# Patent

## Title
Ambience generation for stereo signals

## Abstract

Extracting an ambience signal from a plurality of audio signals includes transforming the signals into a short-time transform domain; computing an interchannel correlation measure in the short-time transform domain; and classifying portions of the signals that correspond to a low correlation measure as the ambience signal.

## Background
FIELD OF THE INVENTION
The present invention relates generally to audio signal processing. More specifically, ambience generation for stereo signals is disclosed.
BACKGROUND OF THE INVENTION
While surround multi-speaker systems are already popular in the home and desktop settings, the number of multi-channel audio recordings available is still limited. Recent movie soundtracks and some musical recordings are available in multi-channel format, but most music recordings are still mixed into two channels and playback of this material over a multi-channel system poses several questions. Sound engineers mix stereo recordings with a very particular set up in mind, which consists of a pair of loudspeakers placed symmetrically in front of the listener. Thus, listening to this kind of material over a multi-speaker system (e.g. 5.1 surround) raises the question as to what signal or signals should be sent to the surround and center channels. Unfortunately, the answer to this question depends strongly on individual preferences and no clear objective criteria exist.
There are two main approaches for mixing multi-channel audio. One is the direct/ambient approach, in which the main (e.g. instrument) signals are panned among the front channels in a frontally oriented fashion as is commonly done with stereo mixes, and “ambience” signals are sent to the rear (surround) channels. This mix creates the impression that the listener is in the audience, in front of the stage (best seat in the house). The second approach is the “in-the-band” approach, where the instrument and ambience signals are panned among all the loudspeakers, creating the impression that the listener is surrounded by the musicians. There is an ongoing debate about which approach is the best.
Whether an in-the-band or a direct/ambient approach is adopted, there is a need for better signal processing techniques to manipulate a stereo recording to extract the signals of ambience signals as well as the individual instruments. This is a very difficult task since no information about how the stereo mix was done is available in most cases.
The existing two-to-N channel up-mix algorithms can be classified in two broad classes: ambience generation techniques which attempt to extract and/or synthesize the ambience of the recording and deliver it to the surround channels (or simply enhance the natural ambience), and multichannel converters that derive additional channels for playback in situations when there are more loudspeakers than program channels. The ambience generation methods generally rely on combinations of the following methods:
1) Applying artificial reverberation to the stereo signal. The resulting impression is essentially of listening to the original recording in a virtual listening room. This artificial ambience information does not match the conditions in which the original recording was produced.
2) Computing the difference of the original left and right signals. This provides a monaural signal whose content includes the desired ambience information and excludes any primary signal panned in the center of the original stereo image. However, the resulting ambience signal also contains unwanted leakage from any primary signals not panned to the center. This leakage can be partially reduced by use of logic steering techniques.
3) Deriving a stereo ambience signal from a mono signal (pseudostereophony). Two weakly correlated signals can be obtained by applying a pair of all-pass filters to a single audio signal.
4) Applying a small delay (typically 5 to 20 ms) on the rear-channel signals to alleviate unwanted localization artifacts caused by any leakage of primary signals into the rear channels. This is an effective method for better preserving the frontal stereo image of the original recording, but it cannot correct the ambience information itself.
5) Deriving room responses corresponding to virtual microphone positions so as to synthesize reverberation signals that match the acoustics of the original venue. However, the application of this method is in principle restricted to live recordings for which detailed additional historical information is available on the original recording conditions and techniques. Also the method cannot reproduce other ambience components due to background noise in the original recording.
While the techniques described above have been of some use, there remains a need for better signal processing techniques for separating ambience for surround channels and developing better techniques for manipulating existing stereo recordings to be played on a multispeaker system remains an important problem.



BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will be readily understood by the following detailed description in conjunction with the accompanying drawings, wherein like reference numerals designate like structural elements, and in which:
FIG. 1 is a block diagram illustrating how upmixing is accomplished in one embodiment.
FIG. 2 is a block diagram illustrating the ambience signal extraction method.
FIG. 3A is a plot of this panning function as a function of α.
FIG. 3B is a plot of this panning function as a function of α.
FIG. 4 is a block diagram illustrating a two-to-three channel upmix system.
FIG. 5 is a diagram illustrating a coordinate convention for a typical stereo setup.
FIG. 6 is a diagram illustrating an up-mix technique based on a re-panning concept.
FIGS. 7A and 7B are plots of the desired gains for each output time frequency region as function of α assuming an angle θ=60°.
FIGS. 7C and 7D are plots of the modification functions.
FIGS. 8A and 8B are plots of the desired gains for θ=30°.
FIGS. 8C and 8D are plots of the corresponding modification functions for θ=30°.
FIG. 9 is a block diagram illustrating a system for unmixing a stereo signal to extract a signal panned in one direction.
FIG. 10 is a plot of the average energy from an energy histogram over a period of time as a function of Γ for a sample signal.
FIG. 11 is a diagram illustrating an up-mixing system used in one embodiment.
FIG. 12 is a diagram of a front channel upmix configuration.
FIG. 13 is a flowchart illustrating an embodiment of a process for extracting an ambience signal from a plurality of audio signals.



