Source: https://patents.justia.com/patent/9837087
Timestamp: 2018-04-25 02:34:15
Document Index: 462801523

Matched Legal Cases: ['Application No. 12305861', 'ART+1', 'ART+1', 'ART+2', 'ART+1', 'ART+2']

US Patent for Method and apparatus for encoding multi-channel HOA audio signals for noise reduction, and method and apparatus for decoding multi-channel HOA audio signals for noise reduction Patent (Patent # 9,837,087 issued December 5, 2017) - Justia Patents Search
Justia Patents US Patent for Method and apparatus for encoding multi-channel HOA audio signals for noise reduction, and method and apparatus for decoding multi-channel HOA audio signals for noise reduction Patent (Patent # 9,837,087)
Sep 26, 2016 - Dolby Labs
This application is continuation of the U.S. patent application Ser. No. 14/415,571, filed Jan. 16, 2015, now U.S. Pat. No. 9,460,728, which is a national application of the International Application No. PCT/EP2013/065032, filed Jul. 16, 2013, which claims priority to European Patent Application No. 12305861.2, filed Jul. 16, 2012, all of which are hereby incorporated by reference in their entirety.
The transmission or storage of such multi-channel audio signal representations usually demands for appropriate multi-channel compression techniques. Usually, a channel independent perceptual decoding is performed before finally matrixing the I decoded signals {circumflex over ({circumflex over (x)})}i(l), i=1, . . . , I, into J new signals {circumflex over (ŷ)}j(l), j=1, . . . , J. The term matrixing means adding or mixing the decoded signals {circumflex over ({circumflex over (x)})}i(l) in a weighted manner. Arranging all signals {circumflex over ({circumflex over (x)})}i(l), i=1, . . . , I, as well as all new signals {circumflex over (ŷ)}j(l), j=1, . . . , J in vectors according to
The particular individual loudspeaker set-up on which the matrix depends, and thus the matrix that is used for matrixing during the rendering, is usually not known at the perceptual coding stage.
with (•)T denoting transposition. The corresponding empirical correlation matrix is given by
Σx:=X XH, (3)
where (•)H denotes the joint complex conjugation and transposition.
ΣE=diag(σe12, . . . ,σeI2). (7)
σ e i 2 = 1 M ⁢ ∑ m = m START + 1 m START + M ⁢ ⁢  e i ⁡ ( m )  2 ( 8 )
SNR x = σ x i 2 σ e i 2 ⁢ ⁢ for ⁢ ⁢ all ⁢ ⁢ i = 1 , … ⁢ , I ⁢ ⁢ with ( 9 ) σ x i 2 := 1 M ⁢ ∑ m = m START + 1 m START + M ⁢ ⁢  x i ⁡ ( m )  2 . ( 10 )
From now on we consider the matrixing of the reconstructed signals into J new signals yj(m), j=1, . . . , J. Without introducing any coding error the sample matrix of the matrixed signals may be expressed by
Y=A X, (11)
Y: =[y(mSTART+1), . . . ,y(mSTART+M)] (12)
y(m): =[y1(m), . . . ,yJ(m)]T. (13)
n(m): =[n1(m) . . . nJ(m)]T (17)
ΣY=A ΣXAH. (18)
AH=[a1, . . . ,aJ] (20)
ΣN=A ΣEAH. (21)
SNR y j := σ y j 2 σ n j 2 , ( 23 )
SNR y j := a j H ⁢ Σ X ⁢ a j a j H ⁢ Σ E ⁢ a j . ( 24 )
diag(σx12, . . . ,σxI2)=SNRx·diag(σe12,σeI2) (27)
SNR y j = a j H ⁢ diag ⁡ ( σ x 1 2 , , σ x I 2 ) ⁢ a j a j H ⁢ Σ E ⁢ a j + a j H ⁢ Σ X , NG ⁢ a j a j H ⁢ Σ E ⁢ a j ( 28 ) SNR y j = SNR x ⁡ ( 1 + a j H ⁢ Σ X , NG ⁢ a j a j H ⁢ diag ⁡ ( σ x 1 2 , … ⁢ , σ x I 2 ) ⁢ a j ) . ( 29 )
SNRyj=SNRx for all j=1, . . . ,J,if ΣX,NG=0I×I (30)
Higher Order Ambisonics (HOA) is based on the description of a sound field within a compact area of interest, which is assumed to be free of sound sources. In that case the spatiotemporal behavior of the sound pressure p (t, x) at time t and position x=[r, θ, φ]T within the area of interest (in spherical coordinates) is physically fully determined by the homogeneous wave equation. It can be shown that the Fourier transform of the sound pressure with respect to time, i.e.,
where ω denotes the angular frequency (and t { } corresponds to ∫−∞∞p (t,x)e−ωtdt), may be expanded into the series of Spherical Harmonics (SHs) according to, [10]:
P ⁡ ( k ⁢ ⁢ c s , x ) = ∑ n = 0 ∞ ⁢ ⁢ ∑ m = - n n ⁢ ⁢ A n m ⁡ ( k ) ⁢ j n ⁡ ( kr ) ⁢ Y n m ⁡ ( θ , ϕ ) ( 32 )
the angular wave number. Further, jn(•) indicate the spherical Bessel functions of the first kind and order n and Ynm(•) denote the Spherical Harmonics (SH) of order n and degree m. The complete information about the sound field is actually contained within the sound field coefficients Anm(k).
