KR100892152B1 - Device and method for encoding a time-discrete audio signal and device and method for decoding coded audio data - Google Patents

Abstract

According to the invention, a time-discrete audio signal is processed (52) in order to provide a quantization block with quantized spectral values (52). In addition, a whole-number spectral representation is generated from a time-discrete audio signal, using a whole-number transformation algorithm (56). The quantization block, which has been generated using a psychoacoustic model (54), is inverse quantized and rounded (58) to form a differential between the whole-number spectral values and the inverse quantized rounded spectral values. The quantization block alone produces a psychoacoustic encoded/decoded audio signal affected by loss after the decoding process, whereas the quantization block together with the combination block provides a loss-free, or practically loss-free encoded and decoded audio signal during said decoding process. The generation of the differential signal in the frequency range allows a simpler encoder/decoder structure to be produced.

Description

Device and method for encoding a time-discrete audio signal and device and method for decoding coded audio data             

The present invention relates to audio encoding / decoding, and more particularly, to a scalable coding / decoding algorithm having a psychoacoustic first scaling layer and a second scaling layer for lossless decoding. decoding algorithms).

Modern audio encoding methods, such as MPEG Layer 3 (MP3) or MPEG AAC, employ a so-called modified discrete cosine transform (MDCT) to obtain a block-wise frequency representation of the audio signal. use. Such an audio coder generally obtains one stream of time-discrete audio samples. One stream of audio samples is windowed to obtain, for example, one windowing block of 1,024 or 2,048 windowed audio samples. For the windowing, various window functions such as a sine window are used.

Windowing time-discrete audio samples are then converted into a spectral representation by one filter bank. In principle, a Fourier transform or a Fourier transform due to special reasons such as FFT or MDCT described above can be used for this. The block of audio spectral values at the output of the filter bank can be further processed as needed. In the audio encoders, quantization of the audio spectral values is followed, whereby the quantization noise caused by the quantization is below the psychoacoustic masking threshold, i.e. masked away. It is common for the quantization step to be chosen. Quantization is lossy coding. In order to further reduce the amount of data, the quantized spectral values are then entropy coded, for example with Huffman coding. By adding side information such as scale factor, one bit stream to be stored or transmitted is obtained from a bitstream multiplexer from an entropy-coded quantized spectral value. Is made by).

In an audio decoder, a bit stream is divided into coded quantized spectral values and auxiliary information by a bit stream de-multiplexer. The entropy-encoded quantization spectral value is then inverse quantized to obtain a decoded spectral value consisting of quantization noise that is inaudible below the psychoacoustic shielding threshold. These spectral values are then transformed into a temporal representation by the synthesis filter bank to obtain a time-discrete decoded audio sample. In the synthesis filter bank, one transform algorithm must be used that is opposite to the transform algorithm. In addition, windowing must be canceled after an inverse or backward transform.

In order to obtain good frequency selectivity, it is common for modern audio encoders to use block overlap. Such a case is shown in FIG. 4A. First, for example 2,048 time-discrete audio samples are taken and windowed by means 402. Window embodying means 402 has a window length of 2N samples and provides a block of 2N window samples to the output side. A second block of 2N windowing samples is created to obtain window overlap by means 404 shown in FIG. 4A as separate from the means 402 for only clarity reasons. However, the 2,048 samples supplied to the means 404 are not time-discrete audio samples that occur immediately following the first window, but comprise the second half of windowed samples by means 402 and 1,024. It only additionally includes "new" samples. Window overlap is shown symbolically by means 406 of FIG. 4A resulting in a 50% overlapping degree. Both 2N windowing samples output output by means 402 and 2N windowing sample output by means 404 are then processed in the MDCT algorithm by means 408 and 410 respectively. The means 408 provides N spectral values for the first window according to a known MDCT algorithm, while the means 410 provides N spectral values for a second window, wherein the first window and the second There is 50% overlap between the windows.

In the decoder, the N spectral value of the first window shown in FIG. 4B is supplied to means 412 for performing an inverse modified discrete cosine transform. The N spectral value of the second window is also the same. These are supplied to means 414 for also performing inversely alternating discrete cosine transform. Both means 412 and 414 provide 2N samples for the first window and 2N samples for the second window, respectively.

In the means 416 indicated by time domain aliasing cancellation (TDAC) in FIG. 4B, the fact that two windows overlap is considered. In particular, the sample y1 of the second half of the first window, i.e., the sample with index N + k, is summed up with the sample y2, i.e., the sample with index k, from the first half of the second window. , N decoding time samples are generated at the output side, i.e. in the decoder.

By means of the function of the means 416, also called an add function, the windowing executed in the encoder shown schematically in FIG. 4A is automatically considered for some time, which is apparent in the decoder shown in FIG. explicit) "inverse windowing" shall not occur.

When the window function executed by the means 402 or 404 is represented by [w (k)], the index k represents time and the squared window weight added to the squared window weight [w (N + k)] It must meet the condition that [w (k)] becomes 1 together, where k is 0 to N-1. When the sine window whose window weight follows the first half-wave of the sine function is used, since the sum of the square of the sine of each angle and the square of the cosine is 1 This condition is always met.

Since the 90 degree angle is another matter, and the sine of one angle between 0 and 180 degrees does not become an integer, the window method with the ensuing MDCT function described in FIG. 4A is floating. Because it is obtained with floating-point numbers, windowing by multiplication of time-discrete samples is disadvantageous when considered as one sine window. Floating-point numbers come after windowing even when integer time-discrete samples are windowed.

Thus, even when the psychoacoustic encoder is not used, i.e. when lossless coding is to be achieved, the quantization at the output of the means 408 or 410 is capable of performing reasonably manageable entropy coding. need.

When attempting to use the known transformation described in accordance with FIG. 4A for lossless audio coding, very fine quantization must be used so that errors resulting from rounding of floating point numbers can be ignored, or an error signal can be used, for example. It must be additionally coded in the time domain.

The concepts described earlier, ie the quantization is so precisely adjusted that the error resulting from the rounding of floating point numbers is negligible are disclosed, for example, in DE 197 42 201 C1. Here, the audio signal is converted and quantized into its spectral representation to obtain quantized spectral values. The quantized spectral values are then dequantized, converted into the time domain, and then compared with the original audio signal. If the error, ie the error between the original audio signal and the quantized / dequantized audio signal is above the error threshold, the quantizer is more precisely adjusted in the feedback and compared again. If the error threshold is below the threshold, the repetition of the above process is terminated. The residual signal, which may still be present, is encoded by the time domain coder, so that the time-domain-encoded residual signal is present at the time of repetition cancellation, apart from the time-domain coder, and also contains a quantized coded spectral value according to quantizer adjustment Write. The quantizer is not controlled from the psychoacoustic model, so it is common for the coded spectral values to be quantized more accurately than with the psychoacoustic model.

tea. (Proc. ICASSP, 2000) publication "A Design of Lossy and Lossless Scalable Audio Coding" by T. Moriya et al. A scalable encoder is described which includes as a lossy data compression module, which has a block-type digital signal form as an input signal and generates a compressed bit stream. In the local decoder, which is also also present, the encoding is canceled again and one encoded / decoded signal is generated. This signal is compared with the original input signal by subtracting the encoded / decoded signal from the original input signal. The error signal is then fed to a second module where lossless bit conversion is used. This conversion takes two steps. The first step consists of switching from a two's complement format to a presign-magnitude format. The second step consists of the conversion from a vertical magnitude sequence to a horizontal bit sequence in one processing block. This lossless data conversion is performed to maximize the number of zeros or to maximize the number of consecutive zeros in one sequence in order to obtain the best possible compression of the time error signal present due to the digital number. This principle is based on the bit slice arithmetic coding (BSAC) scheme described in the publication "Multi-Layer Bit Sliced Rate Scalable Audio Coder" (103rd AES Convention, Preprint No. 4520). Is based on.

