Simultaneous TimeDomain and FrequencyDomain Noise Shaping for TDAC Transforms

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First Claim
1. A frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
 splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; and
for each spectral band;
calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window;
calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and
filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.
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Abstract
A frequencydomain noise shaping method and device interpolates a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal. In the method and device, transform coefficients of the windowed and transformcoded audio signal are split into a plurality of spectral bands. For each spectral band, a first gain representing a spectral shape of the quantization noise at a first transition between a first time window and a second time window is calculated, a second gain representing a spectral shape of the quantization noise at a second transition between the second time window and a third time window is calculated, and the transform coefficients of the second time window are filtered based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.
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30 Claims
 1. A frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; and for each spectral band; calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window; calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.  View Dependent Claims (3, 5, 6, 7, 9, 10)
 2. A frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; and for each spectral band; calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window; calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein the audio signal is windowed using successive overlapping windows, wherein the first gain is a noise gain calculated at a middle point of an overlap between the first and second time windows, and wherein the second gain is a noise gain calculated at a middle point of an overlap between the second and third time windows.
 4. A frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; and for each spectral band; calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window; calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein filtering the transform coefficients comprises achieving a desired spectral shape of the quantization noise at the first and second transitions and a smooth transition of an envelope of this spectral shape from the first transition to the second transition.
 8. A frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; and for each spectral band; calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window; calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein filtering the transform coefficients comprises calculating filter parameters on the basis of the first and second calculated gains.
 11. A frequencydomain noise shaping device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
a splitter of the transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; a calculator, for each spectral band, of a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window, and of a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and a filter of the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.  View Dependent Claims (13, 15, 16, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
 12. A frequencydomain noise shaping device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
a splitter of the transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; a calculator, for each spectral band, of a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window, and of a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and a filter of the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein the audio signal is windowed using successive overlapping windows, and wherein the calculator calculates the first gain at a middle point of an overlap between the first and second time windows, and the second gain at a middle point of an overlap between the second and third time window.
 14. A frequencydomain noise shaping device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
a splitter of the transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; a calculator, for each spectral band, of a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window, and of a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and a filter of the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein the transform coefficient filter achieves a desired spectral shape of the quantization noise at the first and second transitions and a smooth transition of an envelope of this spectral shape from the first transition to the second transition.
 18. A frequencydomain noise shaping device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising:
a splitter of the transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; a calculator, for each spectral band, of a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window, and of a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and a filter of the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise; wherein the transform coefficient filter calculates filter parameters on the basis of the first and second calculated gains.
1 Specification
The present disclosure relates to a frequencydomain noise shaping method and device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal.
Specialized transform coding produces important bit rate savings in representing digital signals such as audio. Transforms such as the Discrete Fourier Transform (DFT) and the Discrete Cosine Transform (DCT) provide a compact representation of the audio signal by condensing most of the signal energy in relatively few spectral coefficients, compared to the timedomain samples where the energy is distributed over all the samples. This energy compaction property of transforms may lead to efficient quantization, for example through adaptive bit allocation, and perceived distortion minimization, for example through the use of noise masking models. Further data reduction can be achieved through the use of overlapped transforms and TimeDomain Aliasing Cancellation (TDAC). The Modified DCT (MDCT) is an example of such overlapped transforms, in which adjacent blocks of samples of the audio signal to be processed overlap each other to avoid discontinuity artifacts while maintaining critical sampling (N samples of the input audio signal yield N transform coefficients). The TDAC property of the MDCT provides this additional advantage in energy compaction.
Recent audio coding models use a multimode approach. In this approach, several coding tools can be used to more efficiently encode any type of audio signal (speech, music, mixed, etc). These tools comprise transforms such as the MDCT and predictors such as pitch predictors and Linear Predictive Coding (LPC) filters used in speech coding. When operating a multimode codec, transitions between the different coding modes are processed carefully to avoid audible artifacts due to the transition. In particular, shaping of the quantization noise in the different coding modes is typically performed using different procedures. In the frames using transform coding, the quantization noise is shaped in the transform domain (i.e. when quantizing the transform coefficients), applying various quantization steps which are controlled by scale factors derived, for example, from the energy of the audio signal in different spectral bands. On the other hand, in the frames using a predictive model in the timedomain (which typically involves longterm predictors and shortterm predictors), the quantization noise is shaped using a socalled weighting filter whose transfer function in the ztransform domain is often denoted W(z). Noise shaping is then applied by first filtering the timedomain samples of the input audio signal through the weighting filter W(z) to obtain a weighted signal, and then encoding the weighted signal in this socalled weighted domain. The spectral shape, or frequency response, of the weighting filter W(z) is controlled such that the coding (or quantization) noise is masked by the input audio signal. Typically, the weighting filter W(z) is derived from the LPC filter, which models the spectral envelope of the input audio signal.
