Systems and methods for reconstructing decomposed audio signals

Information

  • Patent Grant
  • 8934641
  • Patent Number
    8,934,641
  • Date Filed
    Wednesday, December 31, 2008
    15 years ago
  • Date Issued
    Tuesday, January 13, 2015
    9 years ago
Abstract
Systems and methods for reconstructing decomposed audio signals are presented. In exemplary embodiments, a decomposed audio signal is received. The decomposed audio signal may include a plurality of frequency sub-band signals having successively shifted group delays as a function of frequency from a filter bank. The plurality of frequency sub-band signals may then be grouped into two or more groups. A delay function may be applied to at least one of the two or more groups. Subsequently, the groups may be combined to reconstruct the audio signal, which may be outputted accordingly.
Description
BACKGROUND OF THE INVENTION

1. Field of the Invention


The present invention relates generally to audio processing. More specifically, the present invention relates to reconstructing decomposed audio signals.


2. Related Art


Presently, filter banks are commonly used in signal processing to decompose signals into sub-components, such as frequency subcomponents. The sub-components may be separately modified and then be reconstructed as a modified signal. Due to a cascaded nature of the filter bank, the sub-components of the signal may have successive lags. In order to realign the sub-components for reconstruction, delays may be applied to each sub-component. As such, the sub-components may be aligned with a sub-component having the greatest lag. Unfortunately, this process introduces latency between the modified signal and the original signal that is, at a minimum, equal to that greatest lag.


In real-time applications, like telecommunications for example, excessive latency may unacceptably hinder performance. Standards, such as those specified by the 3rd Generation Partner Project (3GPP), require latency below a certain level. In an effort to reduce latency, techniques have been developed at the cost of performance by prior art systems.


SUMMARY OF THE INVENTION

Embodiments of the present invention provide systems and methods for reconstructing decomposed audio signals. In exemplary embodiments, a decomposed audio signal is received from a filter bank. The decomposed audio signal may comprise a plurality of frequency sub-band signals having successively shifted group delays as a function of frequency. The plurality of frequency sub-band signals may be grouped into two or more groups. According to exemplary embodiments, the two or more groups may not overlap.


A delay function may be applied to at least one of the two or more groups. In exemplary embodiments, applying the delay function may realign the group delays of the frequency sub-band signals in at least one of the two or more groups. The delay function, in some embodiments, may be based, at least in part, on a psychoacoustic model. Furthermore, the delay function may be defined using a delay table.


The groups may then be combined to reconstruct the audio signal. In some embodiments, one or more of a phase or amplitude of each of the plurality of frequency sub-band signals may be adjusted. The combining may comprise summing the two or more groups. Finally, the audio signal may be outputted.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is an exemplary block diagram of a system employing embodiments of the present invention.



FIG. 2 illustrates an exemplary reconstruction module in detail.



FIG. 3 is a diagram illustrating signal flow within the reconstruction module in accordance with exemplary embodiments.



FIG. 4 displays an exemplary delay function.



FIG. 5 presents exemplary characteristics of a reconstructed audio signal.



FIG. 6 is a flowchart of an exemplary method for reconstructing a decomposed audio signal.





DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Embodiments of the present invention provide systems and methods for reconstructing a decomposed audio signal. Particularly, these systems and methods reduce latency while substantially preserving performance. In exemplary embodiments, sub-components of a signal received from a filter bank are disposed into groups and delayed in a discontinuous manner, group by group, prior to reconstruction.


Referring to FIG. 1, an exemplary system 100 in which embodiments of the present invention may be practiced is shown. The system 100 may be any device, such as, but not limited to, a cellular phone, hearing aid, speakerphone, telephone, computer, or any other device capable of processing audio signals. The system 100 may also represent an audio path of any of these devices.


In exemplary embodiments, the system 100 comprises an audio processing engine 102, an audio source 104, a conditioning module 106, and an audio sink 108. Further components not related to reconstruction of the audio signal may be provided in the system 100. Additionally, while the system 100 describes a logical progression of data from each component of FIG. 1 to the next, alternative embodiments may comprise the various components of the system 100 coupled via one or more buses or other elements.


The exemplary audio processing engine 102 processes the input (audio) signals received from the audio source 104. In one embodiment, the audio processing engine 102 comprises software stored on a device which is operated upon by a general processor. The audio processing engine 102, in various embodiments, comprises an analysis filter bank module 110, a modification module 112, and a reconstruction module 114. It should be noted that more, less, or functionally equivalent modules may be provided in the audio processing engine 102. For example, one or more the modules 110-114 may be combined into few modules and still provide the same functionality.


The audio source 104 comprises any device which receives input (audio) signals. In some embodiments, the audio source 104 is configured to receive analog audio signals. In one example, the audio source 104 is a microphone coupled to an analog-to-digital (A/D) converter. The microphone is configured to receive analog audio signals while the A/D converter samples the analog audio signals to convert the analog audio signals into digital audio signals suitable for further processing. In other examples, the audio source 104 is configured to receive analog audio signals while the conditioning module 106 comprises the A/D converter. In alternative embodiments, the audio source 104 is configured to receive digital audio signals. For example, the audio source 104 is a disk device capable of reading audio signal data stored on a hard disk or other forms of media. Further embodiments may utilize other forms of audio signal sensing/capturing devices.


The exemplary conditioning module 106 pre-processes the input signal (i.e., any processing that does not require decomposition of the input signal). In one embodiment, the conditioning module 106 comprises an auto-gain control. The conditioning module 106 may also perform error correction and noise filtering. The conditioning module 106 may comprise other components and functions for pre-processing the audio signal.


The analysis filter bank module 110 decomposes the received input signal into a plurality of sub-components or sub-band signals. In exemplary embodiments, each sub-band signal represents a frequency component and is termed as a frequency sub-band. The analysis filter bank module 110 may include many different types of filter banks and filters in accordance with various embodiments (not depicted in FIG. 1). In one example, the analysis filter bank module 110 may comprise a linear phase filter bank.