A disadvantage of the above concept is that data for a lossless expansion layer, i.e. auxiliary data necessary to achieve lossless decoding of the audio signal, must be obtained in the time domain. This means that complete decoding, including frequency / time conversion, is necessary to obtain the encoded / decoded signal in the time domain, so that samples between the original audio input signal and the encoded / decoded audio signal are lost due to psychoacoustic encoding. By sample-wise difference formation, the error signal is calculated. The idea is that in encoders that generate audio data streams, a filter bank or both complete time / frequency conversion means such as, for example, an MDCT algorithm, is required for forward transform. At the same time, it is particularly disadvantageous in that a complete inverse filter bank or a full synthesis algorithm is needed only for the generation of an error signal. Thus, in addition to its inherent encoder functionalities, it must also include complete decoder functionality. If the encoder is implemented in software, then both storage capacity and processor capacity are required, leading to an increase in the cost of executing the encoder.

It is an object of the present invention to provide a less expensive concept that can produce an audio data stream that can be decoded at least in a nearly lossless manner.

This object is claimed by the apparatus for encoding the time-discrete audio signal of claim 1, by the method for encoding the time-discrete audio signal of claim 21, by the apparatus for decoding the encoded audio data signal of claim 22, By the method of decoding the encoded audio data of 31 or by the computer program of claim 32 or 33.

The present invention provides one block of quantized spectral values, typically ancillary audio data that enables lossless decoding of the audio signal, which is then lost due to quantization by a psychoacoustic model. Is based on the finding that it can be obtained by inverse quantization in order to have an inverse quantized spectral value. This inverse quantization spectral value is then rounded to obtain a rounding block of rounded inverse quantization spectral values. According to the present invention, as a reference for difference formation, an integer conversion algorithm for generating one integer block of spectral values consisting only of integer spectral values from one block of integer time-discrete samples is provided. Used. According to the invention, the combination of the spectral values of the rounding block and the integer block is spectral value-wise so that no synthesis algorithm, i.e. an inverse filter bank or an inverse MDCT algorithm, is needed in the encoder itself. , In the frequency domain. The combination block, which consists of differential spectral values, contains only integer values that can be entropy coded in a known manner, due to the integer transform algorithm and the rounded quantization values. It can be seen that arbitrary entropy coders can be used for entropy encoding of a combined block, for example, Huffman coders or arithmetic coders.

Arbitrary encoders can also be used for encoding the quantized spectral values of the quantization block, known tools commonly used in modern audio encoders.

It can be seen that the coding / decoding concept of the present invention is compatible with modern coding tools such as window switching, TNS or center / side coding of multi-channel audio signals. .

In a preferred embodiment of the present invention, MDCT is used to provide one quantization block of spectral values using a psychoacoustic model. In addition, it is preferred to use so-called IntMDCT as the integer conversion algorithm.

In another embodiment of the present invention, provision of a quantization block can be done without conventional MDCT, and IntMDCT can be used as an approximation to MDCT, i.e. by an integer transform algorithm to obtain IntMDCT spectral values. The resulting integer spectrum is fed to a psychoacoustic quantizer, and the quantized IntMDCT is then inverse quantized and quantized again to be compared with the original integer spectral values. In this case only a single transform is needed, ie IntMDCT generates integer spectral values from integer time-discrete samples.

In general, a processor may work as integers, or each floating point number may be represented by one integer.

If integer arithmetic is used in the processor, it can be executed without rounding the dequantized spectral value, which, due to the processor's operation, causes the rounding value to fall within the accuracy of the least significant bit (LSB). Because it exists anyway. In this case, complete lossless processing, ie processing within the accuracy of the processor system used is achieved. Alternatively, however, rounding to a lower accuracy may be performed in which the differential signal of the combined block is rounded to an accuracy that is fixed by a rounding function. Introducing rounding beyond the inherent rounding of the processor system is a lossless "degree" of encryption in order to generate an almost lossless encoder in the sense of data compression. It allows for flexibility as long as it affects.

The decoder of the present invention is characterized by two psychoacoustically encoded audio data and auxiliary audio data which are extracted from the audio data and possibly subjected to entropy encoding, and then processed as described below. First, the quantization block of the decoder is dequantized to be added to the entropy-decoded auxiliary audio data and rounded using the same rounding function also used at the encoder. Next, the decoder has a psychoacoustic compressed spectral representation of the audio signal and a lossless representation of the audio signal, where the psychoacoustic compressed spectral representation of the audio signal is lossy encoded / decoded audio. The signal is transformed into the time domain to obtain a signal, while the lossless representation uses one integer conversion algorithm as opposed to the integer conversion algorithm to obtain an audio signal that is encoded or decoded losslessly or almost losslessly as described above. Is converted to the time domain.

These and other objects and features of the present invention will become apparent from the following description with reference to the accompanying drawings, in which:

1 is a block circuit diagram of a preferred means for processing time-discrete audio samples to obtain an integer value from which an integer spectral value can be ascertained;

2 is a schematic of split-up of MDCT and inverse MDCT in Givens rotations and two DCT-IV operations;

3 is a diagram illustrating the division of 50% overlapped MDCT in the rotation and DCT-IV operations;

4A is a block circuit schematic of a known encoder having 50% overlap with MDCT;

4B is a block circuit schematic diagram of a known decoder for decoding the value generated by FIG. 4A;

5 is a principle block circuit diagram of a preferred encoder of the present invention;

6 is a principle block circuit diagram of another preferred encoder of the present invention;

7 is a principle block circuit diagram of a preferred decoder of the present invention;

8A is a schematic diagram of one bit stream having a first scaling layer and a second scaling layer;

8B is a schematic diagram of one bit stream having a first scaling layer and several additional scaling layers;

9 is a schematic diagram of binary coded differential spectral values for explaining possible scaling in relation to the accuracy (bits) of the differential spectral values and / or the frequency (sample rate) of the differential spectral values.

In the following, the encoder circuit (FIGS. 5 and 7) of the present invention or the preferred decoder circuit (FIG. 7) of the present invention will be described according to Figs. The encoder of the present invention shown in FIG. 5 includes one output unit 52 to which encoded audio data is output, and one input unit 50 to which a time-discrete audio signal is supplied. The time-discrete audio signal supplied to the input unit 50 provides a quantization block of the time-discrete audio signal on the output side and uses the psychoacoustic model 54 to quantize the spectralized audio signal 50. Supplied to the means 52 for providing a quantization block comprising a value. The encoder of the present invention is operative to generate integer spectral values from integer time-discrete samples, and further includes means for generating one integer block using an integer transform algorithm 56.

The encoder of the present invention further includes means 58 for inverse quantization of the quantization block output from the quantization block providing means 52 and a rounding function when further accuracy is required besides the processor accuracy. As mentioned above, if it must reach up to the accuracy of the processor system, the rounding function is already inherent in the dequantization of the quantization block, since a processor with integer arithmetic cannot somehow provide a non-integer value. Included. Thus, the means 58 provide a so-called rounding block comprising dequantized spectral values, which are inherently or explicitly rounded integers. Both the rounding block and the integer block are supplied to combining means that provide differential spectral values to the differential block using difference formation, where the term "differential block" is used to determine that the differential spectral values It means that the value includes the difference between the rounding blocks.