An example of a multimode audio codec is the Moving Pictures Expert Group (MPEG) Unified Speech and Audio Codec (USAC). This codec integrates tools including transform coding and linear predictive coding, and can switch between different coding modes depending on the characteristics of the input audio signal. There are three (3) basic coding modes in the USAC:
 1) An Advanced Audio Coding (AAC)based coding mode, which encodes the input audio signal using the MDCT and perceptuallyderived quantization of the MDCT coefficients;
 2) An Algebraic Code Excited Linear Prediction (ACELP) based coding mode, which encodes the input audio signal as an excitation signal (a timedomain signal) processed through a synthesis filter; and
 3) A Transform Coded eXcitation (TCX) based coding mode which is a sort of hybrid between the two previous modes, wherein the excitation of the synthesis filter of the second mode is encoded in the frequency domain; actually, this is a target signal or the weighted signal that is encoded in the transform domain.
In the USAC, the TCXbased coding mode and the AACbased coding mode use a similar transform, for example the MDCT. However, in their standard form, AAC and TCX do not apply the same mechanism for controlling the spectral shape of the quantization noise. AAC explicitly controls the quantization noise in the frequency domain in the quantization steps of the transform coefficients. TCX however controls the spectral shape of the quantization noise through the use of timedomain filtering, and more specifically through the use of a weighting filter W(z) as described above. To facilitate quantization noise shaping in a multimode audio codec, there is a need for a device and method for simultaneous timedomain and frequencydomain noise shaping for TDAC transforms.
In the appended drawings:
According to a first aspect, the present disclosure relates to a frequencydomain noise shaping method for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising splitting transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands. The frequencydomain noise shaping method also comprises, for each spectral band: calculating a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window; calculating a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and filtering the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.
According to a second aspect, the present disclosure relates to a frequencydomain noise shaping device for interpolating a spectral shape and a timedomain envelope of a quantization noise in a windowed and transformcoded audio signal, comprising: a splitter of the transform coefficients of the windowed and transformcoded audio signal into a plurality of spectral bands; a calculator, for each spectral band, of a first gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a first transition between a first time window and a second time window, and of a second gain representing, together with corresponding gains calculated for the other spectral bands, a spectral shape of the quantization noise at a second transition between the second time window and a third time window; and a filter of the transform coefficients of the second time window based on the first and second gains, to interpolate between the first and second transitions the spectral shape and the timedomain envelope of the quantization noise.
According to a third aspect, the present disclosure relates to an encoder for encoding a windowed audio signal, comprising: a first coder of the audio signal in a timedomain coding mode; a second coder of the audio signal is a transformdomain coding mode using a psychoacoustic model and producing a windowed and transformcoded audio signal; a selector between the first coder using the timedomain coding mode and the second coder using the transformdomain coding mode when encoding a time window of the audio signal; and a frequencydomain noise shaping device as described above for interpolating a spectral shape and a timedomain envelope of a quantization noise in the windowed and transformcoded audio signal, thereby achieving a desired spectral shape of the quantization noise at the first and second transitions and a smooth transition of an envelope of this spectral shape from the first transition to the second transition.
According to a fourth aspect, the present disclosure relates to a decoder for decoding an encoded, windowed audio signal, comprising: a first decoder of the encoded audio signal using a timedomain decoding mode; a second decoder of the encoded audio signal using a transformdomain decoding mode using a psychoacoustic model; and a selector between the first decoder using the timedomain decoding mode and the second decoder using the transformdomain decoding mode when decoding a time window of the encoded audio signal; and a frequencydomain noise shaping device as described above for interpolating a spectral shape and a timedomain envelope of a quantization noise in transformcoded windows of the encoded audio signal, thereby achieving a desired spectral shape of the quantization noise at the first and second transitions and a smooth transition of an envelope of this spectral shape from the first transition to the second transition.
In the present disclosure and the appended claims, the term “time window” designates a block of timedomain samples, and the term “windowed signal” designates a time domain window after application of a nonrectangular window.
The basic principle of Temporal Noise Shaping (TNS), referred to in the following description will be first briefly discussed.