In some embodiments, the analysis filter bank module 110 may include a plurality of complex-valued filters. These filters may be first order filters (e.g., single pole, complex-valued) to reduce computational expense as compared to second and higher order filters. Additionally, the filters may be infinite impulse response (IIR) filters with cutoff frequencies designed to produce a desired channel resolution. In some embodiments, the filters may perform Hilbert transforms with a variety of coefficients upon the complex audio signal in order to suppress or output signals within specific frequency sub-bands. In other embodiments, the filters may perform fast cochlear transforms. The filters may be organized into a filter cascade whereby an output of one filter becomes an input in a next filter in the cascade, according to various embodiments. Sets of filters in the cascade may be separated into octaves. Collectively, the outputs of the filters represent the frequency sub-band components of the audio signal.


The exemplary modification module 112 receives each of the frequency sub-band signals over respective analysis paths from the analysis filter bank module 110. The modification module 112 can modify/adjust the frequency sub-band signals based on the respective analysis paths. In one example, the modification module 112 suppresses noise from frequency sub-band signals received over specific analysis paths. In another example, a frequency sub-band signal received from specific analysis paths may be attenuated, suppressed, or passed through a further filter to eliminate objectionable portions of the frequency sub-band signal.


The reconstruction module 114 reconstructs the modified frequency sub-band signals into a reconstructed audio signal for output. In exemplary embodiments, the reconstruction module 114 performs phase alignment on the complex frequency sub-band signals, performs amplitude compensation, cancels complex portions, and delays remaining real portions of the frequency sub-band signals during reconstruction in order to improve resolution or fidelity of the reconstructed audio signal. The reconstruction module 114 will be discussed in more detail in connection with FIG. 2.


The audio sink 108 comprises any device for outputting the reconstructed audio signal. In some embodiments, the audio sink 108 outputs an analog reconstructed audio signal. For example, the audio sink 108 may comprise a digital-to-analog (D/A) converter and a speaker. In this example, the D/A converter is configured to receive and convert the reconstructed audio signal from the audio processing engine 102 into the analog reconstructed audio signal. The speaker can then receive and output the analog reconstructed audio signal. The audio sink 108 can comprise any analog output device including, but not limited to, headphones, ear buds, or a hearing aid. Alternately, the audio sink 108 comprises the D/A converter and an audio output port configured to be coupled to external audio devices (e.g., speakers, headphones, ear buds, hearing aid.)


In alternative embodiments, the audio sink 108 outputs a digital reconstructed audio signal. For example, the audio sink 108 may comprise a disk device, wherein the reconstructed audio signal may be stored onto a hard disk or other storage medium. In alternate embodiments, the audio sink 108 is optional and the audio processing engine 102 produces the reconstructed audio signal for further processing (not depicted in FIG. 1).


Referring now to FIG. 2, the exemplary reconstruction module 114 is shown in more detail. The reconstruction module 114 may comprise a grouping sub-module 202, a delay sub-module 204, an adjustment sub-module 206, and a combination sub-module 208. Although FIG. 2 describes the reconstruction module 114 as including various sub-modules, fewer or more sub-modules may be included in the reconstruction module 114 and still fall within the scope of various embodiments. Additionally, various sub-modules of the reconstruction module 114 may be combined into a single sub-module. For example, functionalities of the grouping sub-module 202 and the delay sub-module 204 may be combined into one sub-module.


The grouping sub-module 202 may be configured to group the plurality of frequency sub-band signals into two or more groups. In exemplary embodiments, the frequency sub-band signals embodied within each group include frequency sub-band signals from adjacent frequency bands. In some embodiments, the groups may overlap. That is, one or more frequency sub-band signals may be included in more than one group in some embodiments. In other embodiments, the groups do not overlap. The number of groups designated by the grouping sub-module 202 may be optimized based on computational complexity, signal quality, and other considerations. Furthermore, the number of frequency sub-bands included in each group may vary from group to group or be the same for each group.


The delay sub-module 204 may be configured to apply a delay function to at least one of the two or more groups. The delay function may determine a period of time to delay each frequency sub-band signal included in the two or more groups. In exemplary embodiments, the delay function is applied to realign group delays of the frequency sub-band signals in at least one of the two or more groups. The delay function may be based, at least in part, on a psychoacoustic model. Generally speaking, psychoacoustic models treat subjective or psychological aspects of acoustic phenomena, such as perception of phase shift in audio signals and sensitivity of a human ear. Additionally, the delay function may be defined using a delay table, as further described in connection with FIG. 3.


The adjustment sub-module 206 may be configured to adjust one or more of a phase or amplitude of the frequency sub-band signals. In exemplary embodiments, these adjustments may minimize ripples, such as in a transfer function, produced during reconstruction. The phase and amplitude may be derived for any sample by the adjustment sub-module 206. Thus, the reconstruction of the audio signal is mathematically made easier. As a result of this approach, the amplitude and phase for any sample is readily available for further processing. According to some embodiments, the adjustment sub-module 206 is configured to cancel, or otherwise remove, the imaginary portion of each frequency sub-band signal.


The combination sub-module 208 may be configured to combine the groups to reconstruct the audio signal. According to exemplary embodiments, real portions of the frequency sub-band signals are summed to generate a reconstructed audio signal. Other methods for reconstructing the audio signal, however, may be used by the combination sub-module 208 in alternative embodiments. The reconstructed audio signal may then be outputted by the audio sink 108 or be subjected to further processing.



FIG. 3 is a diagram illustrating signal flow within the reconstruction module 114 in accordance with one example. From left to right, as depicted, frequency sub-band signals s1-sn are received and grouped by the grouping sub-module 202, delayed by the delay sub-module 204, adjusted by the adjustments sub-module 206, and reconstructed by the combination sub-module 208, as further described herein. The frequency sub-band signals s1-sn may be received from the analysis filter bank module 110 or the modification module 112, in accordance with various embodiments.


The frequency sub-band signals, as received by the grouping sub-module 202, have successively shifted group delays as a function of frequency, as illustrated by plotted curves associated with each of the frequency sub-band signals. The curves are centered about time τ1n for frequency sub-band signals s1-sn, respectively. Relative to the frequency sub-band signal s1, each successive frequency sub-band signal sx lags by a time τ(sx)=τx−τ1, where x=2, 3, 4, . . . , n. For example, frequency sub-band signal S6 lags frequency sub-band signal s1 by a time τ(s6)=τ6−τ1. Actual values of the lag times τ(sx) may depend on which types of filters are included in the analysis filter bank module 110, delay characteristics of such filters, how the filters are arranged, and a total number of frequency sub-band signals, among other factors.


