Abstract:
Architecture that mitigates echo in voice communications using echo detection and adaptive management of attenuation by a non-linear processor (NLP). Suppression values provided by the NLP are determined based on echo detection and retained on a case-by-case basis to automatically increase or decrease the attenuation as needed. Feedback is incorporated that where the controls for the NLP attenuation is given by the amount of echo that remains, and this in turn affects the amount of echo that remains.

Description:
BACKGROUND 
       [0001]    In a point-to-point or multiple point full-duplex voice communications, if acoustic coupling exists between the receive path (e.g., a speaker) and send path (e.g., microphone) of an endpoint, echo of the talker at the far-end will be present in the microphone at the near-end. An acoustic echo canceller (AEC) is a signal processing technology used to remove this echo. The core of an AEC utilizes adaptive filters that attempt to predict and cancel this echo by modeling the acoustic echo path between the speaker and the microphone. Oftentimes, the echo that remains after the operation of these adaptive filters and other associated echo reduction modules is audible, and additional suppression techniques are needed to remove this residual echo. 
         [0002]    A non-linear processor (NLP), also known as a microphone fading routine, is one such technique where the signal at the microphone is attenuated by an amount proportional to the strength of the signal at the speaker. In order to balance the tradeoff between suppressing residual echo and maintaining full-duplex behavior for the conversation, a conservative setting for the maximum NLP attenuation is used. This is because the signal captured at the microphone also contains speech from the local talker, and in cases where both the remote and local party are active, a strong NLP can distort the speech sent from the local talker&#39;s end. 
         [0003]    This conservative setting for NLP attenuation is inadequate for situations that commonly occur, that of an echo path change. Since the adaptive filters do not provide sufficient cancellation, a higher-than-normal suppression is desired from the NLP to remove the echo. Additionally, having a fixed maximum attenuation for the NLP that is set to satisfy broad categories of devices and scenarios is inadequate in suppressing echo for specific cases, where there is strong acoustic coupling between the speaker and microphone, for example. 
       SUMMARY 
       [0004]    The following presents a simplified summary in order to provide a basic understanding of some novel embodiments described herein. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
         [0005]    The disclosed architecture mitigates echo in voice communications using echo detection and adaptive management of attenuation by a non-linear processor (NLP). Suppression values provided by the NLP are determined based on echo detection and retained on a case-by-case basis to automatically increase or decrease the attenuation as needed. Feedback is incorporated that where the controls for the NLP attenuation is given by the amount of echo that remains, and this in turn affects the amount of echo that remains. 
         [0006]    The architecture operates in at least two stages. In one implementation, operation is in the sub-band domain as follows: in the first stage, multiple independent short length adaptive filters search for echo presence after all the echo reduction components of the acoustic echo cancellation (AEC), across a range of sub-bands and maximum expected echo delay values. These adaptive filters attempt to predict and cancel any echo that may remain based on the content in the speaker signal. Due to the adaptive nature of the search and that multiple filters are used, the method is robust to a wide variety of distortions. In the event significant cancellation is achieved in any of the sub-band filters, it is determined that echo is present after current echo reduction processing. In the second stage, the maximum attenuation of the NLP is adaptively changed based on the echo detection results from the first stage. This way the NLP attenuation automatically adjusts to the requirements. 
         [0007]    To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of the various ways in which the principles disclosed herein can be practiced and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  illustrates computer-implemented echo mitigation system in accordance with the disclosed architecture. 
           [0009]      FIG. 2  illustrates details of the adaptive filter array in searching for echo in the residual microphone signal. 
           [0010]      FIG. 3  illustrates a near end of a two-way voice communications system that employs the disclosed echo mitigation system as part of a two-way voice communications component. 
           [0011]      FIG. 4  illustrates a computer-implemented echo mitigation method. 
           [0012]      FIG. 5  illustrates additional aspects to the method of  FIG. 4 . 
           [0013]      FIG. 6  illustrates a method of adaptive NLP control where the maximum attenuation provided by the NLP is modified based on the echo detected in order to mitigate overall echo leak. 