TNS is a technique known to those of ordinary skill in the art of audio coding to shape coding noise in time domain. Referring to
 A transform processor 101 to subject a block of samples of an input audio signal x[n] to a transform, for example the Discrete Cosine Transform (DCT) or the Modified DCT (MDCT), and produce transform coefficients X[k];
 A single filter 102 applied to all the spectral bands, more specifically to all the transform coefficients X[k] from the transform processor 101 to produce filtered transform coefficients X_{f}[k];
 A processor 103 to quantize, encode, transmit to a receiver or store in a storage device, decode and inverse quantize the filtered transform coefficients X_{f}[k] to produce quantized transform coefficients Y_{f}[k];
 A single inverse filter 104 to process the quantized transform coefficients Y_{f}[k] to produce decoded transform coefficients Y[k]; and, finally,
 An inverse transform processor 105 to apply an inverse transform to the decoded transform coefficients Y[k] to produce a decoded block of output timedomain samples y[n].
Since, in the example of
With reference to
Operation 301 (FIG. 3)—Transform
The input audio signal x[n] of
In operation 301, the input signal x[n] is transformed through a transform processor 201 (
Operation 302 (FIG. 3)—Band splitting
In operation 302, a band splitter 202 (
Operation 303 (FIG. 3)—Filtering 1, 2, 3, . . . , M
After band splitting 302, in operation 303, each spectral band B_{1}[k], B_{2}[k], B_{3}[k], . . . , B_{M}[k] is filtered through a bandspecific filter (Filters 1, 2, 3, . . . , M in
Operation 304 (FIG. 3)—Quantization, encoding, transmission or storage, decoding, inverse quantization
In operation 304, the filtered bands B_{1f}[k], B_{2f}[k], B_{3f}[k], . . . , B_{Mf}[k] from Filters 1, 2, 3, . . . , M may be quantized, encoded, transmitted to a receiver (not shown) and/or stored in any storage device (not shown). The quantization, encoding, transmission to a receiver and/or storage in a storage device are performed in and/or controlled by a Processor Q of
In operation 304, quantized and encoded filtered bands B_{1f}[k], B_{2f}[k], B_{3}[k], . . . , B_{Mf}[k] may also be received by the transceiver or retrieved from the storage device, decoded and inverse quantized by the Processor Q. These operations of receiving (through the transceiver) or retrieving (from the storage device), decoding and inverse quantization produce quantized spectral bands C_{1f}[k], C_{2f}[k], C_{3f}[k], . . . , C_{Mf}[k] at the output of the Processor Q.
Any type of quantization, encoding, transmission (and/or storage), receiving, decoding and inverse quantization can be used in operation 304 without loss of generality.
Operation 305 (FIG. 3)—Inverse Filtering 1, 2, 3, . . . , M
In operation 305, the quantized spectral bands C_{1f}[k], C_{2f}[k], C_{3f}[k], . . . , C_{Mf}[k] are processed through inverse filters, more specifically inverse Filter 1, inverse Filter 2, inverse Filter 3, . . . , inverse filter M of
Operation 306 (
In operation 306, the decoded spectral bands C_{1}[k], C_{2}[k], C_{3}[k], . . . , C_{M}[k] are then concatenated in a band concatenator 203 of
Operation 307 (FIG. 3)—Inverse transform
Finally, in operation 307, an inverse transform processor 204 (
Operation 308 (FIG. 3)—Calculating noise gains g_{1}[m] and g_{2}[m]
In
In
A plurality of different analysis procedures can be used by the calculator 205 (
Having processed through the transform processor 201 of
However, there are fundamental differences between TNS and the herein proposed interpolation. As a first difference between TNS and the herein disclosed technique, the objective and processing are different. In the herein disclosed technique, the objective is to impose, for the duration of a given window (for example window 1 of
Since the objective is to shape, through filtering, the quantization noise in each spectral band B_{m}[k], first concern is directed to the inverse Filters 1 to M of
If we consider then that the quantized transform coefficients Y_{f}[k] of the spectral band C_{mf}[k] are filtered as follows
C_{m}[k]=aC_{mf}[k]+bC_{m}[k−1] (1)
using filter parameters a and b. Equation (1) represents a firstorder recursive filter, applied to the transform coefficients of spectral band C_{mf}[k]. As stated above, it is possible to use other filter structures.
To understand the effect, in timedomain, of the filter of Equation (1) applied in the frequencydomain, use is made of a duality property of Fourier transforms which applies in particular to the MDCT. This duality property states that a convolution (or filtering) of a signal in one domain is equivalent to a multiplication (or actually, a modulation) of the signal in the other domain. For example, if the following filter is applied to a timedomain signal x[n]:
y[n]=ax[n]+by[n−1] (2)
where x[n] is the input of the filter and y[n] is the output of the filter, then this is equivalent to multiplying the transform of the input x[n], which can be noted X(e^{jθ}), by:
In Equation (3), θ is the normalized frequency (in radians per sample) and H(e^{jθ}) is the transfer function of the recursive filter of Equation (2). What is used is the value of H(e^{jθ}) at the beginning (θ=0) and end (θ=π) of the frequency domain scale. It is easy to show that, for Equation (3),
Equations (4) and (5) represent the initial and final values of the curve described by Equation (3). In between those two points, the curve will evolve smoothly between the initial and final values. For the Discrete Fourier Transform (DFT), which is a complexvalued transform, this curve will have complex values. But for other realvalued transforms such as the DCT and MDCT, this curve will exhibit real values only.