As depicted in FIG. 3, the grouping sub-module 202 groups the frequency sub-band signal into groups of three, wherein groups g1, g2, and so forth, through gn comprise the frequency sub-band signals s1-s3, the frequency sub-band signals s4-s6, and so forth, through the frequency sub-band signals sn-2-sn, respectively. According to exemplary embodiments, the grouping sub-module 202 may group the frequency sub-band signals into any number of groups. Consequently, any number of frequency sub-band signals may be included in any one given group, such that the groups do not necessarily comprise an equal number of frequency sub-band signals. Furthermore, the groups may be overlapping or non-overlapping and include frequency sub-band signals from adjacent frequency bands.


After the frequency sub-band signals s1-sn are divided into groups by the grouping sub-module 202, the delay sub-module 204 may apply delays d1-dn to the frequency sub-band signals s1-sn. As depicted, the frequency sub-band signals included in each group are delayed so as to be aligned with the frequency sub-band signal having the greatest lag time τ(sx) within the group. For example, the frequency sub-band signals s1 and s2 are delayed to be aligned with the frequency sub-band signal s3. The frequency sub-band signals s1-sn are delayed as described in Table 1.













TABLE 1








Sub-band





signal
Delay










S1
d1 = τ3 − τ1




S2
d2 = τ3 − τ2




S3
d3 = 0




S4
d4 = τ6 − τ4




S5
d5 = τ6 − τ5




S6
d6 = 0




.
.




.
.




.
.




Sn−2
dn−2 = τn − τn−2




Sn−1
dn−1 = τn − τn−1




Sn
dn = 0











FIG. 4 displays an exemplary delay function 402. The delay function 402 comprises a delay function segment 402a, a delay function segment 402b, and a delay function segment 402c that correspond to the groups comprising the frequency sub-band signals s1-s3, the frequency sub-band signals s4-s6, and the frequency sub-band signals sn-2-sn, respectively, as described in Table 1. Although the delay function segments 402a-402c are depicted as linear, any type of function may be applied depending on the values of the lag times τ(sx), in accordance with various embodiments.


It is noted that for full delay compensation of all of the frequency sub-band signals, a delay function 404 may be invoked, wherein the delay function 404 coincides with the delay function segment 402c. The full delay compensation would result in the frequency sub-band signals s1-sn-1 being delayed so as to be aligned with the frequency sub-band signal sn.


Again referring to FIG. 3, the adjustment sub-module 206 may perform computations c1-cn on the frequency sub-band signals s1-sn. The computations c1-cn may be performed to adjust one or more of a phase or amplitude of the frequency sub-band signals s1-sn. According to various embodiments, the computations c1-cn may include a derivation of the phase and amplitude, as well as cancellation of the imaginary portions, of each of the frequency sub-band signals s1-sn.


The combination sub-module 208, as depicted in FIG. 3, combines the frequency sub-band signals s1-sn to generate a reconstructed audio signal Srecon. According to exemplary embodiments, the real portions of the frequency sub-band signals s1-sn are summed to generate the reconstructed audio signal Srecon. Finally, the reconstructed audio signal Srecon may be outputted, such as by the audio sink 108 or be subjected to further processing.



FIG. 5 presents characteristics 500 of an exemplary audio signal reconstructed from three groups of frequency sub-band signals. The characteristics 500 include group delay versus frequency 502, magnitude versus frequency 504, and impulse response versus time 506.



FIG. 6 is a flowchart 600 of an exemplary method for reconstructing a decomposed audio signal. The exemplary method described by the flowchart 600 may be performed by the audio processing engine 102, or by modules or sub-modules therein, as described below. In addition, steps of the flowchart 600 may be performed in varying orders or concurrently. Additionally, various steps may be added, subtracted, or combined in the exemplary method described by the flowchart 600 and still fall within the scope of the present invention.


In step 602, a decomposed audio signal is received from a filter bank, wherein the decomposed audio signal comprises a plurality of frequency sub-band signals having successively shifted group delays as a function of frequency. An example of the successively shifted group delays is illustrated by the plotted curves associated with the frequency sub-band signals s1-sn shown in FIG. 3. The plurality of frequency sub-band signals may be received by the reconstruction module 114 or by sub-modules included therein. Additionally, the plurality of frequency sub-band signals may be received from the analysis filter bank module 110 or the modification module 112, in accordance with various embodiments.


In step 604, the plurality of frequency sub-band signals is grouped into two or more groups. According to exemplary embodiments, the grouping sub-module 202 may perform step 604. In addition, any number of the plurality of frequency sub-band signals may be included in any one given group. Furthermore, the groups may be overlapping or non-overlapping and include frequency sub-band signals from adjacent frequency bands, in accordance with various embodiments.


In step 606, a delay function is applied to at least one of the two or more groups. The delay sub-module 204 may apply the delay function to at least one of the two or more groups in exemplary embodiments. As illustrated in connection with FIG. 3, the delay function may determine a period of time to delay each frequency sub-band signal included in the two or more groups in order to realign the group delays of some or all of the plurality of frequency sub-band signals. In one example, the plurality of frequency sub-band signals are delayed such that the group delays of frequency sub-band signals in each of the two or more groups are aligned with the frequency sub-band signal having the greatest lag time in each respective group. In some embodiments, the delay function may be based, at least in part, on a psychoacoustic model. Furthermore, a delay table (see, e.g., Table 1) may be used to define the delay function in some embodiments.


In step 608, the groups are combined to reconstruct the audio signal. In accordance with exemplary embodiments, the combination sub-module 208 may perform the step 608. The real portions of the plurality of frequency sub-band signals may be summed to reconstruct the audio signal in some embodiment. In other embodiments, however, various methods for reconstructing the audio signal may also be used.


In step 610, the audio signal is outputted. According to some embodiments, the audio signal may be outputted by the audio sink 108. In other embodiments, the audio signal may be subjected to further processing.


The above-described engines, modules, and sub-modules may be comprised of instructions that are stored in storage media such as a machine readable medium (e.g., a computer readable medium). The instructions may be retrieved and executed by a processor. Some examples of instructions include software, program code, and firmware. Some examples of storage media comprise memory devices and integrated circuits. The instructions are operational when executed by the processor to direct the processor to operate in accordance with embodiments of the present invention. Those skilled in the art are familiar with instructions, processors, and storage media.