           [0014]      FIG. 7  illustrates a method for determining echo in a residual microphone signal. 
           [0015]      FIG. 8  illustrates a method for determining if the echo in a residual microphone is strong echo. 
           [0016]      FIG. 9  illustrates a block diagram of a computing system operable to execute echo detection and adaptive NLP control in accordance with the disclosed architecture. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]    Acoustic echo cancellation (AEC) is a digital signal processing technology used to remove the echo from a speaker phone in two-way or multi-way communication system such as in traditional telephone or modern Internet audio conversation applications such as for voice-over-IP (VoIP). 
         [0018]    In a typical two-way communications system, the far-end voice is played to the near-end user through a loud speaker (or some other audio output device). A portion of the far-end voice is captured by the microphone in the near-end system, processed through the system, and then sent back to the far end. Thus, the far-end user will hear his voice with a certain delay, which is how echo forms. To distinguish from line/network echo that is caused by impedance mismatch, the echo referred to herein is called acoustic echo. 
         [0019]    The echo echo(t) in a two-way system can be modeled as a speaker signal convolved by a room response, which is also referred to as echo path. The echo path includes the effects of delays and phase changes caused by the slow speed of sound wave propagation through air and other materials. The echo path also includes multiple reflections of the sound wave off walls or other surfaces. The echo can be computed as follows: 
         [0000]    
       
         
           
             
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         [0000]    where g(t) is room response, and T e  is echo length that depends on size of the room and material of the reflection surfaces. 
         [0020]    The problem of echo cancellation is to find the room response, calculate echoes, and subtract echoes from the microphone signals. However, echo path is highly variable, so the filter g(t) cannot be fixed and cannot be calculated beforehand. 
         [0021]    A non-linear processor (NLP) is one such technique where the signal at the microphone is attenuated by an amount proportional to the strength of the signal at the speaker. The purpose of the NLP is to attempt removal of echo, and while doing so, find a balance between suppressing residual echo and maintaining full-duplex behavior for the conversation. In practice, fixed values are employed for how much compression the NLP module provides, which in turn, has a fixed cost on the near-end user voice. This solution may work where microphone and speaker specifications are precisely known, separation data for the microphone and speaker, and device properties, for example. However, fixed values for a general solution do not work since the near-end voice gets distorted, or is too soft, for example. 
         [0022]    Hence, the disclosed architecture describes an adaptive NLP algorithm that controls the maximum amount of NLP attenuation that needs to be applied based on the extent of echo leak. The adaptive NLP algorithm detects echo leaks more reliably and quickly as compared to the conventional techniques used for echo cancellation. 
         [0023]    In other words, the architecture provides echo presence detection after processing through all echo reduction components in the AEC, uses an echo reduction component in the form of the adaptive NLP algorithm to mitigate echo leak. 
         [0024]    Reference is now made to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the novel embodiments can be practiced without these specific details. In other instances, well known structures and devices are shown in block diagram form in order to facilitate a description thereof. The intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the claimed subject matter. 
         [0025]      FIG. 1  illustrates computer-implemented echo mitigation system  100  in accordance with the disclosed architecture. The system  100  comprises a filter component  102  for computing echo detection results  104  based on searching for echo in a residual microphone signal (micRes)  106  relative to content in a speaker signal. The system  100  also includes an attenuation adjustment component  108  for adaptively adjusting attenuation in a non-linear processor (NLP)  110  based on the echo detection results  104 . 
         [0026]    Additional aspects and description are provided herein below, and include the following. The filter component  102  and the attenuation adjustment component  108  are part of a two-way voice communications system that processes voice signals between a near-end user and a far-end user. The filter component  102  utilizes an array  112  of adaptive filters (e.g., short length adaptive filters) to search for echo in a residual microphone signal that is output after echo reduction processing. The filter component  102  searches cross a range of sub-bands and maximum expected echo delay values to cancel echo in the microphone signal based on the content in the speaker signal. 