DETAILED DESCRIPTION
It should be appreciated that the present invention can be implemented in numerous ways, including as a process, an apparatus, a system, or a computer program product comprising a computer readable medium such as a computer readable storage medium or a computer network wherein program instructions are sent over optical or electronic communication links. It should be noted that the order of the steps of disclosed processes may be altered within the scope of the invention.
A detailed description of one or more preferred embodiments of the invention are provided below along with accompanying figures that illustrate by way of example the principles of the invention. While the invention is described in connection with such embodiments, it should be understood that the invention is not limited to any embodiment. On the contrary, the scope of the invention is limited only by the appended claims and the invention encompasses numerous alternatives, modifications and equivalents. For the purpose of example, numerous specific details are set forth in the following description in order to provide a thorough understanding of the present invention. The present invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the present invention is not unnecessarily obscured.
Stereo Recording Methods
It is possible to use certain knowledge about how audio engineers record and mix stereo recordings to derive information from the recordings. There are many ways of recording and mixing a musical performance, but we can roughly categorize them into two classes. In the first class, or studio recording, the different instruments are recorded in individual monaural signals and then mixed into two channels. The mix generally involves first panning in amplitude the monaural signals individually so as to position each instrument or set of instruments in a particular spatial region in front of the listener (in the space between the loudspeakers). Then, ambience is introduced by applying artificial stereo reverberation to the pre-mix. In general, the left and right impulse responses of the reverberation engine are mutually de-correlated to increase the impression of spaciousness. In this description, we refer to two channel signals as left and right for the purpose of convenience. It should be noted that the distinction is in some cases arbitrary and the two signals need not actually represent right and left stereo signals.
The second class, or live recording, is done when the number of instruments is large such as in a symphony orchestra or a jazz big band, and/or the performance is captured live. Generally, only a small number of spatially distributed microphones are used to capture all the instruments. For example, one common practice is to use two microphones spaced a few centimeters apart and placed in front of the stage, behind the conductor or at the audience level. In this case the different instruments are naturally panned in phase (time delay) and amplitude due to the spacing between the transducers. The ambience is naturally included in the recording as well, but it is possible that additional microphones placed some distance away from the stage towards the back of the venue are used to capture the ambience as perceived by the audience. These ambience signals could later be added to the stereo mix at different levels to increase the perceived distance from the stage. There are many variations to this recording technique, like using cardioid or figure-of-eight microphones etc., but the main idea is that the mix tries to reproduce the performance as perceived by a hypothetical listener in the audience.
In both cases the main drawback of the stereo down-mix is that the presentation of the material over only two loudspeakers imposes a constraint on the spatial region that the can be spanned by the individual sources, and the ambience can only create a frontal image or “wall” that does not really surround the listener as it happens during a live performance. Had the sound engineer had more channels to work with, the mix would have been different and the results could have been significantly improved in terms of creating a realistic reproduction of the original performance.
Upmixing
In one embodiment, the strategy to up-mix a stereo signal into a multi-channel signal is based on predicting or guessing the way in which the sound engineer would have proceeded if she or he were doing a multi-channel mix. For example, in the direct/ambient approach the ambience signals recorded at the back of the venue in the live recording could have been sent to the rear channels of the surround mix to achieve the envelopment of the listener in the sound field. Or in the case of studio mix, a multi-channel reverberation unit could have been used to create this effect by assigning different reverberation levels to the front and rear channels. Also, the availability of a center channel could have helped the engineer to create a more stable frontal image for off-the-axis listening by panning the instruments among three channels instead of two.
To apply this strategy, we first undo the stereo mix and then remix the signals into a multi-channel mix. Clearly, this is a very ill-conditioned problem given the lack of specific information about the stereo mix. However, the novel signal processing algorithms and techniques described below are useful to achieve this.
A series of techniques are disclosed for extracting and manipulating information in the stereo signals. Each signal in the stereo recording is analyzed by computing its Short-Time Fourier Transform (STFT) to obtain its time-frequency representation, and then comparing the two signals in this new domain using a variety of metrics. One or many mapping or transformation functions are then derived based on the particular metric and applied to modify the STFT's of the input signals. After the modification has been performed, the modified transforms are inverted to synthesize the new signals.
FIG. 1 is a block diagram illustrating how upmixing is accomplished in one embodiment. Left and right channel signals are processed by STFT blocks 102 and 104. Processor 106 unmixes the signals and then upmixes the signals into a greater number of channels than the two input channels. Four output channels are shown for the purpose of illustration. Inverse STFT blocks 112, 114, 116, and 118 convert the signal for each channel back to the time domain.
Ambience Information Extraction and Signal Synthesis
In this section we describe a technique to extract the ambience of a stereo recording. The method is based on the assumption that the reverberation component of the recording, which carries the ambience information, is uncorrelated if we compare the left and right channels. This assumption is in general valid for most stereo recordings. The studio mix is intentionally made in this way so as to increase the perceived spaciousness. Live mixes sample the sound field at different spatial locations, thus capturing partially correlated room responses. The technique essentially attempts to separate the time-frequency elements of the signals which are uncorrelated between left and right channels from the direct-path components (i.e. those that are maximally correlated), and generates two signals which contain most of the ambience information for each channel. As we describe later, these ambience signals are sent to the rear channels in the direct/ambient up-mix system.
Our ambience extraction method utilizes the concept that, in the short-time Fourier Transform (STFT) domain, the correlation between left and right channels across frequency bands will be high in time-frequency regions where the direct component is dominant, and low in regions dominated by the reverberation tails. Let us first denote the STFT's of the left sL(t) and right sR(t) stereo signals as SL(m,k) and SR(m,k) respectively, where m is the short-time index and k is the frequency index. We define the following short-time statistics