The present invention has been described above with reference to exemplary embodiments. It will be apparent to those skilled in the art that various modifications may be made and other embodiments can be used without departing from the broader scope of the invention. Therefore, these and other variations upon the exemplary embodiments are intended to be covered by the present invention.

Claims
  • 1. A method for reconstructing a decomposed audio signal, comprising: receiving, using a processor a plurality of frequency sub-band signals from a filter bank, the filter bank decomposing an audio signal into the plurality of frequency sub-band signals, the plurality of frequency sub-band signals comprising: a first frequency sub-band signal received from the filter bank,a second frequency sub-band signal received, from the filter bank, having a first lag time from the first frequency sub-band signal,a third frequency sub-band signal received from the filter bank, having a second lag time from the second frequency sub-band signal, andadditional frequency sub-band signals each received, from the filter bank, having a respective lag time from a frequency sub-band signal of the plurality of frequency sub-band signals;grouping, using the processor, the plurality of frequency sub-band signals into two or more groups;delaying, using the processor, the two or more groups by a delay function, the delay function delaying by a different delay of a plurality of delays each frequency sub-band signal in each group of the two or more groups, such that each frequency sub-band signal in each group is aligned with the frequency sub-band signal having a greatest lag time in each group, the plurality of delays including a zero delay; andcombining, using the processor, the groups to reconstruct the audio signal.
  • 2. The method of claim 1, further comprising adjusting, using the processor, one or more of a phase or amplitude of at least one of the plurality of frequency sub-band signals.
  • 3. The method of claim 1, wherein the delay function is based, at least in part, on a psychoacoustic model.
  • 4. The method of claim 1, further comprising defining the delay function using a delay table.
  • 5. The method of claim 1, wherein the two or more groups do not overlap.
  • 6. The method of claim 1, wherein the combining comprises summing the two or more groups.
  • 7. A system for reconstructing a decomposed audio signal, comprising: a reconstruction module, using a processor, configured to receive a decomposed audio signal comprising a plurality of frequency sub-band signals from a filter bank, the plurality of frequency sub-band signals comprising: a first frequency sub-band signal received from the filter bank,a second frequency sub-band signal received, from the filter bank, having a first lag time from the first frequency sub-band signal,a third frequency sub-band signal received, from the filter bank, having a second lag time from the second frequency sub-band signal, andadditional frequency sub-band signals each received, from the filter bank, having a respective lag time from a frequency sub-band signal of the plurality of frequency sub-band signals,the reconstruction module comprising: a grouping sub-module configured to group the plurality of frequency sub-band signals into two or more groups,a delay sub-module configured to delay the two or more groups by a delay function, the delay function delaying by a different delay of a plurality of delays each frequency sub-band in each group of the two or more groups, such that each frequency sub-band signal in each group is aligned with the frequency sub-band signal having a greatest lag time in each group, the plurality of delays including a zero delay, anda combination sub-module configured to combine the groups to reconstruct the audio signal.
  • 8. The system of claim 7, wherein the reconstruction module further comprises an adjustment sub-module configured to adjust one or more of a phase or amplitude of at least one of the plurality of frequency sub-band signals.
  • 9. The system of claim 7, wherein the delay function is based, at least in part, on a psychoacoustic model.
  • 10. The system of claim 7, wherein the delay function is defined using a delay table.
  • 11. The system of claim 7, wherein the combination sub-module is further configured to sum the two or more groups.
  • 12. The system of claim 7, further comprising a fast cochlear transform filter bank, the fast cochlear transform filter bank being stored in a memory and running on the processor, and providing the decomposed audio signal.
  • 13. The system of claim 7, further comprising a linear phase filter bank, the linear phase filter bank being stored in a memory and running on the processor, and providing the decomposed audio signal.
  • 14. The system of claim 7, further comprising a complex-valued filter bank, the complex-valued filter bank being configured to operate on complex-valued inputs and being stored in a memory and running using the processor, and providing the decomposed audio signal.
  • 15. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for reconstructing a decomposed audio signal, the method comprising: receiving a decomposed audio signal comprising a plurality of frequency sub-band signals from a filter bank, the plurality of frequency sub-band signals comprising: a first frequency sub-band signal received from the filter bank,a second frequency sub-band signal received, from the filter bank, having a first lag time from the first frequency sub-band signal,a third frequency sub-band signal received, from the filter bank, having a second lag time from the second frequency sub-band signal, andadditional frequency sub-band signals each received, from the filter bank, having a respective lag time from a frequency sub-band signal of the plurality of frequency sub-band signals;grouping the plurality of frequency sub-band signals into two or more groups;delaying the two or more groups by a delay function, the delay function delaying by a different delay of a plurality of delays each frequency sub-band signal in each group of the two or more groups, such that each frequency sub-band signal in the each group is aligned with the frequency sub-band signal having a greatest received lag time in each group, the plurality of delays including a zero delay; andcombining the groups to reconstruct the audio signal.
  • 16. The non-transitory computer readable medium of claim 15, further comprising adjusting one or more of a phase or amplitude of each of the plurality of frequency sub-band signals.
  • 17. The non-transitory computer readable medium of claim 15, wherein the delay function is based, at least in part, on a psychoacoustic model.
  • 18. The non-transitory computer readable medium of claim 15, further comprising defining the delay function using a delay table.
  • 19. A method for reconstructing a decomposed audio signal, comprising: receiving, using a processor, a decomposed audio signal comprising a plurality of frequency sub-band signals from a filter bank, the plurality of frequency sub-band signals comprising: a first frequency sub-band signal received from the filter bank, the first frequency sub-band being substantially centered about a first time,a second frequency sub-band signal, received from the filter bank, having a first lag time from the first frequency sub-band signal, the second frequency sub-band being substantially centered about a second time, such that the first lag time is a difference between the first time and the second time,a third frequency sub-band signal, received from the filter bank, having a second lag time from the second frequency sub-band signal, the third frequency sub-band being substantially centered about a third time, such that the second lag time is a difference between the second time and the third time, andadditional frequency sub-band signals each received, from the filter bank, having a respective lag time from a frequency sub-band signal of the plurality of frequency sub-band signals;grouping, using the processor, the plurality of frequency sub-band signals into two or more groups;delaying, using the processor, the two or more groups by a delay function, the delay function delaying by a different delay of a plurality of delays each frequency sub-band signal in each group of the two or more groups, such that each frequency sub-band signal in each group is aligned with the frequency sub-band signal in each group having a greatest lag time, the plurality of delays including a zero delay, the delay function being based on at least in part on a psychoacoustic model or defined using a delay table; andcombining, using the processor, the groups to reconstruct the audio signal.
CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patent application Ser. No. 11/441,675 filed May 25, 2006 and entitled “System and Method for Processing an Audio Signal,” now U.S. Pat. No. 8,150,065, issued Apr. 3, 2012, the disclosure of which is incorporated herein by reference.