         [0027]    The attenuation adjustment component  108  adaptively decreases a maximum attenuation in the NLP via a maximum attenuation control  114  in response to an absence of strong echo as indicated in the echo detection results  104 , and adaptively increases a maximum attenuation in the NLP via the maximum attenuation control  114  in response to a presence of strong echo as indicated in the echo detection results  104 . 
         [0028]    The filter component  102  determines that echo is present in a residual microphone signal based on cancellation of the echo by the filter array  112  (e.g., an array of independent short length adaptive filters). The filter component  102  computes that the echo is strong echo based on a signal-to-noise ratio (SNR) value of a final signal transmitted to a far end relative to an SNR threshold. 
         [0029]    Put another way, a computer-implemented echo processing system  100  can be part of a two-way voice communications system. The echo processing system  100  includes the filter component  102  for searching for echo in the residual microphone signal  106  using the adaptive filter array  112  of short length adaptive filters, and the attenuation adjustment component  108  for adaptively adjusting maximum attenuation in the NLP  110  based on results of the search. The maximum attenuation in the NLP  110  is adaptively adjusted using the maximum attenuation control  114 . 
         [0030]    The filter component  102  searches cross a range of sub-bands and maximum expected echo delay values to cancel echo in the residual microphone signal  106  based on content in the speaker signal. The attenuation adjustment component  108  adaptively decreases a maximum attenuation in the NLP  110  in response to an absence of echo and adaptively increases the maximum attenuation in response to a presence of echo. 
         [0031]    The filter component  102  predicts correlation between the residual microphone signal  106  and a speaker signal in a sub-band domain by using past samples of the speaker signal over a causal-delay search range and an acausal-delay search range. The filter component  102  computes a measure of prediction ability based on echo return loss enhancement (ERLE) of delay indices, a maximum of the ERLE for all the indices is applied against a threshold to determine if the echo exists in the residual microphone signal  106 . The filter component  102  computes that the echo is strong echo based on the SNR value, the SNR value computed based on the ratio between the final signal transmitted to the far end and the noise level transmitted to the far end, and the SNR value relative to an SNR threshold. 
         [0032]      FIG. 2  illustrates details of an echo mitigation system  200  that employs the adaptive filter array  112  in searching for echo in the residual microphone signal  106 . The filter component  102  includes the adaptive filter array  112  having short length adaptive filters (SLAFs)  202 . An adaptive algorithm is used to adaptively find the optimum filter g(t) that matches echo path. Here, the filter component  102  is depicted as including an SNR component  204  for computing the SNR values. Additionally, the system  200  includes noise suppression  206  and automatic gain control (AGC)  208  of the output of the NLP  110 , before the final output  210  is sent to the far end system, and also fed back to the SNR component  204 . Note that it is possible to have echo detection follow the NLP  110  as an adaptive NLP control, as well. 
         [0033]    In the sub-band approach, the microphone input signal is first passed through a bank of analysis filters h 0 , h 1 , h 2 , . . . , h K-1  and produces vectors of K sub-band signals; then, AEC is performed in each sub-band. At the end, the resultant sub-band error signals are passed through a bank of synthesis filters, and yield a full-band signal. In each sub-band, because signal bandwidth is reduced, the sampling rate can be reduced by a factor of M (decimation). For improved stop-band rejection, M is less than the number of bands K. 
         [0034]    Two advantages of the sub-band approach include a reduction in computational complexity because of the downsampling, and improved convergence rate because the sub-band signal spectra are more nearly uniform than the full-band signal. Therefore, the sub-band approach is used widely in acoustic echo cancellation applications. 
         [0035]    The speaker (spk) and microphone (mic) sub-band signals after timestamp based alignment are denoted as spk[n, k], mic[n, k] respectively, where n is the time (frame) index for sub-band processing, and k is the sub-band frequency (k=0, 1, . . . M-1). 