D ⁡ ( k ⁢ ⁢ c s , Ω ) = ∑ n = 0 ∞ ⁢ ⁢ ∑ m = - n n ⁢ ⁢ B n m ⁡ ( k ) ⁢ Y n m ⁡ ( Ω ) , ( 33 )
with the source field or amplitude density [9] D(k cs, Ω) depending on angular wave number and angular direction Ω=[θ, φ]T. A source field can consist of far-field/near-field, discrete/continuous sources [1]. The source field coefficients Bnm are related to the sound field coefficients Anm by, [1]:
A n m = { 4 ⁢ π ⁢ ⁢ i n ⁢ B n m for ⁢ ⁢ the ⁢ ⁢ far ⁢ ⁢ field - ikh n ( 2 ) ⁡ ( kr s ) ⁢ B n m for ⁢ ⁢ the ⁢ ⁢ near ⁢ ⁢ field 1 ⁢ ⁢ 1 ⁢ We ⁢ ⁢ use ⁢ ⁢ positive ⁢ ⁢ frequencies ⁢ ⁢ ⁢ and ⁢ ⁢ the ⁢ ⁢ spherical ⁢ ⁢ Hankel ⁢ ⁢ function ⁢ ⁢ of ⁢ ⁢ second ⁢ ⁢ kind ⁢ ⁢ h n ( 2 ) ⁢ ⁢ ⁢ for ⁢ ⁢ incoming ⁢ ⁢ waves ⁢ ( related ⁢ ⁢ to ⁢ ⁢ ⅇ ⅈ ⁢ ⁢ kr ) . ( 34 )
where hn(2) is the spherical Hankel function of the second kind and rs is the source distance from the origin.
or by O2D=2N+1 for 2D only descriptions. The coefficients bnm comprise the Audio information of one time sample m for later reproduction by loudspeakers. They can be stored or transmitted and are thus subject of data rate compression. A single time sample m of coefficients can be represented by vector b (m) with O3D elements:
b(m):=[b00(m),b1−1(m),b10(m),b11(m),b1−2(m), . . . ,bNN(m)]T (37)
B: =[b(mSTART+1),b(mSTART+2), . . . ,b(mSTART+M)] (38)
Two dimensional representations of sound fields can be derived by an expansion with circular harmonics. This is can be seen as a special case of the general description presented above using a fixed inclination of
θ = π 2 ,
different weighting of coefficients and a reduced set to O2D coefficients (m=±n). Thus all of the following considerations also apply to 2D representations, the term sphere then needs to be substituted by the term circle.
The following describes a transform from HOA coefficient domain to a spatial, channel based, domain and vice versa. Equation (33) can be rewritten using time domain HOA coefficients for 1 discrete spatial sample positions Ωl=[θl, φl]T on the unit sphere:
d Ω l := ∑ n = 0 N ⁢ ∑ m = - n n ⁢ ⁢ b n m ⁢ Y n m ⁡ ( Ω l ) , ( 35 )
W=ΨiB, (36)
with W:=[w (mSTART+1), w (mSTART+2), . . . , w (mSTART+M)] and
w ⁡ ( m ) = [ d Ω 1 ⁡ ( m ) , … ⁢ , d Ω L sd ⁡ ( m ) ] T
representing a single time-sample of a Lsd multichannel signal, and matrix Ψi=[y1, . . . , yLsd]H with vectors y1=[Y00(Ωl), Y1−1(Ωl), . . . , YNN(Ωl)]T. If the spherical sample positions are selected very regular, a matrix exists with
ΨfΨi=I, (37)
where I is a O3D×O3D identity matrix. Then the corresponding transformation to equation (36) can be defined by:
B=ΨfW. (38)
Equation (38) transforms Lsd spherical signals into the coefficient domain and can be rewritten as a forward transform:
B=DSHT{W}, (39)
where DSHT{ } denotes the Discrete Spherical Harmonics Transform. The corresponding inverse transform, transforms O3D coefficient signals into the spatial domain to form Lsd channel based signals and equation (36) becomes:
W=iDSHT{B}. (40)
This definition of the Discrete Spherical Harmonics Transform is sufficient for the considerations regarding data rate compression of HOA data here because we start with coefficients B given and only the case B=DSHT {iDSHT{B}} is of interest. A more strict definition of the Discrete Spherical Harmonics Transform, is given within [2]. Suitable spherical sample positions for the DSHT and procedures to derive such positions can be reviewed in [3], [4], [6], [5]. Examples of sampling grids are shown in FIGS. 5A, 5B, 5C, and 5D.