US Referenced Citations (233)
Number Name Date Kind
3976863 Engel Aug 1976 A
3978287 Fletcher et al. Aug 1976 A
4137510 Iwahara Jan 1979 A
4433604 Ott Feb 1984 A
4516259 Yato et al. May 1985 A
4536844 Lyon Aug 1985 A
4581758 Coker et al. Apr 1986 A
4628529 Borth et al. Dec 1986 A
4630304 Borth et al. Dec 1986 A
4649505 Zinser, Jr. et al. Mar 1987 A
4658426 Chabries et al. Apr 1987 A
4674125 Carlson et al. Jun 1987 A
4718104 Anderson Jan 1988 A
4811404 Vilmur et al. Mar 1989 A
4812996 Stubbs Mar 1989 A
4864620 Bialick Sep 1989 A
4920508 Yassaie et al. Apr 1990 A
5027410 Williamson et al. Jun 1991 A
5054085 Meisel et al. Oct 1991 A
5058419 Nordstrom et al. Oct 1991 A
5099738 Hotz Mar 1992 A
5119711 Bell et al. Jun 1992 A
5142961 Paroutaud Sep 1992 A
5150413 Nakatani et al. Sep 1992 A
5175769 Hejna, Jr. et al. Dec 1992 A
5187776 Yanker Feb 1993 A
5208864 Kaneda May 1993 A
5210366 Sykes, Jr. May 1993 A
5230022 Sakata Jul 1993 A
5319736 Hunt Jun 1994 A
5323459 Hirano Jun 1994 A
5341432 Suzuki et al. Aug 1994 A
5381473 Andrea et al. Jan 1995 A
5381512 Holton et al. Jan 1995 A
5400409 Linhard Mar 1995 A
5402493 Goldstein Mar 1995 A
5402496 Soli et al. Mar 1995 A
5471195 Rickman Nov 1995 A
5473702 Yoshida et al. Dec 1995 A
5473759 Slaney et al. Dec 1995 A
5479564 Vogten et al. Dec 1995 A
5502663 Lyon Mar 1996 A
5544250 Urbanski Aug 1996 A
5574824 Slyh et al. Nov 1996 A
5583784 Kapust et al. Dec 1996 A
5587998 Velardo, Jr. et al. Dec 1996 A
5590241 Park et al. Dec 1996 A
5602962 Kellermann Feb 1997 A
5675778 Jones Oct 1997 A
5682463 Allen et al. Oct 1997 A
5694474 Ngo et al. Dec 1997 A
5706395 Arslan et al. Jan 1998 A
5717829 Takagi Feb 1998 A
5729612 Abel et al. Mar 1998 A
5732189 Johnston et al. Mar 1998 A
5749064 Pawate et al. May 1998 A
5757937 Itoh et al. May 1998 A
5792971 Timis et al. Aug 1998 A
5796819 Romesburg Aug 1998 A
5806025 Vis et al. Sep 1998 A
5809463 Gupta et al. Sep 1998 A
5825320 Miyamori et al. Oct 1998 A
5839101 Vahatalo et al. Nov 1998 A
5920840 Satyamurti et al. Jul 1999 A
5933495 Oh Aug 1999 A
5943429 Handel Aug 1999 A
5956674 Smyth et al. Sep 1999 A
5974380 Smyth et al. Oct 1999 A
5978824 Ikeda Nov 1999 A
5983139 Zierhofer Nov 1999 A
5990405 Auten et al. Nov 1999 A
6002776 Bhadkamkar et al. Dec 1999 A
6061456 Andrea et al. May 2000 A
6072881 Linder Jun 2000 A
6097820 Turner Aug 2000 A
6108626 Cellario et al. Aug 2000 A
6122610 Isabelle Sep 2000 A
6134524 Peters et al. Oct 2000 A
6137349 Menkhoff et al. Oct 2000 A
6140809 Doi Oct 2000 A
6173255 Wilson et al. Jan 2001 B1
6180273 Okamoto Jan 2001 B1
6216103 Wu et al. Apr 2001 B1
6222927 Feng et al. Apr 2001 B1
6223090 Brungart Apr 2001 B1
6226616 You et al. May 2001 B1
6263307 Arslan et al. Jul 2001 B1
6266633 Higgins et al. Jul 2001 B1
6317501 Matsuo Nov 2001 B1
6339758 Kanazawa et al. Jan 2002 B1
6355869 Mitton Mar 2002 B1
6363345 Marash et al. Mar 2002 B1
6381570 Li et al. Apr 2002 B2
6430295 Handel et al. Aug 2002 B1
6434417 Lovett Aug 2002 B1
6449586 Hoshuyama Sep 2002 B1
6469732 Chang et al. Oct 2002 B1
6487257 Gustafsson et al. Nov 2002 B1
6496795 Malvar Dec 2002 B1
6513004 Rigazio et al. Jan 2003 B1
6516066 Hayashi Feb 2003 B2
6529606 Jackson, Jr. II et al. Mar 2003 B1
6549630 Bobisuthi Apr 2003 B1
6584203 Elko et al. Jun 2003 B2
6622030 Romesburg et al. Sep 2003 B1
6717991 Gustafsson et al. Apr 2004 B1
6718309 Selly Apr 2004 B1
6738482 Jaber May 2004 B1
6760450 Matsuo Jul 2004 B2
6785381 Gartner et al. Aug 2004 B2
6792118 Watts Sep 2004 B2
6795558 Matsuo Sep 2004 B2
6798886 Smith et al. Sep 2004 B1
6810273 Mattila et al. Oct 2004 B1
6882736 Dickel et al. Apr 2005 B2
6915264 Baumgarte Jul 2005 B2
6917688 Yu et al. Jul 2005 B2
6944510 Ballesty et al. Sep 2005 B1
6978159 Feng et al. Dec 2005 B2
6982377 Sakurai et al. Jan 2006 B2
6999582 Popovic et al. Feb 2006 B1
7016507 Brennan Mar 2006 B1
7020605 Gao Mar 2006 B2
7031478 Belt et al. Apr 2006 B2
7054452 Ukita May 2006 B2
7065485 Chong-White et al. Jun 2006 B1
7076315 Watts Jul 2006 B1
7092529 Yu et al. Aug 2006 B2
7092882 Arrowood et al. Aug 2006 B2
7099821 Visser et al. Aug 2006 B2
7142677 Gonopolskiy et al. Nov 2006 B2
7146316 Alves Dec 2006 B2
7155019 Hou Dec 2006 B2
7164620 Hoshuyama Jan 2007 B2
7171008 Elko Jan 2007 B2
7171246 Mattila et al. Jan 2007 B2
7174022 Zhang et al. Feb 2007 B1
7206418 Yang et al. Apr 2007 B2
7209567 Kozel et al. Apr 2007 B1
7225001 Eriksson et al. May 2007 B1
7242762 He et al. Jul 2007 B2
7246058 Burnett Jul 2007 B2
7254242 Ise et al. Aug 2007 B2
7359520 Brennan et al. Apr 2008 B2
7412379 Taori et al. Aug 2008 B2
20010016020 Gustafsson et al. Aug 2001 A1
20010031053 Feng et al. Oct 2001 A1