ΦLL(m,k)=ΣSL(n,k)·SL*(n,k),  (1a)

ΦRR(m,k)=ΣSR(n,k)·SR*(n,k),  (1b)

ΦLR(m,k)=ΣSL(n,k)·SR*(n,k),  (1c)

where the sum is carried over a given time interval n (to be defined later) and * denotes complex conjugation. Using these statistical quantities we define the inter-channel short-time coherence function as

Φ(m,k)=|ΦLR(m,k)|·[ΦLL(m,k)·ΦRR(m,k)]−1/2.  (2)

The coherence function Φ(m,k) is real and will have values close to one in time-frequency regions where the direct path is dominant, even if the signal is amplitude-panned to one side. In this respect, the coherence function is more useful than a correlation function. The coherence function will be close to zero in regions dominated by the reverberation tails, which are assumed to have low correlation between channels. In cases where the signal is panned in phase and amplitude, such as in the live recording technique, the coherence function will also be close to one in direct-path regions as long as the window duration of the STFT is longer than the time delay between microphones.
Audio signals are in general non-stationary. For this reason the short-time statistics and consequently the coherence function will change with time. To track the changes of the signal we introduce a forgetting factor λ in the computation of the cross-correlation functions, thus in practice the statistics in (1) are computed as:

Φij(m,k)=λΦij(m−1,k)+(1−λ)Si(m,k)·Sj*(m,k).  (3)

Given the properties of the coherence function (2), one way of extracting the ambience of the stereo recording would be to multiply the left and right channel STFTs by 1−Φ(m,k) and to reconstruct (by inverse STFT) the two time domain ambience signals aL(t) and aR(t) from these modified transforms. A more general form that we propose is to weigh the channel STFT's with a non-linear function of the short-time coherence, i.e.

AL(m,k)=SL(m,k)M[Φ(m,k)]  (4a)

AR(m,k)=SR(m,k)M[Φ(m,k)],  (4b)

where AL(m,k) and AR(m,k) are the modified, or ambience transforms. The behavior of the non-linear function M that we desire is one in which the low coherence values are not modified and high coherence values above some threshold are heavily attenuated to remove the direct path component. Additionally, the function should be smooth to avoid artifacts. One function that presents this behavior is the hyperbolic tangent, thus we define M as:

M[Φ(m,k)]=0.5(μmax−μmin)tan h{σπ(Φo−Φ(m,k))}+0.5(μmax+μmin)  (5)

where the parameters μmax and μmin define the range of the output, Φo is the threshold and σ controls the slope of the function. In general the value of μmax is set to one since we do not wish to enhance the non-coherent regions (though this could be useful in other contexts). The value of μmin determines the floor of the function and it is important that this parameter is set to a small value greater than zero to avoid spectral-subtraction-like artifacts.
FIG. 2 is a block diagram illustrating the ambience signal extraction method. The inputs to the system are the left and right channel signals of the stereo recording, which are first transformed into the short-time frequency domain by STFT blocks 202 and 204. The parameters of the STFT are the window length N, the transform size K and the stride length L. The coherence function is estimated in block 206 and mapped to generate the multiplication coefficients that modify the short-time transforms in block 208. The coefficients are applied in multipliers 210 and 212. After modification, the time domain ambience signals are synthesized by applying the inverse short-time transform (ISTFT) in blocks 214 and 216. Illustrated below are values of the different parameters used in one embodiment in the context of a 2-to-5 multi-channel system.
Panning Information Estimation
In this section we describe another metric used to compare the two stereo signals. This metric allows us to estimate the panning coefficients, via a panning index, of the different sources in the stereo mix. Let us start by defining our signal model. We assume that the stereo recording consists of multiple sources that are panned in amplitude. The stereo signal with Ns amplitude-panned sources can be written as

sL(t)=Σi(1−αi)si(t) and sR(t)=Σiαi(t), for i=1, . . . , Ns.  (6)

where αi are the panning coefficients. Since the time domain signals corresponding to the sources overlap in amplitude, it is very difficult (if not impossible) to determine which portions of the signal correspond to a given source, not to mention the difficulty in estimating the corresponding panning coefficients. However, if we transform the signals using the STFT, we can look at the signals in different frequencies at different instants in time thus making the task of estimating the panning coefficients less difficult.
Again, the channel signals are compared in the STFT domain as in the method described above for ambience extraction, but now using an instantaneous correlation, or similarity measure. The proposed short-time similarity can be written as

