived weighting factors. For example, the analyzer is configured to determine linear prediction coefficients (LPC) of order <NUM> or <NUM>, i.e. a number of <NUM> or <NUM> LPC. Although the analyzer may also be configured to determine any other number of linear prediction coefficients or a different type of coefficient, the following description is made with reference to <NUM> coefficients, as this number of coefficients is used in mobile communication.

<FIG> shows a schematic block diagram of an encoder <NUM> additionally comprising a spectral analyzer <NUM> and a spectral processor <NUM> comprising when compared to the encoder <NUM>. The spectral analyzer <NUM> is configured for deriving spectral parameters <NUM> from the audio signal <NUM>. The spectral parameters may be, for example, an envelope curve of a spectrum of the audio signal or of a frame thereof and/or parameters characterizing the envelope curve. Alternatively coefficients related to the power spectrum may be obtained.

The spectral processor <NUM> comprises an energy calculator 145a which is configured to compute an amount or a measure <NUM> for an energy of frequency bins of the spectrum of the audio signal <NUM> based on the spectral parameters <NUM>. The spectral processor further comprises a normalizer 145b for normalizing the converted prediction coefficients <NUM>' (LSF) to obtain normalized prediction coefficients <NUM>. The converted prediction coefficients may be normalized, for example, relatively, with respect to a maximum value of a plurality of the LSF and/or absolutely, i.e. with respect to a predetermined value such as a maximum value being expected or being representable by used computation variables.

The spectral processor <NUM> further comprises a first determiner 145c configured for determining a bin energy for each normalized prediction parameter, i.e., to relate each normalized prediction parameter <NUM> obtained from the normalizer 145b to a computed to a measure <NUM> to obtain a vector W1 containing the bin energy for each LSF. The spectral processor <NUM> further comprises a second determiner 145d configured for finding (determining) a frequency weighting for each normalized LSF to obtain a vector W2 comprising the frequency weightings. The further information <NUM> comprises the vectors W1 and W2, i.e., the vectors W1 and W2 are the feature representing the further information <NUM>.

The processor <NUM>' is configured for determining the IHM based on the converted prediction parameters <NUM>' and a power of the IHM, for example the second power, wherein alternatively or in addition also a higher power may be computed, wherein the IHM and the power(s) thereof form the weighting factors <NUM>'.

A combiner <NUM>" is configured for determining the corrected weighting factors (corrected LSF weights) <NUM>' based on the further information <NUM> and the weighting factors <NUM>'.

Alternatively, the processor <NUM>', the spectral processor <NUM> and/or the combiner may be implemented as a single processing unit such as a Central processing unit, a (micro-) controller, a programmable gate array or the like.

In other words, a first and a second entry to the combiner are IHM and IHM<NUM>, i.e. the weighting factors <NUM>'. A third entry is for each LSF-vector element i : <MAT> wherein wfft is the combination of W1 and W2 and wherein min is the minimum of wfft.

M where M may be <NUM> when <NUM> prediction coefficients are derived from the audio signal and <MAT> wherein binEner contains the energy of each bin of the spectrum, i.e., binEner corresponds to the measure <NUM>.

The mapping <MAT> is a rough approximation of the energy of a formant in the spectral envelope. FreqWTable is a vector containing additional weights which are selected depending on the input signal being voiced or unvoiced.

Wfft is an approximation of the spectral energy close to a prediction coefficient like a LSF coefficient. In simple terms, if a prediction (LSF) coefficient comprises a value X, this means that the spectrum of the audio signal (frame) comprises an energy maximum (formant) at the Frequency X or beneath thereto. The wfft is a logarithmic expression of the energy at frequency X, i.e., it corresponds to the logarithmic energy at this location. When compared to embodiments described before as utilizing reflection coefficients as further information, alternatively or in addition a combination of wfft (W1) and FrequWTable (W2) may be used to obtain the further information <NUM>. FreqWTable describes one of a plurality of possible tables to be used. Based on a "coding mode" of the encoder <NUM>, e.g., voiced, fricative or the like, at least one of the plurality of tables may be selected. One or more of the plurality of tables may be trained (programmed and adapted) during operation of the encoder <NUM>.

A finding of using the wfft is to enhance coding of converted prediction coefficients that represent a formant. In contrast to classical noise shaping in which the noise is at frequencies comprising large amounts of (signal) energy the described approach relates to quantize the spectral envelope curve. When the power spectrum comprises a large amount of energy (a large measure) at frequencies comprising or arranged adjacent to a frequency of a converted prediction coefficient, this converted prediction coefficient (LSF) may be quantized better, i.e., with lower errors achieved by higher weightings, than other coefficients comprising a lower measure of energy.