20020002455 Accardi et al. Jan 2002 A1
20020009203 Erten Jan 2002 A1
20020041693 Matsuo Apr 2002 A1
20020080980 Matsuo Jun 2002 A1
20020106092 Matsuo Aug 2002 A1
20020116187 Erten Aug 2002 A1
20020133334 Coorman et al. Sep 2002 A1
20020147595 Baumgarte Oct 2002 A1
20020184013 Walker Dec 2002 A1
20030014248 Vetter Jan 2003 A1
20030026437 Janse et al. Feb 2003 A1
20030033140 Taori et al. Feb 2003 A1
20030039369 Bullen Feb 2003 A1
20030040908 Yang et al. Feb 2003 A1
20030061032 Gonopolskiy Mar 2003 A1
20030063759 Brennan et al. Apr 2003 A1
20030072382 Raleigh et al. Apr 2003 A1
20030072460 Gonopolskiy et al. Apr 2003 A1
20030095667 Watts May 2003 A1
20030099345 Gartner et al. May 2003 A1
20030101048 Liu May 2003 A1
20030103632 Goubran et al. Jun 2003 A1
20030128851 Furuta Jul 2003 A1
20030138116 Jones et al. Jul 2003 A1
20030147538 Elko Aug 2003 A1
20030169891 Ryan et al. Sep 2003 A1
20030228023 Burnett et al. Dec 2003 A1
20040013276 Ellis et al. Jan 2004 A1
20040047464 Yu et al. Mar 2004 A1
20040057574 Faller Mar 2004 A1
20040078199 Kremer et al. Apr 2004 A1
20040131178 Shahaf et al. Jul 2004 A1
20040133421 Burnett et al. Jul 2004 A1
20040165736 Hetherington et al. Aug 2004 A1
20040196989 Friedman et al. Oct 2004 A1
20040263636 Cutler et al. Dec 2004 A1
20050025263 Wu Feb 2005 A1
20050027520 Mattila et al. Feb 2005 A1
20050049864 Kaltenmeier et al. Mar 2005 A1
20050060142 Visser et al. Mar 2005 A1
20050152559 Gierl et al. Jul 2005 A1
20050185813 Sinclair et al. Aug 2005 A1
20050213778 Buck et al. Sep 2005 A1
20050216259 Watts Sep 2005 A1
20050228518 Watts Oct 2005 A1
20050276423 Aubauer et al. Dec 2005 A1
20050288923 Kok Dec 2005 A1
20060072768 Schwartz et al. Apr 2006 A1
20060074646 Alves et al. Apr 2006 A1
20060098809 Nongpiur et al. May 2006 A1
20060120537 Burnett et al. Jun 2006 A1
20060133621 Chen et al. Jun 2006 A1
20060149535 Choi et al. Jul 2006 A1
20060184363 McCree et al. Aug 2006 A1
20060198542 Benjelloun Touimi et al. Sep 2006 A1
20060222184 Buck et al. Oct 2006 A1
20070021958 Visser et al. Jan 2007 A1
20070027685 Arakawa et al. Feb 2007 A1
20070033020 (Kelleher) Francois et al. Feb 2007 A1
20070067166 Pan et al. Mar 2007 A1
20070078649 Hetherington et al. Apr 2007 A1
20070094031 Chen Apr 2007 A1
20070100612 Ekstrand et al. May 2007 A1
20070116300 Chen May 2007 A1
20070150268 Acero et al. Jun 2007 A1
20070154031 Avendano et al. Jul 2007 A1
20070165879 Deng et al. Jul 2007 A1
20070195968 Jaber Aug 2007 A1
20070230712 Belt et al. Oct 2007 A1
20070276656 Solbach et al. Nov 2007 A1
20080019548 Avendano Jan 2008 A1
20080033723 Jang et al. Feb 2008 A1
20080140391 Yen et al. Jun 2008 A1
20080201138 Visser et al. Aug 2008 A1
20080228478 Hetherington et al. Sep 2008 A1
20080260175 Elko Oct 2008 A1
20090012783 Klein Jan 2009 A1
20090012786 Zhang et al. Jan 2009 A1
20090129610 Kim et al. May 2009 A1
20090220107 Every et al. Sep 2009 A1
20090238373 Klein Sep 2009 A1
20090253418 Makinen Oct 2009 A1
20090271187 Yen et al. Oct 2009 A1
20090323982 Solbach et al. Dec 2009 A1
20100278352 Petit et al. Nov 2010 A1
20110178800 Watts Jul 2011 A1
Foreign Referenced Citations (14)
Number Date Country
62110349 May 1987 JP
4184400 Jul 1992 JP
5053587 Mar 1993 JP
6269083 Sep 1994 JP
10-313497 Nov 1998 JP
11-249693 Sep 1999 JP
2005110127 Apr 2005 JP
2005195955 Jul 2005 JP
0174118 Oct 2001 WO
03043374 May 2003 WO
03069499 Aug 2003 WO
2007081916 Jul 2007 WO
2007140003 Dec 2007 WO
2010005493 Jan 2010 WO
Non-Patent Literature Citations (64)
Entry
US Reg. No. 2,875,755 (Aug. 17, 2004).
International Search Report dated May 29, 2003 in Application No. PCT/US03/04124.
International Search Report and Written Opinion dated Oct. 19, 2007 in Application No. PCT/US07/00463.
International Search Report and Written Opinion dated Apr. 9, 2008 in Application No. PCT/US07/21654.
International Search Report and Written Opinion dated Sep. 16, 2008 in Application No. PCT/US07/12628.
International Search Report and Written Opinion dated Oct. 1, 2008 in Application No. PCT/US08/08249.
International Search Report and Written Opinion dated May 11, 2009 in Application No. PCT/US09/01667.
International Search Report and Written Opinion dated Aug. 27, 2009 in Application No. PCT/US09/03813.
International Search Report and Written Opinion dated May 20, 2010 in Application No. PCT/US09/06754.
Dahl, Mattias et al., “Acoustic Echo and Noise Cancelling Using Microphone Arrays”, International Symposium on Signal Processing and its Applications, ISSPA, Gold coast, Australia, Aug. 25-30, 1996, pp. 379-382.
Demol, M. et al. “Efficient Non-Uniform Time-Scaling of Speech With WSOLA for CALL Applications”, Proceedings of InSTIL/ICALL2004—NLP and Speech Technologies in Advanced Language Learning Systems—Venice Jun. 17-19, 2004.