Ψ(m,k)=2|SL(m,k)·SR*(m,k)|[|SL(m,k)|2+|SR(m,k)|2]−1,  (7)

we also define two partial similarity functions that will become useful later on:

ΨL(m,k)=|SL(m,k)·SR*(m,k)|·|SL(m,k)|−2  (7a)

ΨR(m,k)=|SR(m,k)·SL*(m,k)|·|SR(m,k)|−2.  (7b)

The similarity in (7) has the following important properties. If we assume that only one amplitude-panned source is present, then the function will have a value proportional to the panning coefficient at those time/frequency regions where the source has some energy, i.e.

ω(m,k)=2|αS(m,k)·(1−α)S*(m,k)|[|αS(m,k)|2+|(1−α)S(m,k)|2]−1,

=2(α−α2)(α2+(1−α)2)−1.

If the source is center-panned (α=0.5), then the function will attain its maximum value of one, and if the source is panned completely to one side, the function will attain its minimum value of zero. In other words, the function is bounded. Given its properties, this function allows us to identify and separate time-frequency regions with similar panning coefficients. For example, by segregating time-frequency bins with a given similarity value we can generate a new short-time transform, which upon reconstruction will produce a time domain signal with an individual source (if only one source was panned in that location).
FIG. 3A is a plot of this panning function as a function of α. Notice that given the quadratic dependence on α, the function Ψ(m,k) is multi-valued and symmetrical about 0.5. That is, if a source is panned say at α=0.2, then the similarity function will have a value of Ψ=0.47, but a source panned at α=0.8 will have the same similarity value.
While this ambiguity might appear to be a disadvantage for source localization and segregation, it can easily be resolved using the difference between the partial similarity measures in (7). The difference is computed simply as

D(m,k)=ΨL(m,k)−ΨR(m,k),  (8)

and we notice that time-frequency regions with positive values of D(m,k) correspond to signals panned to the left (i.e. α<0.5), and negative values correspond to signals panned to the right (i.e. α>0.5). Regions with zero value correspond to non-overlapping regions of signals panned to the center. Thus we can define an ambiguity-resolving function as

D′(m,k)=1 if D(m,k)>0 for all m and k  (9)

and

D′(m,k)=−1 if D(m,k)<=0 for all m and k. 

Shifting and multiplying the similarity function by D′(m,k) we obtain a new metric, which is anti-symmetrical, still bounded but whose values now vary from one to minus one as a function of the panning coefficient, i.e.

Γ(m,k)=[1−Ψ(m,k)]·D′(m,k),  (10)