<FIG> illustrates a vector LSF comprising <NUM> values of entries of the determined line spectral frequencies which are obtained by the converter based on the determined prediction coefficients. The processor is configured to also obtain <NUM> weights, exemplarily inverse harmonic means IHM represented in a vector IHM. The correction values <NUM> are grouped, for example, to a vector a, a vector b, and a vector c. Each of the vectors a, b and c comprises <NUM> values a<NUM>-<NUM>, b<NUM>-<NUM> and c<NUM>-<NUM>, wherein equal indices indicate that the respective correction value is related to a prediction coefficient, a converted representation thereof and a weighting factor comprising the same index. <FIG> illustrates a determination rule executed by the combiner <NUM> or <NUM>' according to an embodiment. The combiner is configured for computing or determining a result for a polynomial function based on a form y = a + bx + cx<NUM>, i.e. different correction values a, b, c are combined (multiplied) with different powers of the weighting factors (illustrated as x). y denotes a vector of obtained corrected weighting factors.

Alternatively or in addition, the combiner may also be configured to add further correction values (d, e, f,. ) and further powers of the weighting factors or of the further information. For example, the polynomial depicted in <FIG> may be extended by a vector d comprising <NUM> values being multiplied with a third power of the further information <NUM>, a respective vector also comprising <NUM> values. This may be, for example a vector based on IHM<NUM> when the processor <NUM>' as described in <FIG> is configured to determine further powers of IHM. Alternatively, only at least the vector b and optionally one or more of the higher order vectors c, d,. may be computed. Simplified the order of the polynomial increases with each term, wherein each type may be formed based on the weighting factor and/or optionally based on the further information, wherein the polynomial is based on the form y = a + bx + cx<NUM> also when comprising a term of higher order. The correction values a, b, c and optionally d, e,. may comprise values real and/or imaginary values and may also comprise a value of zero.

<FIG> depicts an exemplary determination rule for illustrating the step of the obtaining the corrected weighting factors <NUM> or <NUM>'. The corrected weighting factors are represented in a vector w comprising <NUM> values, one weighting factor for each of the converted prediction coefficients depicted in <FIG>. Each of the corrected weighting factors w<NUM>-<NUM> is computed according to the determination rule shown in <FIG>. The above descriptions shall only illustrate a principle of determining the corrected weighting factors and shall not be limited to the determination rules described above. The above described determination rules may also be varied, scaled, shifted or the like. In general, the corrected weighting factors are obtained by performing a combination of the correction values with the determined weighting factors.

<FIG> depicts an exemplary determination scheme which may be implemented by a quantizer such as the quantizer <NUM> to determine the quantized representation of the converted prediction coefficients. The quantizer may sum up an error, e.g. a difference or a power thereof between a determined converted coefficient shown as LSFi and a reference coefficient indicated as LSF'l, wherein the reference coefficients may be stored in a database of the quantizer. The determined distance may be squared such that only positive values are obtained. Each of the distances (errors) is weighted by a respective weighting factor wi. This allows for giving frequency ranges or converted prediction coefficients with a higher importance for audio quality a higher weight and frequency ranges with a lower importance for audio quality a lower weight. The errors are summed up over some or all of the indices <NUM>-<NUM> to obtain a total error value. This may be done for a plurality of predefined combinations (database entries) of coefficients that may be combined to sets Qu', Qu",. Qun as indicated in <FIG>. The quantizer may be configured for selecting a code word related to a set of the predefined coefficients comprising a minimum error with respect to the determined corrected weighted factors and the converted prediction coefficients. The code word may be, for example, an index of a table such that a decoder may restore the predefined set Qu', Qu",. based on the received index, the received code word, respectively.

To obtain the correction values during a training phase , a reference determination rule according to which reference weights are determined is selected. As the encoder is configured to correct determined weighting factors with respect to the reference weights and determination of the reference weights may be done offline, i.e. during a calibration step or the like, a determination rule comprising a high precision (e.g., low LSD) may be selected while neglecting resulting computational effort. Preferably, a method comprising a high precision and maybe a high computation complexity may be selected to obtain pre-sized reference weighting factors. For example, a method to determine weighting factors according to the G. <NUM> Standard [<NUM>] may be used.

A determination rule according to which the encoder will determine the weighting factors is also executed. This may be a method comprising a low computational complexity while accepting a lower precision of the determined results. Weights are computed according to both determination rules while using a set of audio material comprising, for example, speech and/or music. The audio material may be represented in a number of M training vectors, wherein M may comprise a value of more than <NUM>, more than <NUM> or more than <NUM>. Both sets of obtained weighting factors are stored in a matrix, each matrix comprising vectors that are each related to one of the M training vectors.

For each of the M training vectors, a distance is determined between a vector comprising the weighting factors determined based on the first (reference) determination rule and a vector comprising the weighting vectors determined based on the encoder determination rule. The distances are summed up to obtain a total distance (error), wherein the total error may be averaged to obtain an average error value.