Both the quantization block output from the means 52 and the difference block output from the difference forming means perform, for example, the normal processing of the quantization block and cause, for example, entropy encoding of the difference block. Supplied by. The processing means 60 outputs the encoded audio data including both the information of the quantization block and the information of the difference block from the output unit 52.

In the first preferred embodiment shown in Fig. 6, the time-discrete audio signal is converted into its spectral representation by MDCT and then quantized. Thus, the means 52 for providing the quantization block consist of MDCT means 52a and one quantizer 52b.

It is also preferable to generate an integer block with one IntMDCT 56, which is an integer conversion algorithm.

In FIG. 6, the processing means 60 shown in FIG. 5 uses the bits for bitstream encoding the quantization block outputted by the means 52b as well as by the entropy encoder 60b for entropy encoding the differential block. Also shown by the stream encoding means 60a. Entropy encoder 60b outputs one entropy-coded difference block, while bit stream encoder 60a outputs psychoacoustic coded audio data. The two output data of blocks 60a and 60b are in an appropriate manner with one bit stream having psychoacoustic coded audio data in the first scaling layer and additional audio data for lossless decoding in the second scaling layer. Can be combined. The scaled bit stream then corresponds to the coded audio data shown in FIG. 5 at the output 52 of the encoder.

In another preferred embodiment, as indicated by the dashed arrow 62 in FIG. 5, it may be executed without the MDCT block 52a of FIG. 6. In this case, the integer spectrum provided by the integer converting means 56 is supplied to both the difference forming means 59 and the quantizer 52b in FIG. The spectral values generated by the integer conversion are used here to some extent as an approximation to conventional MDCT spectra. This embodiment has the advantage that only the IntMDCT algorithm is present in the encoder and not both the IntMDCT algorithm and the MDCT algorithm must exist in the encoder.

Referring back to FIG. 6, dashed blocks and lines illustrate the extension of such a conventional MPEG encoder, while solid blocks and lines are one of the MPEG standards. It can be seen that the conventional audio encoder according to the description. Thus, a fundamental change of the conventional MPEG encoder is not necessary, but an inventive capture of auxiliary audio data for lossless encoding by integer conversion can be added without changing the basic structure of the encoder / decoder. It can be seen.

7 shows a principle block circuit diagram of a decoder of the present invention for decoding the encoded audio data output from the output unit 52 of FIG. It is first divided into psychoacoustic encoded audio data on the one hand and auxiliary audio data on the other. While the auxiliary audio data is entropy coded by the entropy encoder 72 when it is entropy coded in the encoder, psychoacoustic coded audio data is supplied to one conventional bit stream decoder 70. The quantized spectral values present at the output of the bit stream decoder 70 of FIG. 7 are fed to an inverse quantizer 74, which can be constructed in principle identically to the inverse quantizer of the means of FIG. For the purpose of accuracy that does not match the processor accuracy, the decoder is also provided with a rounding means 76 that executes the same algorithm or the same rounding function for mapping real numbers to integers, and the means 58 of FIG. Can also be run from In the decoder-side combiner 78, rounded inversely quantized spectral values are further coupled to entropy coded auxiliary audio data with respect to the spectral value-wise. In the decoder, the dequantized spectral values on the one hand are present at the output of the means 74 and on the other hand the integer spectral values are at the output of the combiner 78.

The output spectral values of the means 74 are then passed to the means 80 for executing an inverse modified discrete cosine transform to obtain a lossy psychoacoustically encoded and decoded audio signal. By the time domain. The output signal of the combiner 78 is also an inverse integer MDCT to obtain an almost lossless encoded and decoded audio signal when a lossless encoded / decoded audio signal, or a corresponding rougher rounding is used. The means 82 for executing (IntMDCT) is converted to the time display.

Next, the entropy encoder 60b of FIG. 6 of a particularly preferred embodiment will be described. Since there are various code tables selected according to average statistics of quantization spectral values in a typical modern MPEG encoder, the same code table or codebook for entropy encoding of differential blocks at the output of the combiner 59 Preference is given to using. Since the size of the differential block, ie, the residual IntMDCT spectrum, depends on the accuracy of the quantization, one codebook selection of entropy encoder 60b can be performed without ancillary side information.

In an MPEG-2 AAC encoder, the spectral coefficients, or quantized spectral values, are grouped into scale factor bands in the quantization block, where the spectral values are associated with one scale factor band. It is compared with a gain factor derived from one corresponding scale factor involved. In this known encoder concept a non-uniform quantizer is used to quantize the weighted spectral values, so that residual values, ie combiners 59 The magnitude of the spectral values at the output of is dependent not only on the scale factor but also on their own quantized values. However, since both the scale factor and the quantization spectrum value are included in the bit stream generated by the means 60a of FIG. 6, that is, psychoacoustic encoded audio data, the codebook selection is performed in the encoder according to the magnitude of the differential spectrum value. In addition, it is desirable to ascertain the code table used for the encoder in the decoder based on two scale factors and quantization values transmitted in the bit stream. Since the auxiliary information at the output of the combiner 59 does not have to be transmitted for entropy encoding of differential spectral values, entropy encoding sets the signaling bits as auxiliary information for the entropy encoder 60b. It does not expand in the data stream, but only leads to data rate compression.

In audio encoders according to the standard MPEG-2 AAC, window switching is used to avoid pre-echoes in transient audio signal areas. This technique is based on the possibility of individually selecting window shapes in each half of the MDCT window, and makes it possible to change the block size in successive blocks. Similarly, the IntMDCT type integer conversion algorithm described with reference to FIGS. 1-3 also uses different window shapes in windowing and in the time domain aliasing section of the MDCT split-up. To be executed. Thus, it is desirable to use the same window decisions for both the integer transform algorithm and the transform algorithm for generating the quantization block.

In the encoder according to MPEG-2 AAC, there are also some additional coding tools to which temporal noise shaping (TNS) and center / side (CS) stereo coding are mentioned. In TNS encoding, such as CS coding, the conversion of spectral values is performed prior to quantization. Thus, the difference between IntMDCT values, that is, the difference between an integer block and a quantized MDCT value, increases. According to the present invention, an integer conversion algorithm is made to allow both TNS coding and center / side coding of integer spectral values. TNS techniques are based on adaptive forward prediction of MDCT values over the frequency. The same prediction filter calculated by a conventional TNS module in a signal-adaptive manner is also preferably used to predict integer spectral values, where a non-integer value is thereby used. If generated, downstream rounding may be used to regenerate the integer value. This rounding preferably occurs after each expected step. In the decoder, the original spectrum can be reconstructed again using the same rounding function as the inverse filter. Similarly, CS coding can also be applied to IntMDCT spectral values by applying rounded Givens rotations of angle p / 4 based on a lifting scheme. Thereby, the original IntMDCT values of the decoder can be reconstructed.

It can be seen that the inventive concept in the preferred embodiment with IntMDCT as an integer conversion algorithm can be applied to all MDCT-based hearing-adapted audio coders. Such encoders are, by way of example only, MPEG-4 AAC Scalable, MPEG-4 AAC Low Delay, MPEG-4 BSAC, MPEG-4 Twin VQ, Dolby AC -Encoders according to -3 and the like.                 