FIG. 3B is a plot of this panning function as a function of α. In the following sections we describe the application of the short-time similarity and panning index to up-mix (re-panning), un-mix (separation) and source identification (localization). Notice that given a panning index we can obtain the corresponding panning coefficient given the one-to-one correspondence of the functions.
Two-Channel to N-Channel Up-Mix
Here we describe the application of the panning index to the problem of up-mixing a stereo signal composed of amplitude-panned sources, into an N-channel signal. We focus on the particular case of two-to-three channel up-mix for illustration purposes, with the understanding that the method can easily be extended to more than three channels. The two-to-three channel up-mix case is also relevant to the design example of the two-to-five channel system described below.
In a stereo mix it is common that one featured vocalist or soloist is panned to the center. The intention of the sound engineer doing the mix is to create the auditory impression that the soloist is in the center of the stage. However, in a two-loudspeaker reproduction set up, the listener needs to be positioned exactly between the loudspeakers (sweet spot) to perceive the intended auditory image. If the listener moves closer to one of the loudspeakers, the percept is destroyed due to the precedence effect, and the image collapses towards the direction of the loudspeaker. For this reason (among others) a center channel containing the dialogue is used in movie theatres, so that the audience sitting towards either side of the room can still associate the dialogue with the image on the screen. In fact most of the popular home multi-channel formats like 5.1 Surround now include a center channel to deal with this problem. If the sound engineer had had the option to use a center channel, he or she would have probably panned (or sent) the soloist or dialogue exclusively to this channel. Moreover, not only the center-panned signal collapses for off-axis listeners. Sources panned primarily toward on side (far from the listener) might appear to be panned toward the opposite side (closer to the listener). The sound engineer could have also avoided this by panning among the three channels, for example by panning between center and left-front channels all the sources with spatial locations on the left hemisphere, and panning between center and right-front channels all sources with locations toward the right.
To re-pan or up-mix a stereo recording among three channels we first generate two new signal pairs from the stereo signal. FIG. 4 is a block diagram illustrating a two-to-three channel upmix system. The first pair, sLF(t) and sLC(t), is obtained by identifying and extracting the time-frequency regions corresponding to signals panned to the left (α<0.5) and modifying their amplitudes according to a mapping function ML that depends on the location of the loudspeakers. The mapping function should guarantee that the perceived location of the sources is preserved when the pair is played over the left and center loudspeakers. The second pair, sRC(t) and sRF(t), is obtained in the same way for the sources panned to the right. The center channel is obtained by adding the signals sLC(t) and sRC(t). In this way, sources originally panned to the left will have components only in the sLF(t) and sC(t) channels and sources originally panned to the right will have components only in the sC(t) and sRF(t) channels, thus creating a more stable image for off-axis listening. All sources panned to the center will be sent exclusively to the sC(t) channel as desired. The main challenge is to derive the mapping functions ML and MR such that a listener at the sweet spot will not perceive the difference between stereo and three-channel playback. In the next sections we derive these functions based on the theory of localization of amplitude panned sources.
FIG. 5 is a diagram illustrating a coordinate convention for a typical stereo setup. The perceived location of a “virtual” source s=[x y]T is determined by the panning gains gL=(1−α) and gR=α, and the position of the loudspeakers relative to the listener, which are defined by vectors sL=[xL yL]T and sR=[xR yR]T. FIG. 6 is a diagram illustrating a coordinate convention for a typical stereo setup. At low frequencies (f<700 Hz) the perceived location is obtained by vector addition as [6]:

s=βS·g 

where

S=[sLsR]T 

and

g=[gLgR]T 

The scalar β=(gTu)−1 with u=[1 1]T, is introduced for normalization purposes and it is generally assumed to be unity for a stereo recording, i.e. gL=1−gR. At high frequencies (f>700 Hz) the apparent or perceived location of the source is determined by adding the intensity vectors generated by each loudspeaker (as opposed to amplitude vectors). The intensity vector is computed as

s=γS·q 

where

q=[gL2gR2]T 

and the scalar γ=(qTu)−1 is introduced for power normalization purposes. Notice that there is a discrepancy in the perceived location in different frequency ranges.
FIG. 6 is a diagram illustrating an up-mix technique based on a re-panning concept. The right loudspeaker is moved to the center location sc. In order to preserve the apparent location of the virtual source, i.e. s=s′, the new panning coefficients g′ need to be computed. If we write the new virtual source position at low frequencies, as

s′=S′·g′

where

S′=[sLsc]T 

and

g′=[gL′gLC]T,

then the new panning coefficients are easily found by solving the following equation:

S·g=S′·g′. 

If the angle between loudspeakers is not zero, then the solution to this equation exists and the new panning coefficients are found as

g′=(S′)−1S·g. 

Notice that these gains do not necessarily add to one, thus a normalization factor β=(g′Tu)−1 needs to be introduced. Similarly, at high frequencies we obtain

q′=(S′)−1S·q, 

where

q′=[gL′2gLC2]T,

and the power normalization factor is computed as γ′=(q′Tu)−1.
The re-panning algorithm then consists of computing the desired gains and modifying the original signals accordingly. For sources panned to the right, the same re-panning strategy applies, where the loudspeaker on the left is moved to the center.
In practice we do not have knowledge of the location (or panning coefficients) of the different sources in a stereo recording. Thus, the re-panning procedure needs to be applied blindly for all possible source locations. This is accomplished by identifying time-frequency bins that correspond to a given location by using the panning index Γ(m,k), and then modifying their amplitudes according to a mapping function derived from the re-panning technique described in the previous section.
We identify four time-frequency regions that, after modification, will be used to generate the four output signals sLF(t), sLC(t), sRC(t) and sRF(t) as shown in FIG. 4. Let us define two short-time functions ΓL(m,k) and ΓR(m,k) as

ΓL(m,k)=1 for Γ(m,k)<0, and ΓL(m,k)=0 for Γ(m,k)>=0

ΓR(m,k)=1 for Γ(m,k)>=0, and ΓR(m,k)=0 for Γ(m,k)<0,

The four regions are then defined as:

SLL(m,k)=SL(m,k)ΓL(m,k)

SLR(m,k)=SR(m,k)ΓL(m,k)

SRL(m,k)=SL(m,k)ΓR(m,k)