During determination of the correction values, an objective may be to reduce the total error and/or the average error. Therefore, a polynomial fitting may be executed based on the determination rule shown in <FIG>, wherein the vectors a, b, c and/or further vectors are adapted to the polynomial such that the total and/or average error is reduced or minimized. The polynomial is fit to the weighting factors determined based on the determination rule, which will be executed at the decoder. The polynomial may be fit such that the total error or the average error is below a threshold value, for example, <NUM>, <NUM> or <NUM>, wherein <NUM> indicates a total mismatch. Alternatively or in addition, the polynomial may be fit such that the total error is minimized by utilizing based on an error minimizing algorithm. A value of <NUM> may indicate a relative error that may be expressed as a difference (distance) and/or as a quotient of distances. Alternatively, the polynomial fitting may be done by determining the correction values such that the resulting total error or average error comprises a value that is close to a mathematical minimum. This may be done, for example, by derivation of the used functions and an optimization based on setting the obtained derivation to zero.

A further reduction of the distance (error), for example the Euclidian distance, may be achieved when adding the additional information, as it is shown for <NUM> at encoder side. This additional information may also be used during calculating the correction parameters. The information may be used by combining the same with the polynomial for determining the correction value.

In other words first the IHM weights and the G. <NUM> weights may be extracted from a database containing more than <NUM> seconds (or M training vectors) of speech and music material. The IHM weights may be stored in the matrix I and the G. <NUM> weights may be stored in the matrix G. Let Ii and Gi be vectors containing all IHM and G. <NUM> weights wi of the i-th ISF or LSF coefficient of the whole training database. The average Euclidean distance between these two vectors may be determined based on: <MAT>.

In order to minimize the distance between these two vectors a second order polynomial may be fit: <MAT>.

A matrix <MAT> may be introduced and a vector Pi = [p<NUM>,i p<NUM>,i p<NUM>,i]T in order to rewrite: <MAT> and: <MAT>.

In order to get the vector Pi having the lowest average Euclidean distance the derivation <MAT> may be set to zero: <MAT> to obtain : <MAT>.

To further reduce the difference (Euclidean distance) between the proposed weights and the G. <NUM> weights reflection coefficients of other information are added to the matrix EIi. Because, for example, the reflection coefficients carry some information about the LPC model which is not directly observable in the LSF or ISF domain, they help to reduce the Euclidean distance di. In practice probably not all reflection coefficients will lead to a significant reduction in Euclidean distance. The inventors found that it may be sufficient to use the first and the 14th reflection coefficient. Adding the reflection coefficients the matrix EIi will look like: <MAT> where rx,y is the y-th reflection coefficient (or the other information) of the x-th instance in the training dataset. Accordingly the dimension of vector Pi will comprise changed dimensions according to the number of columns in matrix EIi. The calculation of the optimal vector Pi stays the same as above.

By adding further information, the determination rule depicted in <FIG> may be changed (extended) according to y = a + b x + c x<NUM> + d r<NUM><NUM> +.

<FIG> shows a schematic block diagram of an audio transmission system <NUM> according to an embodiment. The audio transmission system <NUM> comprises the encoder <NUM> and a decoder <NUM> configured to receive the output signal <NUM> as a bitstream comprising the quantized LSF, or an information related thereto, respectively. The bitstream is sent over a transmission media <NUM>, such as a wired connection (cable) or the air.

In other words, <FIG> shows an overview of the LPC coding scheme at the encoder side. It is worth mentioning that the weighting is used only by the encoder and is not needed by the decoder. First a LPC analysis is performed on the input signal. It outputs LPC coefficients and reflection coefficients (RC). After the LPC analysis the LPC predictive coefficients are converted to LSFs. These LSFs are vector quantized by using a scheme like a multi-stage vector quantization and then transmitted to the decoder. The code word is selected according to a weighted squared error distance called WED as introduced in the previous section. For this purpose associated weights have to be computed beforehand. The weights derivation is function of the original LSFs and the reflection coefficients. The reflection coefficients are directly available during the LPC analysis as intern variables needed by the Levinson-Durbin algorithm.

<FIG> illustrates an embodiment of deriving the correction values as it was described above. The converted prediction coefficients <NUM>' (LSFs) or other coefficients are used for determining weights according to the encoder in a block A and for computing corresponding weights in a block B. The obtained weights <NUM> are either directly combined with obtained reference weights <NUM>" in a block C for fitting the modeling, i.e. for computing the vector Pi as indicated by the dashed line from block A to block C. Optionally, if the further information <NUM> is such as the reflection coefficients or the spectral power information is used for determining the correction values <NUM>, the weights <NUM>' are combined with the further information <NUM> in a regression vector indicated as block D as it was described by extended EIi by the reflection values. Obtained weights <NUM>‴ are then combined with the reference weighting factors <NUM>" in the block C.