In the following, rate compression of Higher Order Ambisonics coefficient data and noise unmasking is described. First, a test signal is defined to highlight some properties, which is used below.
Bg=y gT, (45)
with matrix Bg analogous to equation (38) and encoding vector y=[Y00*(Ωs1), Y1−1*(Ωs1), . . . , YNN*(Ωs1)]T composed of conjugate complex Spherical Harmonics evaluated at direction Ωs1=[θs1, φs1]T (if real valued SH are used the conjugation has no effect). The test signal Bg can be seen as the simplest case of an HOA signal. More complex signals consist of a superposition of many of such signals.
Ŵ=A {circumflex over (B)}, (47)
with decoding matrix AεL×O3D (and AH=[a1, . . . , aL]) and matrix ŴεL×M holding the M time samples of L speaker signals. This is analogous to (14). Applying all considerations described above, the SNR of speaker channel 1 can be described by (analogous to equation (29)):
SNR W l = SNR B g ⁡ ( 1 + a l H ⁢ ∑ B , NG ⁢ a l a l H ⁢ diag ⁢ ⁢ ( σ B 1 2 , … ⁢ , σ B O 3 ⁢ D 2 ) ⁢ a l ) , ( 48 )
ΣB=B BH. (49)
As the decoding matrix A should not be influenced, because it should be possible to decode to arbitrary speaker layouts, the matrix ΣB needs to become diagonal to obtain SNRwl=SNRBg. With equations (45) and (49), (B=Bg)
ΣB=y gH g yH=c yyH becomes non diagonal with constant scalar value c=gT g. Compared to SNRBg the signal to noise ratio at the speaker channels SNRwl decreases. But since neither the source signal g nor the speaker layout are usually known at the encoding stage, a direct lossy compression of coefficient channels can lead to uncontrollable unmasking effects especially for low data rates.
The current block of HOA coefficient data B is transformed into the spatial domain prior to compression using the Spherical Harmonics Transform as given in equation (36):
with inverse transform matrix Ψi related to the LSd≧O3D spatial sample positions, and spatial signal matrix WSHεLSd×M. These are subject to compression and decompression and quantization noise is added (analogous to equation (4)):
with coding noise component E according to equation (5). Again we assume a SNR, SNRSd that is constant for all spatial channels. The signal is transformed to the coefficient domain equation (42), using transform matrix Ψf, which has property (41): Ψf Ψi=I. The new block of coefficients {circumflex over (B)} becomes:
This signals are rendered to L speakers signals ŴεL×M, by applying decoding matrix AD: Ŵ=AD {circumflex over (B)}. This can be rewritten using (52) and A=ADΨf:
Ŵ=A ŴSd. (53)
SNR W l = SNR S d ⁡ ( 1 + a l H ⁢ ∑ W Sd , NG ⁢ a l a l H ⁢ diag ⁢ ⁢ ( σ S d 1 2 , … ⁢ , σ S d L Sd 2 ) ⁢ a l ) , ( 54 )
ΣWSd=c Ψiy yHΨiH, (56)
a l H ⁢ ∑ W Sd , NG ⁢ a l a l H ⁢ diag ⁢ ⁢ ( σ S d 1 2 , … ⁢ , σ S d L Sd 2 ) ⁢ a l
∑ l = 1 L Sd ⁢ ∑ j = 1 L Sd ⁢  ∑ W Sd l , j  - ∑ ( σ S d 1 2 , … ⁢ , σ S d L Sd 2 ) ( 57 )
 ∑ W Sd l , j 
Visualized, this process corresponds to a rotation of the spherical sampling grid of the DSHT in a way that a single spatial sample position matches the strongest source direction, as shown in FIGS. 4A and 4B. Using the simple test signal from equation (45) (B=Bg), it can be shown that the term WSd of equation (55) becomes a vector εLSd×1 with all elements close to zero except one. Consequently ΣWSd becomes near diagonal and the desired SNR SNRsd can be kept.