In particular, it can be seen that the inventive concepts are backwards compatible. The listening-adapted coder or decoder does not change but only expands. Ancillary information for lossless components may be transmitted in a bit stream encoded in a listen-adaptive manner in a backwards compatible manner, such as MPEG-2 AAC in the "Auxiliary Data" field. The addition to the previous listen-adaptive decoder drawn by the dashed line in FIG. 7 can evaluate this auxiliary data and restore the IntMDCT spectrum along with the quantized MDCT spectrum from the listen-adaptive decoder in a lossless manner.

The concept of psychoacoustic coding complemented by lossless or near lossless coding of the present invention is particularly suitable for the generation, transmission and decoding of scalable data streams. It is well known that a scalable data stream includes various scalable layers, at least the lower scaling layers of which can be transmitted and decoded independently of the higher scaling layers. In addition, scaling layers or enhancement layers are added to the first scaling layer or base layer in a scalable processing of the data. The fully equipped encoder has a first scaling layer and can in principle generate one scalable data stream with an arbitrary number of additional scaling layers. The advantage of the scaling concept is that if a wideband transmission channel is available, the scaled data stream generated by the encoder is transmitted over the wideband transmission channel completely, i.e. including all the scaling layers. Can be. However, if there is only a narrowband transmission channel, the coded signal can be transmitted through the transmission channel only in the form of a first scaling layer or a specific number of additional scaling layers, where the specific number is an encoder. Is smaller than the total number of scaling layers generated by. Of course, an encoder adapted to the channel to which it is connected may generate a base scaling layer or a first scaling layer and many additional scaling layers depending on the channel.

On the decoder side, the scalable concept is also advantageous in that it is backwards compatible. This means that a decoder that can only process the first scaling layer can easily ignore the second and additional scaling layers of the data stream and generate a useful output signal. However, if the decoder is a state-of-the-art decoder capable of processing several scaling layers from the scaled data stream, this encoder can be addressed to the base decoder with the same data stream.

In the present invention, basic scalability means that the output of the quantization block, i.e., the bit stream encoder 60a, is psychoacoustically encoded data, for example, for one frame in consideration of FIG. (psychoacoustically coded data) is recorded in the first scaling layer of FIG. Preferably entropy-encoded differential spectral values generated by the combining means 59 are at simple scalability, 82 of FIG. 8A, comprising auxiliary audio data for one frame. Written in the second scaling layer, denoted by.

If the transport channel from the encoder to the decoder is one broadband transport channel, both scaling layers 81 and 82 can be transmitted to the decoder. However, if the transport channel is one narrowband transport channel that only "fits" the first scaling layer, the second scaling layer is easily removed from the data stream prior to transmission, so that the decoder designates only the first scaling layer. Can be addressed.

On the decoder side, the " base decoder " capable of processing only one psychoacoustic encoded data can easily omit the second scaling layer 82 as long as it is received via the wideband transport channel. However, if the decoder is a fully equipped decoder that includes both a psychoacoustic decoding algorithm and an integer decoding algorithm, the fully equipped decoder is lossless coded and again includes a first scaling layer and a second scaling layer for decoding to generate a decoded output signal. It can take all of the scaling layers.

In the preferred embodiment of the present invention schematically shown in FIG. 8A, the psychoacoustic encoded data for one frame is again in the first scaling layer. However, the second scaling layer of FIG. 8A is scaled more precisely, so that from this second scaling layer of FIG. 8A some of the second (smaller) scaling layer, third scaling layer, fourth scaling layer, etc. Scaling layers are created.

The differential spectral values output from adder 59 are particularly suited for further subscaling, as described based on FIG. 9. 9 schematically illustrates binary coded spectral values. Each column 90 in FIG. 9 represents a binary coded differential spectrum value. In FIG. 9, the differential spectral values are sorted according to frequency as indicated by arrow 91. Thus, the differential spectral value 92 has a higher frequency than the differential spectral value 90. The first column of the table of FIG. 9 provides the most significant bit of one differential spectral value. The second digit gives the bit with importance MSB-1, and the third column gives the bit with importance MSB-2. The third column from the back provides the bits with importance LSB + 2. The second column from the back provides the bits with importance LSB + 1. Finally, the last column provides the bits with the importance LSB, i.e., the least significant bits of the differential spectral values.

In a preferred embodiment of the present invention, precision scaling is performed, for example, when the 16 most significant bits of the differential spectral values are taken into the second scaling layer so that if desired, entropy coded by the entropy encoder 60b. Is done. The decoder using the second scaling layer obtains a differential spectral value with an accuracy of 16 bits on the output side, so that the second scaling layer together with the first scaling layer provides a lossless decoded audio signal in CD quality. It is known that there are audio samples of CD quality with a width of 16 bits.

On the other hand, if a studio quality audio signal, i.e. an audio signal having samples each comprising 24 bits, is supplied to the encoder, the encoder comprises a differential spectral value of the last eight bits, If necessary, a third scaling layer (means 60 in FIG. 6), which is also entropy encoded, may be further generated.

A fully equipped decoder that obtains a data stream having a first scaling layer, a second scaling layer (difference spectral value of 16 most significant bits), and a third scaling layer (difference spectral value of 8 lower bits), obtains all three scaling layers. Can be used to provide a lossless encoded / decoded audio signal of studio quality, i.e., having a word width of a 24-bit sample present at the output of the decoder.

In the studio area, samples with higher word length are more common than in the consumer area. In the consumer domain, the word width is 16 bits for the audio CD, whereas in the studio domain, 24 or 20 bits are used.

Based on the concept of scaling in the IntMDCT domain as described above, all three accuracies (16 bits, 20 bits or 24 bits) or arbitrary accuracies scaled to at least 1 bit are scalable Can be coded well.

Here, the audio signal provided with 24-bit accuracy is provided in the integer spectral region with the aid of inverse IntMDCT, and is scalable to be coupled to the hearing-adapted MDCT-based audiocoder output signal. Scalably combined.                 

Integer differential values present for lossless representation are not fully encoded in one scaling layer but are initially encoded with lower accuracy. Only in the additional scaling layer, the residual values necessary for accurate display are transmitted. Alternatively, however, the differential spectral values may be represented entirely, i.e., 24 bits, in one additional layer, so that an underlying scaling layer is not needed for decoding of this additional scaling layer. However, this scenario leads to a higher bit stream size as a whole, but since the scaling layer in the decoder is then no longer combined and can be decoded by one scaling layer alone, the transport channel When the bandwidth of the signal is unproblematic, it can contribute to the simplification of the decoder.

For example, if the lower eight LSBs shown in FIG. 9 are not transmitted first, scalability between 24 and 16 bits is achieved.

For inverse conversion of values transmitted in the time domain with lower accuracy, the transmitted values are inversely scaled back to the original region, for example 24 bits, for example by multiplying them by 2 ⁸ . desirable. Inverse IntMDCT is then applied to correspondingly scaled-back values.

In the fine scaling in the frequency domain of the present invention, it is also preferable to use the redundancy of LSBs. For example, if an audio signal has very little energy in the upper frequency domain, it is also significantly smaller than the possible values (-128, ..., 127) in the IntMDCT spectrum, for example, 8 bits. Represent a smaller value. This is shown in the compressibility of LSB values of the IntMDCT spectrum. In addition, very small differential spectral values, typically the majority of bits of MSB-1 in the MSB, are generally zero, and in addition, the first of the binary coded differential spectral values, i.e. leading 1, is the significance MSB-n. Does not appear before the bit with -1. In such a case, entropy coding is particularly well suited for further data compression when the differential spectral value of the second scaling layer contains only zero.