Now, because of the duality property of the Fourier transform, if the filtering of Equation (2) is applied in the frequencydomain as in Equation (1), then this will have the effect of multiplying the timedomain signal by a smooth envelope with initial and final values as in Equations (4) and (5). This timedomain envelope will have a shape that could look like the curve of
It is reminded that these timedomain envelopes of each spectral band are made equal, at the beginning and the end of a block of samples of the input signal x[n] (for example window 1 of
For the specific case of the frequencydomain filter of Equation (1), this implies the following constraints to determine parameters a and b in the filter equation from the noise gains g_{1}[m] and g_{2}[m]:
To simplify notation, let us set g_{1}=g_{1}[m] and g_{2}=g_{2}[m], and remember that this is only for spectral band B_{m}[k]. The following relations are obtained:
From Equations (8) and (9), it is straightforward, for each inverse Filter 1, 2, 3, . . . , M, to calculate the filter coefficients a and b as a function of g_{1 }and g_{2}. The following relations are obtained:
To summarize, coefficients a and b in Equations (10) and (11) are the coefficients to use in the frequencydomain filtering of Equation (1) in order to temporally shape the quantization noise in that m^{th }spectral band such that it follows the timedomain envelope shown in
This timedomain reversal of the TimeDomain Aliasing Cancellation (TDAC) is specific to the special case of the MDCT.
Now, the inverse filtering of Equation (1) shapes both the quantization noise and the signal itself. To ensure a reversible process, more specifically to ensure that y[n]=x[n] in
B_{mf}[k]=aB_{m}[k]−bB_{m}[k−1] (14)
In Equation (14), coefficients a and b calculated for the Filters 1, 2, 3, . . . , M are the same as in Equations (10) and (11), or Equations (12) and (13) for the special case of the MDCT. Equation (14) describes the inverse of the recursive filter of Equation (1). Again, if another type or structure of filter different from that of Equation (1) is used, then the inverse of this other type or structure of filter is used instead of that of Equation (14).
Another aspect is that the concept can be generalized to any shapes of quantization noise at points A and B of the windows of
Such flexibility allows the use of the frequencydomain noise shaping device 200 and method 300 for interpolating the spectral shape and timedomain envelope of quantization noise in a system in which the resolution of the shape of the spectral noise changes in time. For example, in a variable bit rate codec, there might be enough bits at some frames (point A or point B in
Illustrated in
Still referring to
As described hereinabove, the encoder 700 comprises an ACELP coder including an LPC quantizer which calculates, encodes and transmits the LPC coefficients from an LPC analysis. More specifically, referring to
As described hereinabove, the system 700 of
The bit multiplexer 713 receives as input the quantized and encoded spectral coefficients from processed spectrum quantizer 711, the quantized scale factors from quantizer 705, the quantized LPC coefficients from LPC quantizer 706 and the encoded excitation of the LPC filter from encoder 712 and produces in response to these encoded parameters a stream of bits for transmission or storage.
Illustrated in
The decoder 800 comprises a demultiplexer 801 receiving as input the stream of bits from bit multiplexer 713 (
The recovered quantized LPC coefficients (transformcoded window of the windowed audio signal) from demultiplexer 801 are supplied to a LPC decoder 804 to produce decoded LPC coefficients. The recovered encoded excitation of the LPC filter from demultiplexer 301 is supplied to and decoded by an ACELP excitation decoder 805. An ACELP synthesis filter 806 is responsive to the decoded LPC coefficients from decoder 804 and to the decoded excitation from decoder 805 to produce an ACELPdecoded audio signal.
The recovered quantized scale factors are supplied to and decoded by a scale factors decoder 803.
The recovered quantized and encoded spectral coefficients are supplied to a spectral coefficient decoder 802. Decoder 802 produces decoded spectral coefficients which are used as input by a FDNS processor 807. The operation of FDNS processor 807 is as described in
Finally, a windowing and overlap/add processor 811 combines the ACELPdecoded audio signal from the ACELP synthesis filter 806 with the transformdecoded audio signal from the IMDCT processor 810 to produce a synthesis audio signal.