Details of the encoder and decoder processing building blocks pE and pD are shown in FIG. 6. Both blocks own the same codebook of spherical sampling position grids that are the basis for the DSHT. Initially, the number of coefficients O3D is used to select a basis grid in module pE with LSd=O3D positions, according to the common codebook. LSd must be transmitted to block pD for initialization to select the same basis sampling position grid as indicated in FIG. 3. The basis sampling grid is described by matrix DSHT=[Ω1, . . . , ΩLsd], where Ωl=[θl, φl]T defines a position on the unit sphere. As described above, FIGS. 5A, 5B, 5C, and 5D show examples of basic grids.
Input to the rotation finding block (building block ‘find best rotation’) 320 is the coefficient matrix B. The building block is responsible to rotate the basis sampling grid such that the value of eq. (57) is minimized. The rotation is represented by the ‘axis-angle’ representation and compressed axis ψrot and rotation angle φrot related to this rotation are output to this building block as side information SI. The rotation axis ψrot can be described by a unit vector from the origin to a position on the unit sphere. In spherical coordinates this can be articulated by two angles: ψrot=[θaxis, φaxis]T, with an implicit related radius of one which does not need to be transmitted The three angles θaxis, φaxis, φrot are quantized and entropy coded with a special escape pattern that signals the reuse of previously used values to create side information SI.
The building block ‘Build Ψi’ 330 decodes the rotation axis and angle to {circumflex over (ψ)}rot and {circumflex over (φ)}rot and applies this rotation to the basis sampling grid DSHT to derive the rotated grid DSHT=[{circumflex over (Ω)}1, . . . , {circumflex over (Ω)}Lsd]. It outputs an iDSHT matrix Ψi=[y1, . . . , yLsd] which is derived from vectors yl=[Y00({circumflex over (Ω)}l), Y1−1({circumflex over (Ω)}l), . . . , YNN({circumflex over (Ω)}l)]T.
The building block ‘Build Ψf’ 350 of the decoding processing block pD receives and decodes the rotation axis and angle to {circumflex over (ψ)}rot and {circumflex over (φ)}rot and applies this rotation to the basis sampling grid DSHT to derive the rotated grid DSHT=[{circumflex over (Ω)}1, . . . , {circumflex over (Ω)}Lsd]. The iDSHT matrix Ψi=[y1, . . . , yLsd] is derived with vectors yl=[Y00({circumflex over (Ω)}l), Y1−1({circumflex over (Ω)}l), . . . , YNN({circumflex over (Ω)}l)]T and the DSHT matrix Ψf=Ψi−1 is calculated on the decoding side.
A respective compression decoder building block comprises, in one embodiment, demultiplexer D1 for demultiplexing the bitstream S73 to LSd bitstreams and side information SI, and feeding the bitstreams to LSd mono decoders, decoding them to LSd spatial Audio channels with M samples to form block ŴSd(μ), and feeding ŴSd(μ) and SI to pD. In another embodiment, where the bitstream is not multiplexed, a compression decoder building block comprises a receiver 74 for receiving the bitstream and decoding it to a LSd multichannel signal WSd(μ), depacking SI and feeding ŴSd(μ) and SI to pD.
∑ l = 1 L Sd ⁢ ∑ j = 1 L Sd ⁢  ∑ W Sd l , j  - ∑ ( σ S d 1 2 , … ⁢ , σ S d L Sd 2 )
are the diagonal elements of ΣWSd, where ΣWSd=WSd WSdH and WSd is a number of audio channels by number of block processing samples matrix, and WSd is the result of the aDSHT.
In an embodiment shown in FIG. 8B, a method for decoding coded multi-channel HOA audio signals with reduced noise comprises steps of receiving 85 encoded multi-channel HOA audio signals and channel rotation information (within side information SI), decompressing 86 the received data, wherein perceptual decoding is used, spatially to decoding 87 each channel using an adaptive DSHT, wherein a DSHT 872 and a rotation 871 of a spatial sampling grid of the DSHT according to said rotation information are performed and wherein the perceptually decoded channels are recorrelated, and matrixing 88 the recorrelated perceptually decoded channels, wherein reproducible audio signals mapped to loudspeaker positions are obtained.
In one embodiment, the rotation information is a vector composed out of 3 angles: θaxis, θaxis, φrot, where θaxis, φaxis define the information for the rotation axis with an implicit radius of one in spherical coordinates, and φrot defines the rotation angle around this axis.