According to another embodiment of the present invention, one sample rate scalability is preferred for the second scaling layer of FIG. 8A. Sample rate scalability is obtained by the difference spectrum value up to the first cutoff frequency included in the second scaling layer, as shown on the right side of FIG. 9, while the difference spectrum value having a frequency between the first cutoff frequency and the maximum frequency. Is included in the additional scaling layer. Of course, further scaling may be performed such that several scaling layers are made in the entire frequency domain.

In a preferred embodiment of the present invention, the second scaling layer of FIG. 9 includes differential spectral values up to a frequency of 24 kHz corresponding to a sample rate of 48 kHz. The third scaling layer then includes spectral values from 24 kHz to 48 kHz corresponding to a sample rate of 96 kHz.                 

Not all bits of the differential spectral values in the second and third scaling layers need to be coded. In a further form of combined scalability, the second scaling layer may include differential spectral values of MSB to MSB-X bits up to a specific cutoff frequency. The third scaling layer can then include differential spectral values of the MSB to MSB-X bits from the first cutoff frequency to the maximum frequency. The fourth scaling layer can then include differential spectral values of residual bits up to the cutoff frequency. The final scaling layer can then include the differential spectral value of the residual bits for the higher frequency. This concept causes the table of FIG. 9 to be divided into quarters, each representing one scaling layer.

Regarding the scalability of frequency, in a preferred embodiment of the present invention, scalability between 48 kHz and 96 kHz sample rates is described. The 96 kHz sample signal is first encoded and transmitted only in the IntMDCT region of the lossless extension layer. If the upper part is not sent further, it is recognized as 0 in the decoder. Inverse IntMDCT (the same length as in the encoder), followed by a 96 kHz signal that does not include the energy of the higher frequency domain, which can be subsampled at 48 kHz without quality losses.

In practice, scaling of differential spectral values in the quadrant of FIG. 9 with fixed boundaries, since the scaling layer must include, for example, 16-bit or 8-bit or spectral values higher than or above the cutoff frequency. Is advantageous with regard to the size of the scaling layer.

Another scaling is to “soften” some of the quadrant boundaries of FIG. 9. In the example of frequency scalability, this may mean that the differential spectral value does not apply to a so-called "brickwall low pass" where the difference spectrum value is unchanged before the cutoff frequency and zero after the cutoff frequency. Instead, the spectral values below the cutoff frequency are somewhat hindered but the spectral values above the cutoff frequency can also be filtered out with any lowpass that still retains energy despite the differential spectral value decreasing energy. . The resulting scaling layer then also includes spectral values above the cutoff frequency. However, since these spectral values are relatively small, they can be effectively coded by entropy coding. In this case, the highest scaling layer has a difference between a complete difference spectrum value and a spectral value included in the second scaling layer.

Precision scaling can similarly be weakened to some extent. The first scaling layer can also have a spectral value, for example greater than 16 bits, where the next scaling layer still has a difference thereafter. In general, the second scaling layer has a differential spectral value with such a low accuracy because the difference between the rest of the next scaling layer, that is, the full spectral value and the spectral value included in the second scaling layer, is transmitted. This reduces the variable accuracy of accuracy.                 

The encoding or decoding method of the present invention is preferably stored in a digital storage medium such as a floppy disk as electronically readable control signals, where the control signal is a programmable computer. The encoding and / or decoding method may be executed in cooperation with. In other words, there is a computer program product having a program code stored in a machine-readable carrier for executing an encoding method and / or a decoding method when the program product is executed in a computer. The method of the present invention can be realized with program code for executing the method of the present invention when the computer program is executed on a computer.

Next, as an example of the integer transform algorithm, it leads to the IntMDCT transform algorithm described in "Audio Coding Based on Integer Transforms" [111st Order AES Convention, New York, 2001]. IntMDCT is particularly advantageous because it has the salient characteristics of MDCT, such as an excellent spectral representation of the audio signal, critical sampling and block superposition. As shown by arrow 62 in FIG. 5, the fact that one IntMDCT is a good approximation of MDCT makes it possible to use only one transform algorithm in the decoder shown in FIG. 5. On the basis of Figs. 1 to 4, substantial characteristics of this particular type of integer conversion algorithm are described.

1 shows a schematic diagram of a preferred apparatus of the present invention for processing time-discrete samples representing one audio signal to obtain an integer value at which the Int-MDCT integer conversion algorithm operates. Time-discrete samples are windowed by the apparatus shown in FIG. 1 and optionally converted into spectral representations. The time-discrete samples supplied to the device at the input 10 are windowed into one window w having a length corresponding to 2N time-discrete samples to obtain integer windowing samples at the output 12. This is suitable for conversion into one spectral representation by means of conversion and in particular means 14 for carrying out one integer DCT. Unlike the MDCT function 408 of FIG. 4A, the integer DCT is made to produce an N output value from the N input value, which only generates N spectral values from 2N windowed samples due to the MDCT equation.

To window the time-discrete samples, firstly two time-discrete samples are selected in means 16 which simultaneously represent one vector of time-discrete samples. The time-discrete samples selected by the means 16 are in the frist quarter of the window. Another time-discrete sample is in the second quadrant of the window described in more detail based on FIG. 3.

A rotation matrix of 2 × 2 dimension is applied to the vector produced by the means 16, where this operation is not executed immediately except by some so-called lifting matrices.

The lifting matrix has a property that depends on the window w and consists of only one element, not "1" or "0". The factorization of the wavelet transform to the lifting step is published by Ingrid Daubechies and Weim Sweldens, "Factoring Wavelet Transforms Into Lifting Steps" [preprint , Bell Laboratories, Lucent Technologies, 1996. In general, the lifting scheme is a simple relation between perfectly reconstructed filter pairs with the same low or high-pass filter. Each pair of complementary filters may be factorized in the lifting step. This applies in particular to Givens rotations. Considering the case where the poly-phase matrix is one Givens rotation, the following applies.

Each of the three lifting matrices to the right of the equal sign have the value "1" as the main diagonal elements. Also, in each lifting matrix one element not on the major diagonal is zero, and one element not on the major diagonal depends on the angle of rotation.

The vector is multiplied by a third matrix, i.e. the rightmost lifting matrix, in order to obtain the first result vector. This is illustrated by means 18 in FIG. 1. The first result vector is rounded with an arbitrary rounding function that maps the real set to an integer set, as described by means 20 in FIG. 1. At the output of the means 20, a rounded first result vector is obtained. A rounding first result vector is fed to the middle 22, ie, means for multiplying the second lifting matrix, to obtain a second result vector that is rounded again in means 24 to obtain a rounding second result vector. The rounding second result vector outputs integer windowed samples that must be processed by means 14 to obtain an integer spectral value at spectral output 30 when its spectral representation is needed. Is fed to the lifting matrix shown to the left of the equation, i.e. the means 26 for multiplying the first one, to obtain a third result vector that is still rounded to a means 28 for obtaining.

The means 14 is preferably embodied in an integer DCT.