         [0036]    Non-linear processing is an echo reduction technique that follows all other echo reduction techniques. Non-linear processing is used to remove any residual echo that remains, and in practice is employed in most cases, since in typical operating environments the residual echo is sufficiently perceptible. 
         [0037]    The NLP  110  operates to attenuate the microphone signal whenever there is strong signal activity on the speaker, the amount of attenuation being dependent on the strength of the speaker signal. 
         [0038]    This directly leads to a half-duplex-like nature of communication (the NLP  110  behaving as a gentle microphone-mute based voice switch). The NLP  110  operates in the sub-band (or full-band) domain by attenuating the residual microphone signal (following all other echo reduction components) by applying the following gain to the residual microphone signal: 
         [0000]      10̂(− NLP _max_attenuation/20)*log10(spkPow[ n, k ]/spkPowMin[ n, k ])/log10(spkPowMax[ n, k ]/spkPowMin[ n, k ]), 
         [0000]    where, NLP_max_attenuation is the maximum attenuation (echo reduction) provided by the NLP  110  in decibels (dB); spkPow[n, k] is the power in the corresponding sub-band of the speaker (possibly averaged over some frames); and, spkPowMin[n, k] and spkPowMax[n, k] track respectively the minimum and maximum values of spkPow[n,k] in short term history (these can be set to fixed values). 
         [0039]    In case spkPow[n, k]&lt;spkPowMin[n, k], no attenuation is applied by the NLP  110 . In case spkPow[n, k]&gt;spkPowMax[n, k], NLP_max_attenuation dB of attenuation is provided by the NLP  110  for the sub-band ‘k’ in the frame ‘n’ in the microphone signal (after all other echo reduction techniques). 
         [0040]    With respect to detecting presence of echo in the sub-band domain, filter component attempts to predict any correlation between the speaker signal and the microphone signal after passing through all other echo reduction components (the adaptive filter described previously being one such component) except NLP, using SLAFs (e.g., hereafter fixed to be of length  3 ). 
         [0041]    Since the true physical delay between the timestamp adjusted speaker and microphone sub-band samples can be positive or negative, a search for this delay is performed by using the speaker signal delayed in either direction. The microphone signal, after passing through all previous echo reduction components, is referred to as the microphone residual signal (micRes[n, k] in the sub-band domain). The procedure is:
       Predict micRes[n, k] using past samples of the speaker signals {spk[n−d, k], spk[n−d−1, k], spk[n−d−2, k]} as micRes[n,k, d], where d=0, 1, 2, . . . L 0 , and L 0  is the causal-delay search range.   Predict micRes[n, k] using {spk[n+d, k], spk[n+d−1, k], spk[n+d−2, k]} as micRes[n,k, −d], where d=1, 2, . . . L 1 , and L 1  is the acausal-delay search range.       
 
         [0044]    As a measure of the prediction ability, the following quantity (expanded as echo return loss enhancement (ERLE)) is computed (illustrated only for the causal-delay case, the computation for the acausal case is similar): 
         [0000]        ERLE ( d, k )=var(micRes[ n, k ])/var(micRes[ n, k ]−micRes[ n, k, d ]) 
         [0000]    where the variance var( ) is computed using recursive averaging over time, and micRes[n, k, d] is the predicted micRes[n, k] by the adaptive filter using {spk[n−d, k], spk[n−d−1, k], spk[n−d−2, k]} 
         [0045]    Furthermore, for a given delay index d, the representative ERLE is computed as ERLE(d)=max k (ERLE(d, k)), where k ranges over the sub-bands considered. ERLE(d) is then averaged over time to smooth out statistical variations. 
         [0046]    In order to determine if there is any echo in the residual microphone signal, an overall ERLE value is calculated as the maximum of all ERLE(d) (considering both causal and acausal delay values). If this value exceeds a threshold (e.g., 3 dB), echo is declared to be present in the residual microphone signal; otherwise, the decision is that there is no echo present over the range of delay values considered. Furthermore, strong echo is declared to be present if echo is detected according to the above described criteria and the SNR of the final signal that is transmitted to the far-end exceeds a threshold (e.g., 3 dB). 