Comparison of aDSHT vs. KLT Tab. 1 provides a direct comparison between the aDSHT and the KLT. Although some similarities exist, the aDSHT provides significant advantages over the KLT.
Definition B is a N order HOA signal matrix, (N + 1)2 rows (coefficients), T columns (time samples); W is a spatial matrix with (N + 1)2 rows (channels), T columns (time samples)
Encoder, spatial Inverse aDSHT Karhunen Loève transform transform WSd = ΨiB Wk = KB Transform Matrix A spherical regular sampling grid Build covariance matrix: with (N + 1)2 spherical sample C = BBH positions known to encoder and Eigenwert decomposition: decoder is selected. This grid is C = KHΛK, rotated around axis ψrot and with Eigen values diagonal in rotation angle φrot, (which have Λ and related Eigen vectors been derived before (see arranged in KH with remark below). A Mode-matrix KKH = 1 like in any Ψf of that grid is created (i.e. orthogonal transform. spherical harmonics of these The transform matrix is positions): Ψi = Ψf−1 derived from the signal B for (Or more general Ψi = Ψf+ with every processing block. ΨfΨi = I when the number of spatial channels becomes bigger than (N + 1)2) The transform matrix is the inverse mode matrix of a rotated spherical grid. The rotation is signal driven and updated every processing block Side Info to transmit axis ψrot and rotation angle φrot for example coded as 3 values: θaxis, φaxis, φrot More than half of the elements of C (that is, ( N + 1 ) 4 + ( N + 1 ) 2 2 ⁢ ⁢ values ) ⁢ ⁢ or ⁢ ⁢ K (that is, (N + 1)4 values) Lossy The spatial signals are lossy The spatial signals are lossy decompressed coded, (coding noise Ecod). A coded (coding noise Êcod). A spatial signal block of T samples is arranges as block of T samples is arranges ŴSd as Ŵk Decoder, inverse {circumflex over (B)} = ΨfŴSd = B + ΨfEcod {circumflex over (B)}k = KŴk = B + KÊcod spatial transform
Remark In one embodiment, the grid is rotated such that a sampling position matches the strongest signal direction within B. An analysis of the covariance matrix can be used here, like it is usable for the KLT. In practice, since more simple and less computationally complex, signal tracking models can be used that also allow to adapt/modify the rotations smoothly from block to block, which avoids creation of blocking artifacts within the lossy (perceptual) coding blocks
1. A method for decoding encoded Higher Order Ambisonics (HOA) audio signals, the method comprising:
receiving the encoded HOA audio signals and rotation information;
decompressing the encoded HOA audio signals based on perceptual decoding to determine HOA representations corresponding to the encoded HOA audio signals;
determining a rotated transform based on a rotation of a spherical sample grid associated with the rotation information; and
2. The method according to claim 1, wherein the rotated transform is determined based on:
selecting a default spherical sample grid;
rotating, for a block of M time samples, the default spherical sample grid based on rotation information to determine a rotated spherical sample grid; and
determining a mode matrix with respect to the rotated spherical sample grid.
3. The method according to claim 1, wherein the rotation information corresponds to a three component rotation based on three angles: θaxis, φaxis, φrot, where θaxis, φaxis define the information for a rotation axis with an implicit radius of one in spherical coordinates and φrot defines a rotation angle around the rotation axis.
4. An apparatus for decoding encoded Higher Order Ambisonics (HOA) audio signals, the apparatus comprising:
a receiver for receiving the encoded HOA audio signals and rotation information;
decompress the encoded HOA audio signals based on perceptual decoding to determine HOA representations corresponding to the encoded HOA audio signals;
determine a rotated transform based on a rotation of a spherical sample grid associated with the rotation information; and
5. The apparatus according to claim 4, wherein the decoder is configured to determine the rotated transform based on a selection of a default spherical sample grid for the new transform; a rotation, for a block of M time samples, the default spherical sample grid according to said rotation information to determine a rotated spherical sample grid; and a determination of a mode matrix with respect to the rotated spherical sample grid.
6. The apparatus according to claim 4, wherein the rotation information corresponds to a three component rotation based on three angles: θaxis, φaxis, φrot, where θaxis, φaxis define the information for a rotation axis with an implicit radius of one in spherical coordinates and φrot defines a rotation angle around the rotation axis.
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Patent Publication Number: 20170061974
Inventors: Johannes Boehm (Gottingen), Sven Kordon (Wunstorf), Alexander Krueger (Hannover), Peter Jax (Hannover)
Application Number: 15/275,699
International Classification: G10L 19/012 (20130101); G10L 19/008 (20130101); G10L 19/02 (20130101); G10L 19/038 (20130101); H04S 3/02 (20060101);