Laroche, Jean. “Time and Pitch Scale Modification of Audio Signals”, in “Applications of Digital Signal Processing to Audio and Acoustics”, The Kluwer International Series in Engineering and Computer Science, vol. 437, pp. 279-309, 2002.
Moulines, Eric et al., “Non-Parametric Techniques for Pitch-Scale and Time-Scale Modification of Speech”, Speech Communication, vol. 16, pp. 175-205, 1995.
Verhelst, Werner, “Overlap-Add Methods for Time-Scaling of Speech”, Speech Communication vol. 30, pp. 207-221, 2000.
Allen, Jont B. “Short Term Spectral Analysis, Synthesis, and Modification by Discrete Fourier Transform”, IEEE Transactions on Acoustics, Speech, and Signal Processing. vol. ASSP-25, No. 3, Jun. 1977. pp. 235-238.
Allen, Jont B. et al. “A Unified Approach to Short-Time Fourier Analysis and Synthesis”, Proceedings of the IEEE. vol. 65, No. 11, Nov. 1977. pp. 1558-1564.
Avendano, Carlos, “Frequency-Domain Source Identification and Manipulation in Stereo Mixes for Enhancement, Suppression and Re-Panning Applications,” 2003 IEEE Workshop on Application of Signal Processing to Audio and Acoustics, Oct. 19-22, pp. 55-58, New Paltz, New York, USA.
Boll, Steven F. “Suppression of Acoustic Noise in Speech using Spectral Subtraction”, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-27, No. 2, Apr. 1979, pp. 113-120.
Boll, Steven F. et al. “Suppression of Acoustic Noise in Speech Using Two Microphone Adaptive Noise Cancellation”, IEEE Transactions on Acoustic, Speech, and Signal Processing, vol. ASSP-28, No. 6, Dec. 1980, pp. 752-753.
Boll, Steven F. “Suppression of Acoustic Noise in Speech Using Spectral Subtraction”, Dept. of Computer Science, University of Utah Salt Lake City, Utah, Apr. 1979, pp. 18-19.
Chen, Jingdong et al. “New Insights into the Noise Reduction Wiener Filter”, IEEE Transactions on Audio, Speech, and Language Processing. vol. 14, No. 4, Jul. 2006, pp. 1218-1234.
Cohen, Israel et al. “Microphone Array Post-Filtering for Non-Stationary Noise Suppression”, IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2002, pp. 1-4.
Cohen, Israel, “Multichannel Post-Filtering in Nonstationary Noise Environments”, IEEE Transactions on Signal Processing, vol. 52, No. 5, May 2004, pp. 1149-1160.
Dahl, Mattias et al., “Simultaneous Echo Cancellation and Car Noise Suppression Employing a Microphone Array”, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, Apr. 21-24, pp. 239-242.
Elko, Gary W., “Chapter 2: Differential Microphone Arrays”, “Audio Signal Processing for Next-Generation Multimedia Communication Systems”, 2004, pp. 12-65, Kluwer Academic Publishers, Norwell, Massachusetts, USA.
“ENT 172.” Instructional Module. Prince George's Community College Department of Engineering Technology. Accessed: Oct. 15, 2011. Subsection: “Polar and Rectangular Notation”. <http://academic.ppgcc.edu/ent/ent172—instr—mod.html>.
Fuchs, Martin et al. “Noise Suppression for Automotive Applications Based on Directional Information”, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 17-21, pp. 237-240.
Fulghum, D. P. et al., “LPC Voice Digitizer with Background Noise Suppression”, 1979 IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 220-223.
Goubran, R.A. “Acoustic Noise Suppression Using Regression Adaptive Filtering”, 1990 IEEE 40th Vehicular Technology Conference, May 6-9, pp. 48-53.
Graupe, Daniel et al., “Blind Adaptive Filtering of Speech from Noise of Unknown Spectrum Using a Virtual Feedback Configuration”, IEEE Transactions on Speech and Audio Processing, Mar. 2000, vol. 8, No. 2, pp. 146-158.
Haykin, Simon et al. “Appendix A.2 Complex Numbers.” Signals and Systems. 2nd Ed. 2003. p. 764.
Hermansky, Hynek “Should Recognizers Have Ears?”, In Proc. ESCA Tutorial and Research Workshop on Robust Speech Recognition for Unknown Communication Channels, pp. 1-10, France 1997.
Hohmann, V. “Frequency Analysis and Synthesis Using a Gammatone Filterbank”, ACTA Acustica United with Acustica, 2002, vol. 88, pp. 433-442.
Jeffress, Lloyd A. et al. “A Place Theory of Sound Localization,” Journal of Comparative and Physiological Psychology, 1948, vol. 41, p. 35-39.
Jeong, Hyuk et al., “Implementation of a New Algorithm Using the STFT with Variable Frequency Resolution for the Time-Frequency Auditory Model”, J. Audio Eng. Soc., Apr. 1999, vol. 47, No. 4., pp. 240-251.
Kates, James M. “A Time-Domain Digital Cochlear Model”, IEEE Transactions on Signal Processing, Dec. 1991, vol. 39, No. 12, pp. 2573-2592.