The discrete cosine transform according to type 4 (DCT-IV) with length N is given by:

The coefficients of DCT-IV form an orthonormal N × N matrix. Each square N x N matrix is p. blood. As described in PP Vaidyanathan's publication "Multirate Systems And Filter Banks" (Prentice Hall, Englewood Cliffs, 1993). , N (N-1) / 2 Givens rotations. It can be seen that there is an additional split.                 

Regarding the classification of various DCT algorithms, H. s. See Malcolm's "Signal Processing With Lapped Transforms" [Artech House, 1992.] In general, DCT algorithms have their basis function. The DCT-IV preferred in the present invention includes an asymmetric basis function, that is, a cosine quarter wavelength, a cosine 3/4 wavelength, a cosine 5/4 wavelength, a cosine 7/4 wavelength, and the like. Discrete cosine transforms of type II (DCT-II) have axis-symmetrical and point-symmetrical basis functions, where the 0th basis function has one DC element and the first basis The function is half the cosine wavelength, the second base function is the total cosine wavelength, etc. Due to the fact that DCT-II takes into account the DC component in particular, unlike video encoding, since the DC component is irrelevant This is the audio encoding Used in non-video encoding.

In the following, it is explained how the rotation angle a of the Givens rotation depends on the window function.

MDCT with 2N window length can reduce the discrete cosine transform of type IV with N length. This is accomplished by a TDAC operation explicitly executed in the time domain and then DCT-IV applied. The left half of the window of block t overlaps with the preceding block, i.e., the right half of block t-1, by 50% overlap. The overlap of two consecutive blocks (t-1 and t) is preprocessed in the time domain, before conversion, ie between the input 10 and the output 12 of FIG. 1 as follows:                 

In the above equation, an x value addressed without a tilde is a value at the input unit 10 or a value after the selection means 16, while an x value with a tilde is the output unit of FIG. It is the value in (12). While w represents the window function, the running index k is a value from 0 to N / 2-1.

From the TDAC condition for the window function w the following relationship applies:

For any angle a _k (k = 0,..., N / 2-1), this pretreatment in the time domain can be recorded in Givens rotation as described above.

The angle a of the Givens rotation depends on the window function w as follows:

Any window function (w) that meets this TDAC condition can be used.

In the following, a cascaded encoder and decoder are described based on FIG. The time-discrete samples (x (0) through x (2N-1)) windowed together into one window are initially selected by means 16 of FIG. 1 to obtain a sample [x (0)]. And samples [x (N-1)], i.e., samples of the first quadrant of the window and samples of the second quadrant of the window, are selected to produce a vector at the output of the means 16. The cross arrows outline the lifting multiplication and the successive roundings of the means 18, 20 or 22, 24 or 26, 28 to obtain integer windowing samples at the input of the DCT-IV block.

When the first vector is processed as described above, the second vector is a sample [x (N / 2-1) and x (N / 2)], i.e. one sample of the first quadrant of the window and the second of the window. One sample of the quadrant is selected and again processed by the algorithm described in FIG. All other sample pairs of the first and second quadrants of the window are similarly processed. The same processing is performed for the third and fourth quadrants of the first window. As illustrated in FIG. 2, there are N windowing integer samples fed to one DCT-IV transform at the output 12. In particular, integer windowing samples of the second and third quadrants are fed to the DCT. Windowing integer samples of the first quadrant of the window are treated with preceding DCT-IV along with windowing integer samples of the fourth quadrant of the preceding window. A fourth quadrant of the windowing integer samples of FIG. 2 is similarly fed to one DCT-IV transform with the first quadrant of the next window. The center integer DCT-IV transform 32 shown in FIG. 2 provides N integer spectral values y (0) to y (N-1). These integer spectral values can be easily entropy coded without out of an intervening quantization because windowing and transform provide an integer output value.                 

The decoder is described in the half of the right half of FIG. Decoders that include inverse transforms and "inverse windowing" act as the inverse of the encoder. Inverse DCT-IV may be used for inverse transformation of DCT-IV as shown in FIG. 2. In order to regenerate time-discrete audio samples (x (0) to x (2N-1)) from the integer windowing samples at the means 34 or at the output of the preceding and trailing transform, as shown in FIG. Likewise, the output value of the decoder (DCT-IV) 34 is reversed to the corresponding value of the preceding or trailing transform.

The output-side operation is created by inverse Givens rotation, so that blocks 26, 28 or 22, 24 or 18, 20 are passed in the opposite direction. This is explained in more detail based on the second lifting matrix of equation (1). When the second result vector (in the encoder) is created by multiplying the rounding first result vector by the second lifting matrix [means 22], the following term results:

The right value (x, y) of equation (6) is an integer. However, this does not apply to the [x sinα] value. Here, the rounding function r should be described by the following equation.

This operation executes the means 24.

Inverse mapping (in the decoder) is defined as follows:                 

Due to the minus sign before the rounding action, it becomes clear that the integer approximation of the lifting step can be reversed without introducing an error. The application of this approximation to each of the three lifting steps leads to an integer approximation of Givens rotation. When the algorithm decoding of Fig. 1 is executed from bottom to top, the rounding rotation (in the encoder) can be reversed (in the decoder) without introducing an error, i.e. by passing the reverse rounding lifting step in reverse order.

If the rounding function r is point symmetrical, the reverse rounding rotation is equal to the rounding rotation with the angle -α and is read as follows.

In this case the decoder, i.e. the lifting matrix for the inverse Givens rotation, is immediately produced as a result of equation (1) by simply replacing the term "sinα" with the term "-sinα".

In the following, a split-up of a conventional MDCT with overlapping windows 40 to 46 is described once again based on FIG. 3. The windows 40 to 46 are 50% overlap each. As outlined by the arrow 48, each window first undergoes a Givens rotation within the first and second quadrants of the window or within the third and fourth quadrants of the window. The rotation value, or windowing integer samples, is then fed to the N-to-N DCT, such that the second and third quadrants of the window or the fourth and first quadrants of the continuous window are together. Is converted into the spectral display.

Therefore, a typical Givens rotation that is executed continuously is divided into lifting matrices, where one rounding step is introduced after each lifting matrix multiplication, so that floating-point numbers round immediately after development, and the lifting matrix of the result vector Before each multiplication by, the result vector has only integers.

The output value always remains an integer, preferably using an integer input value. Exemplary PCM samples stored on one CD are integer values that vary depending on the bit width, ie, whether the time-discrete digital input value is a 16-bit or 24-bit value. This does not mean a limit. However, as described above, the entire process is invertible by executing reverse rotation in reverse order. Thus, there is an integer approximation of a perfectly reconstructed MDCT, i.e. a lossless transform.

The shown transform provides an integer output value instead of a floating point value. When performing forward and then reverse translations, it provides complete recovery so that no errors are introduced. The transform according to the preferred embodiment of the present invention is a replacement for the modified discrete cosine transform. However, other conversion methods can also be carried out in an integer manner, with division into rotation and division of rotation into lifting steps.

Integer MDCTs have the most advantageous properties of MDCT. It has one overlapping structure, whereby better frequency selectivity is obtained than in a non-overlapping block. With the TDAC functionality already considered when windowed prior to conversion, critical sampling is maintained so that the overall number of spectral values representing one audio signal is equal to the total number of input samples.

Compared to normal MDCT, which provides floating point samples in the preferred integer transformations described above, noise is increased only in the spectral region where there is little signal level, but this increase in noise is a significant signal level. I can't feel it.