In other words, the fitting model of block C is the vector P which is described above. In the following, a pseudo-code exemplarily summarizes the weight derivation processing:
<IMG>
which indicates the smoothing described above in which present weights are weighted with a factor of <NUM> and past weights are weighted with a factor of <NUM>.

The obtained coefficients for the vector P may comprise scalar values as indicated exemplarily below for a signal sampled at <NUM> and with a LPC order of <NUM>: <MAT>.

As stated above, instead of the LSF also the ISF may be provided by the converter as converted coefficients <NUM>. A weight derivation may be very similar as indicated by the following pseudo-code. ISFs of order N are equivalent to LSFs of order N-<NUM> for the N-<NUM> first coefficients to which we append the Nth reflection coefficients. Therefore the weights derivation is very close to the LSF weights derivation. It is given by the following pseudo-code:
<IMG>
<IMG>
where fitting model coefficients for input signal with frequency components going up to <NUM>: <MAT> where fitting model coefficients for input signal with frequency components going up to <NUM> and with zero energy for frequency component going from <NUM> to <NUM>: <MAT>.

Basically, the orders of the ISF are modified which may be seen when compared the block /* compute IHN weights */ of both pseudo-codes.

<FIG> shows a schematic flowchart of a method <NUM> for encoding an audio signal. The method <NUM> comprises a step <NUM> in which the audio signal is analyzed in in which analysis prediction coefficients are determined from the audio signal. The method <NUM> further comprises a step <NUM> in which converted prediction coefficients are derived from the analysis prediction coefficients. In a step <NUM> a multitude of correction values is stored, for example in a memory such as the memory <NUM>. In a step <NUM> the converted prediction coefficients and the multitude of correction values are combined to obtain corrected weighting factors. In a step <NUM> the converted prediction coefficients are quantized using the corrected weighting factors to obtain a quantized representation of the converted prediction coefficients. In a step <NUM> an output signal is formed based on representation of the converted prediction coefficients and based on the audio signal.

In other words, the present invention proposes a new efficient way of deriving the optimal weights w by using a low complex heuristic algorithm. An optimization over the IHM weighting is presented that results in less distortion in lower frequencies while giving more distortion to higher frequencies and yielding a less audible the overall distortion. Such an optimization is achieved by computing first the weights as proposed in [<NUM>] and then by modifying them in a way to make them very close to the weights which would have been obtained by using the G. <NUM>'s approach [<NUM>]. The second stage consist of a simple second order polynomial model during a training phase by minimizing the average Euclidian distance between the modified IHM weights and the G. <NUM>'s weights. Simplified, the relationship between IHM and G. <NUM> weights is modeled by a (probably simple) polynomial function.

The inventive encoded audio signal can be stored on a digital storage medium or can be transmitted on a transmission medium such as a wireless transmission medium or a wired transmission medium such as the Internet.

Claim 1:
Encoder (<NUM>) for encoding an audio signal (<NUM>), the encoder (<NUM>) comprising:
an analyzer (<NUM>) configured for analyzing the audio signal (<NUM>) and for determining analysis prediction coefficients (<NUM>) from the audio signal (<NUM>);
a converter (<NUM>) configured for deriving converted prediction coefficients (<NUM>; <NUM>') from the analysis prediction coefficients (<NUM>);
a memory (<NUM>) configured for storing a multitude of correction values (<NUM>);
a calculator (<NUM>; <NUM>') comprising:
a processor (<NUM>; <NUM>') configured for processing the converted prediction coefficients (<NUM>; <NUM>') to obtain spectral weighting factors (<NUM>; <NUM>');
a combiner (<NUM>; <NUM>') configured for combining the spectral weighting factors (<NUM>; <NUM>') and the multitude of correction values (<NUM>; a, b, c) to obtain corrected weighting factors (<NUM>; <NUM>'); and
a quantizer (<NUM>) configured for quantizing the converted prediction coefficients (<NUM>; <NUM>') using the corrected weighting factors (<NUM>; <NUM>') to obtain a quantized representation (<NUM>) of the converted prediction coefficients (<NUM>; <NUM>'); and
a bitstream former (<NUM>) configured for forming an output signal (<NUM>) based on the quantized representation (<NUM>) of the converted prediction coefficients (<NUM>) and based on the audio signal (<NUM>);
wherein the combiner (<NUM>') is configured for combining the spectral weighting factors (<NUM>; <NUM>'), the multitude of correction values (<NUM>; a, b, c) and a further information (<NUM>) related to the input signal (<NUM>) to obtain the corrected weighting factors (<NUM>');
wherein the further information (<NUM>) related to the input signal (<NUM>) comprises reflection coefficients obtained by the analyzer (<NUM>).