Lazzaro, John et al., “A Silicon Model of Auditory Localization,” Neural Computation Spring 1989, vol. 1, pp. 47-57, Massachusetts Institute of Technology.
Lippmann, Richard P. “Speech Recognition by Machines and Humans”, Speech Communication, Jul. 1997, vol. 22, No. 1, pp. 1-15.
Liu, Chen et al. “A Two-Microphone Dual Delay-Line Approach for Extraction of a Speech Sound in the Presence of Multiple Interferers”, Journal of the Acoustical Society of America, vol. 110, No. 6, Dec. 2001, pp. 3218-3231.
Martin, Rainer et al. “Combined Acoustic Echo Cancellation, Dereverberation and Noise Reduction: A two Microphone Approach”, Annales des Telecommunications/Annals of Telecommunications. vol. 49, No. 7-8, Jul.-Aug. 1994, pp. 429-438.
Martin, Rainer “Spectral Subtraction Based on Minimum Statistics”, in Proceedings Europe. Signal Processing Conf., 1994, pp. 1182-1185.
Mitra, Sanjit K. Digital Signal Processing: a Computer-based Approach. 2nd Ed. 2001. pp. 131-133.
Mizumachi, Mitsunori et al. “Noise Reduction by Paired-Microphones Using Spectral Subtraction”, 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, May 12-15. pp. 1001-1004.
Moonen, Marc et al. “Multi-Microphone Signal Enhancement Techniques for Noise Suppression and Dereverbration,” http://www.esat.kuleuven.ac.be/sista/yearreport97//node37.html, accessed on Apr. 21, 1998.
Watts, Lloyd Narrative of Prior Disclosure of Audio Display on Feb. 15, 2000 and May 31, 2000.
Cosi, Piero et al. (1996), “Lyon's Auditory Model Inversion: a Tool for Sound Separation and Speech Enhancement,” Proceedings of ESCA Workshop on ‘The Auditory Basis of Speech Perception,’ Keele University, Keele (UK), Jul. 15-19, 1996, pp. 194-197.
Parra, Lucas et al. “Convolutive Blind Separation of Non-Stationary Sources”, IEEE Transactions on Speech and Audio Processing. vol. 8, No. 3, May 2008, pp. 320-327.
Rabiner, Lawrence R. et al. “Digital Processing of Speech Signals”, (Prentice-Hall Series in Signal Processing). Upper Saddle River, NJ: Prentice Hall, 1978.
Weiss, Ron et al., “Estimating Single-Channel Source Separation Masks: Revelance Vector Machine Classifiers vs. Pitch-Based Masking”, Workshop on Statistical and Perceptual Audio Processing, 2006.
Schimmel, Steven et al., “Coherent Envelope Detection for Modulation Filtering of Speech,” 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, No. 7, pp. 221-224.
Slaney, Malcom, “Lyon's Cochlear Model”, Advanced Technology Group, Apple Technical Report #13, Apple Computer, Inc., 1988, pp. 1-79.
Slaney, Malcom, et al. “Auditory Model Inversion for Sound Separation,” 1994 IEEE International Conference on Acoustics, Speech and Signal Processing, Apr. 19-22, vol. 2, pp. 77-80.
Slaney, Malcom. “An Introduction to Auditory Model Inversion”, Interval Technical Report IRC 1994-014, http://coweb.ecn.purdue.edu/˜maclom/interval/1994-014/, Sep. 1994, accessed on Jul. 6, 2010.
Solbach, Ludger “An Architecture for Robust Partial Tracking and Onset Localization in Single Channel Audio Signal Mixes”, Technical University Hamburg-Harburg, 1998.
Stahl, V. et al., “Quantile Based Noise Estimation for Spectral Subtraction and Wiener Filtering,” 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun. 5-9, vol. 3, pp. 1875-1878.
Syntrillium Software Corporation, “Cool Edit User's Manual”, 1996, pp. 1-74.
Tashev, Ivan et al. “Microphone Array for Headset with Spatial Noise Suppressor”, http://research.microsoft.com/users/ivantash/Documents/Tashev—MAforHeadset—HSCMA—05.pdf. (4 pages).
Tchorz, Jurgen et al., “SNR Estimation Based on Amplitude Modulation Analysis with Applications to Noise Suppression”, IEEE Transactions on Speech and Audio Processing, vol. 11, No. 3, May 2003, pp. 184-192.
Valin, Jean-Marc et al. “Enhanced Robot Audition Based on Microphone Array Source Separation with Post-Filter”, Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sep. 28-Oct. 2, 2004, Sendai, Japan. pp. 2123-2128.
Watts, Lloyd, “Robust Hearing Systems for Intelligent Machines,” Applied Neurosystems Corporation, 2001, pp. 1-5.
Widrow, B. et al., “Adaptive Antenna Systems,” Proceedings of the IEEE, vol. 55, No. 12, pp. 2143-2159, Dec. 1967.
Yoo, Heejong et al., “Continuous-Time Audio Noise Suppression and Real-Time Implementation”, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, May 13-17, pp. IV3980-IV3983.
International Search Report dated Jun. 8, 2001 in Application No. PCT/US01/08372.
International Search Report dated Apr. 3, 2003 in Application No. PCT/US02/36946.
Related Publications (1)
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20100094643 A1 Apr 2010 US
Continuation in Parts (1)
Number Date Country
Parent 11441675 May 2006 US
Child 12319107 US