Since only multiplication steps that can be easily divided into shift / add steps and can be executed easily and quickly in hardware are used, integer processing itself contributes to valid hardware execution. Of course, software execution is also possible.

Integer conversion provides an excellent spectral representation of the audio signal but still remains in the integer range. When applied to the tonal parts of an audio signal, this concentrates the energy considerably. As such, an efficient lossless coding scheme can be built up by simply cascading the windowing / transformation described in FIG. 1 with one entropy encoder. In particular, stacked coding using escape values is advantageous because it is used in MPEG AAC. It is desirable to reduce all values by a certain power of two until they fit the desired sign table and then code the omitted least significant bits. Compared with other cases using a larger sign table, another case is more advantageous for storage consumption for storing the sign table. Nearly lossless coding can also be obtained by simply omitting any least significant bit.

In particular, for tonal signals, entropy coding of integer spectral values enables high coding gain. In the transient parts of the signal,

Due to the flat spectrum of the transient signal, in other words because of the low spectral value being zero or almost zero, the coding gain is low. second. Here, Jay. D. J. Herre, JD Johnston, "Enhancing the Performance of Perceptual Audio Coders by Using Temporal Noise Shaping (TNS)" [101st AES Convention, Los Angeles, 1996, As described in Preprint 4384, this flatness can however be used for the use of linear prediction in the frequency domain. One alternative is a predictor with an open loop. Another alternative is a predictor with a closed loop. The first alternative, the predictor with open loop, is called TNS. Post-prediction quantization leads to the adaptation of the resulting quantization noise to the temporal structure of the audio signal, thereby preventing pre-echoes in the psychoacoustic audio coder. A second alternative for lossless audio coding, i.e., a predictor with a closed loop, is more suitable, since the prediction using the closed loop allows accurate reconstruction of the input signal. When this technique is applied to the generated spectrum, the rounding step must be performed after each step of the prediction filter in order to stay in the range of integers. By using an inverse filter and the same rounding function, the original spectrum can be generated accurately.

In order to take advantage of the redundancy between the two channels for data reduction, center-side coding can be used in a lossless manner in which a rounding rotation of angle a / 4 is used. Rounding rotation has the advantage of energy conservation when compared to other methods of calculating the sum and difference of the left and right channels of a stereo signal. Since so-called joint stereo coding techniques are also implemented in the standard MPEG AAC, its use can be switched on or off for each band. Additional angles of rotation can also be considered to more flexibly reduce the redundancy between the two channels.

Claims (33)