SRR(m,k)=SR(m,k)FR(m,k),

where SL(m,k) and SR(m,k) are the STFT's of the left and right input signals, L and R respectively. The regions SLL and SLR contain the contributions to the left and right channels of the left-panned signals respectively, and the regions SRR and SRL contain the contributions to the right and left channels of the right-panned signals respectively. Each region is multiplied by a modification function M and the output signals are generated by computing the inverse STFT's of these modified regions as:

sLF(t)=ISTFT{SLL(m,k)MLF(m,k)}

sLC(t)=ISTFT{SLR(m,k)MLC(m,k)}

sRC(t)=ISTFT{SRL(m,k)MRC(m,k)}

sRF(t)=ISTFT{SRR(m,k)MRF(m,k)}

Thus the modification function in FIG. 4 are such that ML is equal to ΓL(m,k)MLF(m,k) for the left input signals and ΓL(m,k)MLC(m,k) for the right input signal, and similarly for MR. To find the modification functions, we first find the desired gains for all possible input panning coefficients as described above. FIGS. 7A and 7B are plots of the desired gains for each output time frequency region as function of α assuming an angle θ=60°.
The modification functions are simply obtained by computing the ratio between the desired gains and the input gains. FIGS. 7C and 7D are plots of the modification functions. While a value of θ=60° is typical, it is likely that some listener will prefer different setups and the modification functions will greatly depend on this. FIGS. 8A and 8B are plots of the desired gains for θ=30°. FIGS. 8C and 8D are plots of the corresponding modification functions for θ=30°.
Source Un-Mix
Here we describe a method for extracting one or more audio streams from a two-channel signal by selecting directions in the stereo image. As we discussed in previous sections, the panning index in (10) can be used to estimate the panning coefficient of an amplitude-panned signal. If multiple panned signals are present in the mix and if we assume that the signals do not overlap significantly in the time-frequency domain, then the Γ(m,k) will have different values in different time-frequency regions corresponding to the panning coefficients of the signals that dominate those regions. Thus, the signals can be separated by grouping the time-frequency regions where Γ(m,k) has a given value and using these regions to synthesize time domain signals.
FIG. 9 is a block diagram illustrating a system for unmixing a stereo signal to extract a signal panned in one direction. For example, to extract the center-panned signal(s) we find all time-frequency regions for which the panning metric is zero and define a function Θ(m,k) that is one for all Γ(m,k)=0, and zero otherwise. We can then synthesize a time domain function by multiplying SL(m,k) and SR(m,k) by Θ(m,k) and applying the ISTFT. The same procedure can be applied to signals panned to other directions.
To avoid artifacts due to abrupt transitions and to account for possible overlap, instead of using a function Θ(m,k) like we described above, we apply a narrow window centered at the panning index value corresponding to the desired panning coefficient. The width of the window is determined based on the desired trade-off between separation and distortion (a wider window will produce smoother transitions but will allow signal components panned near zero to pass).
To illustrate the operation of the un-mixing algorithm we performed the following simulation. We generated a stereo mix by amplitude-panning three sources, a speech signal s1(t), an acoustic guitar s2(t) and a trumpet s3(t) with the following weights:

sL(t)=0.5s1(t)+0.7s2(t)+0.1s3(t) and SR(t)=0.5s1(t)+0.3s2(t)+0.9s3(t).

We applied a window centered at Γ=0 to extract the center-panned signal, in this case the speech signal, and two windows at Γ=−0.8 and Γ=0.27 (corresponding to α=0.1 and α=0.3) to extract the horn and guitar signals respectively. In this case we know the panning coefficients of the signals that we wish to separate. This scenario corresponds to applications where we wish to extract or separate a signal at a given location. Other applications that require identification of prominent sources are discussed in the next section.
Identification of Prominent Sources
In this section we describe a method for identifying amplitude-panned sources in a stereo mix. In one embodiment, the process is to compute the short-time panning index Γ(m,k) and produce an energy histogram by integrating the energy in time-frequency regions with the same (or similar) panning index value. This can be done in running time to detect the presence of a panned signal at a given time interval, or as an average over the duration of the signal. FIG. 10 is a plot of the average energy from an energy histogram over a period of time as a function of Γ for a sample signal. The histogram was computed by integrating the energy in both stereo signals for each panning index value from −1 to 1 in 0.01 increments. Notice how the plot shows three very strong peaks at panning index values of Γ=−0.8, 0 and 0.275, which correspond to values of α=0.1, 0.5 and 0.7 respectively.
Once the prominent sources are identified automatically from the peaks in the energy histogram, the techniques described above can be used extract and synthesize signals that consist primarily of the prominent sources.
Multi-Channel Up-Mixing System
In this section we describe the application of the ambience extraction and the source up-mixing algorithms to the design of a direct/ambient stereo-to-five channel up-mix system. The idea is to extract the ambience signals from the stereo recording using the ambience extraction technique described above and use them to create the rear or surround signals. Several alternatives for deriving the front channels are described based on applying a combination of the panning techniques described above.
Surround Channels
FIG. 11 is a diagram illustrating an up-mixing system used in one embodiment. The surround tracks are generated by first extracting the ambience signals as shown in FIG. 2. Two filters GL(z) and GR(z) are then used to filter the ambience signals. These filters are all-pass filters that introduce only phase distortion. The reason for doing this is that we are extracting the ambience from the front channels, thus the surround channels will be correlated with the front channels. This correlation might create undesired phantom images to the sides of the listener.
In one embodiment, the all-pass filters were designed in the time domain following the pseudo-stereophony ideas of Schroeder as described in J. Blauert, “Spatial Hearing.” Hirzel Verlag, Stuttgart, 1974 and implemented in the frequency domain. The left and right filters are different, having complementary group delays. This difference has the effect of increasing the de-correlation between the rear channels. However, this is not essential and the same filter can be applied to both rear channels. Preferably, the phase distortion at low frequencies is kept to a small level to prevent bass thinning.
The rear signals that we are creating are simulating the tracks that were recorded with the rear microphones that collect the ambience at the back of the venue. To further decrease the correlation and to simulate rooms of different sizes, the rear channels are delayed by some amount Δ.
Front Channels
In some embodiments, the front channels are generated with a two-to-three channel up-mix system based on the techniques described above. Many alternatives exist, and we consider one simple alternative as follows.
The simplest configuration to generate the front channels is to derive the center channel using the techniques described above to extract the center-panned signal and sending the residual signals to the left and right channels. FIG. 12 is a diagram of such a front channel upmix configuration. Processing block 1201 represents a short-time modification function that depends on the non-linear mapping of the panning index. The signal reconstruction using the inverse STFT is not shown. This system is capable of producing a stable center channel for off-axis listening, and it preserves the stereo image of the original recording when the listener is at the sweet spot. However, side-panned sources will still collapse if the listener moves off-axis.
System Implementation
The system has been tested with a variety of audio material. The best performance so far has been obtained with the following parameter values:















Parameter
Value
Description




















N
1024
STFT window size



K
2048
STFT transform size



L
256
STFT stride size



λ
0.90
Cross-correlation forgetting factor



σ
8.00
Slope of mapping functions M



Φo
0.15
Breakpoint of mapping function M



μmin
0.05
Floor of mapping functions M



Δ
256
Rear channel delay



Np
15
Number of complex conjugate poles of G(z)










These parameters assume that the audio is sampled at 44.1 kHz. The configuration shown in FIG. 4 is used for the front channel up-mix.
In general, the ambience can be effectively extracted using the methods described above. The ambience signals contain a very small direct path component at a level of around −25 dB. This residual is difficult to remove without damaging the rest of the signal. However, increasing the aggressiveness of the mapping function (increasing σ and decreasing Φo and μmin) can eliminate the direct path component but at the cost of some signal distortion. If μmin is set to zero, spectral-subtraction-like artifacts tend to become apparent.
The parameters above represent a good compromise. While distortion is audible if the rear signals are played individually, the simultaneous playback of the four signals masks the distortion and creates the desired envelopment in the sound field with very high fidelity.
FIG. 13 is a flowchart illustrating an embodiment of a process for extracting an ambience signal from a plurality of audio signals. In the example shown, at 1302, the signals are transformed into a short-time transform domain. At 1304, an interchannel correlation measure is computed in the short-time transform domain. At 1306, an ambience signal is extracted at least in part by classifying portions of the signals that correspond to a low correlation measure as the ambience signal.
Although the foregoing invention has been described in some detail for purposes of clarity of understanding, it will be apparent that certain changes and modifications may be practiced within the scope of the appended claims. It should be noted that there are many alternative ways of implementing both the process and apparatus of the present invention. Accordingly, the present embodiments are to be considered as illustrative and not restrictive, and the invention is not to be limited to the details given herein, but may be modified within the scope and equivalents of the appended claims.

## Claims

1. A method of extracting an ambience signal from an audio signal having at least two channels, the method comprising:
transforming the channel signals into a short-time transform domain;
computing an interchannel correlation measure in the short-time transform domain;
identifying an ambient portion of the signal as having a low correlation measure and a non-ambient portion as having a higher correlation measure; and
attenuating the non-ambient portions of the signal to a greater degree than the ambient portions of the signal.

2. A method of extracting an ambience signal as recited in claim 1 wherein the correlation measure is based on the interchannel short-time coherence.

3. A method of extracting an ambience signal as recited in claim 1 wherein the correlation measure is based on a non-linear mapping of the interchannel short-time coherence.

4. A method of extracting an ambience signal as recited in claim 1 wherein the correlation measure includes a forgetting factor.

5. A method of extracting an ambience signal as recited in claim 1 wherein the channel signals are left and right stereo signals.

6. A method of extracting an ambience signal as recited in claim 1 wherein the short-time transform is the Fourier transform and the short-time domain is the short-time frequency domain.