Means 52 for providing a quantization block of spectral values of the quantized time-discrete audio signal using a psychoacoustic model 54; Means (58) for dequantizing the quantization block and rounding the dequantized spectral value to obtain a rounding block of rounded inversely quantized spectral values; Means for generating an integer block of integer spectral values using an integer transform algorithm configured to generate an integer block of spectral values from a block of integer time-discrete samples 56; Combining means for generating one differential block according to the spectral value-wise difference between the rounding block and the integer block in order to obtain a difference block having differential spectral values (combination means) 59; A time for obtaining encoded audio data, comprising means (60) for processing the quantized block and the differential block to produce encoded audio data comprising information of the quantization block and information of the differential block. Discrete audio signal encoding apparatus. The method of claim 1, wherein the quantization block providing means (52) The MDCT block is formed using a psychoacoustic model to form an MDCT block of MDCT spectral values in a time block of temporal audio signal values through MDCT, and generate a quantized block composed of quantized MDCT spectral values. Apparatus for encoding time-discrete audio signals configured to quantize to obtain coded audio data. The method according to claim 2, wherein the means for generating the integer block (56) A time-discrete audio signal encoding apparatus for obtaining encoded audio data, configured to execute IntMDCT on the time block to form an integer block composed of IntMDCT spectral values. The method of claim 1, wherein the quantization block providing means (52) A time-discrete audio signal encoding apparatus for obtaining coded audio data, configured to calculate a quantization block using a floating-point transform algorithm. The method according to any one of claims 1 to 3, wherein the providing means 52 A time-discrete audio signal encoding apparatus for obtaining coded audio data, configured to calculate a quantization block using an integer block generated by the generating means (56). The method of claim 1 wherein the processing means 60 comprises: Entropy-encode the quantization block (60a) to obtain an entropy-coded quantization block, Entropy encoding the rounding block (60b) to obtain an entropy-encoded rounding block, Converting the entropy-coded quantization block into a first scaling layer of a scaled data stream representing coded audio data, and converting the entropy-encoded rounding block into a second scaling layer of the scale data stream. And a time-discrete audio signal encoding apparatus for obtaining encoded audio data. The method of claim 6, wherein the processing means 60 further comprises: A quantizer formed for use with one of a plurality of code tables that depend on quantization spectral values for entropy encoding of the quantization block, and usable for quantization to produce a quantization block for entropy encoding of the differential block. A time-discrete audio signal encoding apparatus for obtaining coded audio data, configured to select one of a plurality of code tables depending on the characteristic. The method of claim 1, The quantization block providing means 52 is configured to use one of a plurality of windows to window a temporal block of an audio signal value depending on the characteristics of the audio signal, And said generating means (56) is configured to select a window, such as a window, to use for windowing said time block for said integer conversion algorithm. The method of claim 1, The generating means, Corresponds to 2N time-discrete samples to provide windowed time-discrete samples for conversion of the time-discrete sample to a spectral representation through a transform capable of generating N outputs with N input values. Windowing the time-discrete samples into a window w having a length of A small step 16 of selecting one time-discrete sample from one quadrant of the window and one time-discrete sample from the other quadrant of the window to obtain a vector of time-discrete samples, A partial step 18 of multiplying the vector of time-discrete samples by a lifting matrix to obtain a first result vector, and mapping a real number to an integer to obtain a rounded first result vector. A rounding function (r), which comprises a partial step (20) of rounding one component of the first result vector, each element dependent on the window (w) and not one or zero. And a square rotation matrix whose dimension matches a dimension of the vector of time-discrete samples, wherein the dimension is matched to the vector of time-discrete samples. Small steps to apply, Until all the lifting matrices have been processed to obtain a rotated vector consisting of an integer window sample from one quadrant of the window and an integer window sample from the other quadrant of the window. A small step of successively executing the steps of multiplying (22) and rounding (24) by the lifting matrix, and A sub-step of performing windowing on all time-discrete samples of the remaining quadrants of the window to obtain 2N filtered integer values; And N windowed integer samples are subjected to spectral representation by integer DCT processing on values having filtered integer samples of the second quadrant of the window and the third quadrant of the window to obtain N integer spectral values. Time-discrete audio signal encoding apparatus for obtaining encoded audio data, the method being configured for using an integer conversion algorithm configured to convert (14). The method of claim 1, The means for providing the quantization block 52 uses a predictive filter prior to the quantization step 52b to obtain the prediction residual spectral values that enable the quantization block to be represented after quantization. Is configured to predict spectral values over the frequency; And encoding means for predicting a frequency of an integer spectral value of the integer block, and rounding means for rounding the prediction residual spectral value to an integer spectral value for displaying a rounding block. Time-discrete audio signal encoding apparatus for obtaining. The method of claim 1, The time-discrete audio signal comprises at least two channels; The providing means 52 is configured to perform center / side coding with the spectral values of the time-discrete audio signal to obtain a quantization block after quantization of the center / side spectral values; And And a means (56) for generating said integer block is further configured to perform center / side coding corresponding to the center / side coding of said providing means (52). The method of claim 1, The processing means 60 is configured to generate an MPEG-2 AAC data stream, and encoded audio data into which ancillary information for an integer conversion algorithm is introduced into one field in the ancillary data. Time-discrete audio signal encoding apparatus for obtaining. The method of claim 1, And the processing means (60) is configured to output the encoded audio data to a data stream having a plurality of scaling layers. The method of claim 13, The processing means 60 is configured to insert information of the quantization block into the first scaling layer 81 and insert the information of the difference block into the second scaling layer 82, to obtain time-discrete for obtaining the encoded audio data. Audio signal encoding device. The method of claim 13, The processing means 60 is configured to insert information of the quantization block into the first scaling layer 81 and insert information of the difference block into at least the second and third scaling layers. Discrete audio signal encoding apparatus. The method of claim 15, Encoding in which a reduced spectral value with reduced accuracy is included in a second scaling layer and a residual part of the differential spectral value is included in one or more higher scaling layers. An apparatus for encoding time-discrete audio signals for obtaining audio data. The method of claim 15, The information of the difference block includes binary coded difference spectral values, The second scaling layer for the differential block comprises a plurality of bits from the most significant bit (MSB) to the less significant bit (MSB-x) for the differential spectral value, and Apparatus for encoding coded audio data, wherein the third scaling layer includes a plurality of bits from a less significant bit (MSB-x-1) to a least significant bit (LSB) . The method of claim 17, The time-discrete audio signal is present in the form of a sample having a width of 24 bits, The processing means 60 inserts more valid 16 bits of the differential spectral value into the second scaling layer and inserts the remaining 8 bits of the differential spectral value into the third scaling layer so that the decoder 12. A time-discrete audio signal encoding apparatus for obtaining encoded audio data, configured to reach CD quality using two scaling layers and reaching studio quality using a third scaling layer as well. The method of claim 15, The processing means 60, Inserting at least a portion of one differential spectral value into a second scaling layer for display of a low-pass signal, Time-discrete audio signal encoding for obtaining encoded audio data, configured to insert a difference between the difference spectral value of the second scaling layer and the original difference spectral values into at least one additional scaling layer. Device. The method of claim 15, The processing means 60 inserts at least a portion of the difference spectral value up to a certain cut-off frequency into a second scaling layer, and the difference from the specific cutoff frequency to a higher frequency. And a time-discrete audio signal encoding device for obtaining encoded audio data, the at least part of the spectral values being inserted into the third scaling layer. Providing 52 a quantization block of spectral values of the quantized time-discrete audio signal using psychoacoustic model 54; Inverse quantizing the quantization block and rounding the inverse quantization spectral value to obtain a rounding block of rounded inverse quantization spectral values; Generating (56) an integer block of integer spectral values using an integer transform algorithm configured to generate an integer block of spectral values from a block of integer time-discrete samples; Forming a differential block according to a spectral value-wise difference between a rounding block and an integer block to obtain a differential block having a differential spectral value (59); And processing 60 the quantization block and the differential block to generate encoded audio data including information of the quantization block and information of the differential block. . Psychoacoustic model 54 is used to provide a quantization block of spectral values of the quantized time-discrete audio signal (52), inversely quantize the quantization block to obtain a quantized block of rounded inverse quantization spectral values, and inverse quantization spectrum. Rounding the value (58), generating an integer block of integer spectral values (56) using an integer conversion algorithm configured to generate an integer block of spectral values from integer time-discrete samples, and having a differential spectral value An apparatus for decoding encoded audio data generated from a time-discrete audio signal by forming (59) one difference block according to the spectral value difference between a rounding block and an integer block to obtain a difference block. Means (70) for processing the encoded audio data to obtain one quantization block and one difference block; Means 74 for dequantizing and rounding the quantization block to obtain one integer inversely quantized quantization block; Means (78) for combining the integer quantization block and the differential block into spectral related values to obtain one combined block; And Generating from a time-discrete audio signal, comprising means 82 for using a joint block and generating a temporal representation of the time-discrete audio signal using the inverse integer transform algorithm of the integer transform algorithm. Encoded audio data decoding apparatus. The method of claim 22, The encoded audio data is scaled and comprises a plurality of scaling layers, And ascertain a quantization block from the encoded audio data as a first scaling layer and identify a difference block from the encoded audio data as a second scaling layer. The method of claim 22, The information of the difference block includes binary coded differential spectral values; The encoded audio data is scaled and comprises a plurality of scaling layers; The means for processing the encoded audio data 70 identifies the quantization block obtained from the encoded audio data as a first scaling layer and displays a representation of the differential spectral value with reduced accuracy in the second scaling layer. And an encoded audio data decoding device generated from a time-discrete audio signal. The method of claim 24, The means for processing the coded audio data 70 is configured to extract a plurality of bits from a most significant bit to a less significant bit than the least significant bit of the differential spectral value. To extract to a second scaling layer, and Means for generating a time representation of the time-discrete audio signal 82, configured to generate missing bits for differential spectral values synthetically prior to use of the integer conversion algorithm. An encoded audio data decoding apparatus generated from a signal. The method of claim 25, The means 82 is adapted for synthetic generation, in which one scale factor equal to 2 ⁿ (where n is the number of lower bits not included in the second scaling layer) is used. An apparatus for decoding encoded audio data generated from a time-discrete audio signal, configured to perform upscaling of a second scaling layer or to use a dithering algorithm for comprehensive generation. The method of claim 22, The encoded audio data is scaled and comprises a plurality of scaling layers, Means 70 for processing the encoded audio data is configured to identify a quantization block obtained from the encoded audio data with a first scaling layer and identify a low-pass filtered differential spectrum value with a second scaling layer. The encoded audio data decoding apparatus generated from the. The method of claim 22, The encoded audio data is scaled and comprises a plurality of scaling layers, The means for processing the coded audio data 70 identifies the quantization block of coded data as a first scaling layer and a first cutoff that is less than the maximum frequency of the differential spectral value that can be generated in one coder. The encoded audio data decoding apparatus generated from the time-discrete audio signal, configured to confirm the differential spectrum value up to frequency with the second scaling layer. The method of claim 28, Means 82 for generating a time representation of the time-discrete audio signal sets an input value in an integer conversion algorithm for a full length of cutoff frequency of a second scaling layer to a predetermined value. And time-discrete after using an inverse integer transform algorithm with one factor selected corresponding to the ratio of the maximum frequency and the cutoff frequency of the differential spectrum value that can be generated by one encoder. An encoded audio data decoding apparatus generated from a time-discrete audio signal, configured to downsample a time representation of an audio signal. The method of claim 29, And the predetermined value is 0 for all input values on the cutoff frequency. Processing (70) the encoded audio data to obtain one quantization block and one difference block; Inverse quantizing and rounding the quantization block to obtain one integer dequantized quantization block; Spectral value-wise combining 78 with an integer quantization block and a differential block to obtain one combining block; And Using an integer transform algorithm inversed to an integer transformation algorithm used when generating the coded audio data Decoding of the encoded audio data generated from the time-discrete audio signal by a providing, inverse quantization, generation, forming and processing step comprising the step 82 of generating a time representation of the time-discrete audio signal. Way. A computer program storage medium storing a computer program having a program code for executing the encoding method of claim 21 when executed in a computer. A computer program storage medium having stored therein a computer program having a program code for executing the decoding method of claim 31 when executed in a computer.

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