7. A method of extracting an ambience signal as recited in claim 1; wherein the short-time transform is a wavelet transform.

8. A method of extracting an ambience signal as recited in claim 1 wherein the short-time transform is computed using a gammatone filter bank.

9. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal does not contain a large magnitude direct path component.

10. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal is output to a loudspeaker.

11. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal comprises two different additional sound channels.

12. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal is generated by a system wherein portions of the signal that correspond to a low coherence measure are not modified and portions of the signal that correspond to a high coherence measure above some threshold are heavily attenuated.

13. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal is a signal in which an ambience component of the signal predominates other components of the signal.

14. A method of extracting an ambience signal as recited in claim 1 wherein the portions of the signal that correspond to a high correlation measure are attenuated more than the portions of the signals that correspond to a low correlation measure.

15. A method of extracting an ambience signal as recited in claim 1 further including not attenuating portions of the signal that correspond to a low correlation measure.

16. A method of extracting an ambience signal as recited in claim 1 wherein the ambience signal is provided as at least one channel that is distinct from channels associated with the audio signal.

17. A system for extracting an ambience signal from an audio signal having at least two channels, the method comprising:
a processor configured to:
transform the channel signals into a short-time transform domain;
compute an interchannel correlation measure in the short-time transform domain;
identify an ambient portion of the signal as having a low correlation measure and a non-ambient portion as having a higher correlation measure; and
attenuate the non-ambient portions of the signal to a greater degree than the ambient portions of the signal.

18. A system for extracting an ambience signal as recited in claim 17 wherein the correlation measure is based on the interchannel short-time coherence.

19. A system for extracting an ambience signal as recited in claim 17 wherein the correlation measure is based on a non-linear mapping of the interchannel short-time coherence.

20. A system for extracting an ambience signal as recited in claim 17 wherein the correlation measure includes a forgetting factor.

21. A system for extracting an ambience signal as recited in claim 17 wherein the channel signals are left and right stereo signals.

22. A system for extracting an ambience signal as recited in claim 17 wherein the short-time transform is the Fourier transform and the short-time domain is the short-time frequency domain.

23. A system for extracting an ambience signal as recited in claim 17 wherein the short-time transform is a wavelet transform.

24. A system for extracting an ambience signal as recited in claim 17 wherein the short-time transform is computed using a gammatone filter bank.

25. A system for extracting an ambience signal as recited in claim 17 wherein the ambience signal does not contain a large magnitude direct path component.

26. A system for extracting an ambience signal as recited in claim 18 wherein the ambience signal is output to a loudspeaker.

27. A system for extracting an ambience signal as recited in claim 17 wherein the ambience signal comprises two different additional sound channels.

28. A system for extracting an ambience signal as recited in claim 17 wherein the ambience signal is generated by a system wherein portions of the signal that correspond to a low coherence measure are not modified and portions of the signal that correspond to a high coherence measure above some threshold are heavily attenuated.

29. A computer program product for extracting an ambience signal from an audio signal having at least two channels, the computer program product being embodied in a computer readable medium and comprising computer instructions for:
transforming the signals into a short-time transform domain;
computing an interchannel correlation measure in the short-time transform domain;
identifying an ambient portion of the signal as having a low correlation measure and a non-ambient portion as having a higher correlation measure; and
attenuating the non-ambient portions of the signal to a greater degree than the ambient portions of the signal.

30. A computer program product for extracting an ambience signal as recited in claim 29 wherein the correlation measure is based on the interchannel short-time coherence.

31. A computer program product for extracting an ambience signal as recited in claim 29 wherein the correlation measure is based on a non-linear mapping of the interchannel short-time coherence.

32. A computer program product for extracting an ambience signal as recited in claim 29 wherein the correlation measure includes a forgetting factor.

33. A computer program product for extracting an ambience signal as recited in claim 29 wherein the channel signals are left and right stereo signals.

34. A computer program product for extracting an ambience signal as recited in claim 29 wherein the short-time transform is the Fourier transform and the short-time domain is the short-time frequency domain.

35. A computer program product for extracting an ambience signal as recited in claim 29 wherein the short-time transform is a wavelet transform.

36. A computer program product for extracting an ambience signal as recited in claim 29 wherein the short-time transform is computed using a gammatone filter bank.

37. A computer program product for extracting an ambience signal as recited in claim 29 wherein the ambience signal does not contain a large magnitude direct path component.

38. A computer program product for extracting an ambience signal as recited in claim 29 wherein the ambience signal is output to a loudspeaker.

39. A computer program product for extracting an ambience signal as recited in claim 29 wherein the ambience signal comprises two different additional sound channels.

40. A computer program product for extracting an ambience signal as recited in claim 29 wherein portions of the signal that correspond to a low coherence measure are not modified and portions of the signal that correspond to a high coherence measure above some threshold are heavily attenuated.