         [0047]      FIG. 3  illustrates a near end  300  of a two-way voice communications system that employs the disclosed echo mitigation system  100  as part of a two-way voice communications component  302 . The far end can be exactly the same. In such system, the far-end voice from the far end is received over a network  304  is decoded using a voice decoder  306  and then played through a loud speaker  308  to the near-end user  310 . The far-end voice is also leaked over to (along with room response), captured by a microphone  312 , and sent back to the far end through prior echo reduction components  314  (e.g., AEC), the echo mitigation system  100  as an improved echo reduction component, and a voice encoder  316 . Thus, if the prior (or previous) echo reduction components  314  do not remove all echoes, the far-end user will hear his voice with a certain delay, thereby generating acoustic echo (to differentiate from line/network echo that is caused by impedance mismatch). As indicated, the echo mitigation system  100  precedes the NLP but follows all prior echo reduction components  314 . 
         [0048]    Included herein is a set of flow charts representative of exemplary methodologies for performing novel aspects of the disclosed architecture. While, for purposes of simplicity of explanation, the one or more methodologies shown herein, for example, in the form of a flow chart or flow diagram, are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel implementation. 
         [0049]      FIG. 4  illustrates a computer-implemented echo mitigation method. At  400 , a residual microphone signal is analyzed for echo. At  402 , attenuation of an NLP is adaptively adjusted based on strength of the echo detected. At  404 , the attenuation is applied to the residual microphone signal. 
         [0050]      FIG. 5  illustrates additional aspects to the method of  FIG. 4 . At  500 , echo is searched in the residual microphone signal using short length adaptive filters. At  502 , searching is across a range of sub-bands and maximum expected echo delay values to cancel echo in the residual microphone signal based on the content in a speaker signal. At  504 , analysis is performed for strong echo based on a SNR value relative to a final signal for transmission to a far-end user. At  506 , the attenuation is adaptively decreased in the NLP in response to an absence of strong echo in the residual microphone signal. At  508 , the attenuation is adaptively increased in the NLP in response to a presence of strong echo in the residual microphone signal. 
         [0051]      FIG. 6  illustrates a method of adaptive NLP control where the maximum attenuation provided by the NLP is modified based on the echo detected in order to mitigate overall echo leak. At  600 , a check for the presence of echo is performed based on the inputs of microphone residual signal and speaker signal. At  602 , a further check is made as to the strength of the echo. At  604 , if strong echo is present, flow is to  606  to increase the NLP maximum attenuation by a fixed amount. At  608 , suitable limits are then set on the maximum NLP attenuation, and applied. At  610 , other processing can be performed, such as noise suppression, AGC, and so on. Flow is then back to  602  to again check for the strength of echo. This is a continuing process during the communication. If strong echo is not detected at  604 , flow is then to  612  to decrease the maximum NLP attenuation by a fixed amount. Flow is to  608  to set the suitable limits based on this decrease, and then apply the attenuation. 
         [0052]      FIG. 7  illustrates a method for determining echo in a residual microphone signal. At  700 , an overall ERLE value is computed. The overall value is computed as a maximum of all ERLE of the delay indices. At  702 , the overall ERLE value is compared to an ERLE threshold. At  704 , if the overall value exceeds the threshold, flow is to  706  where echo is declared to be present in the residual microphone signal. On the other hand, if the overall value does not exceed the threshold, flow is from  704  to  708  where it is determined that no echo is detected. 
         [0053]      FIG. 8  illustrates a method for determining if the echo in a residual microphone is strong echo. At  800 , echo is detected to be in the residual microphone signal. At  802 , the SNR ratio is computed for the final signal send over the network to the far end. At  804 , the SNR is compared against a threshold. At  806 , if the SNR exceeds the threshold, flow is to  808  to determine that strong echo is present in the microphone residual signal. On the other hand, if the SNR does not exceed the threshold, flow is from  806  to  810  where the echo is considered not to be strong. 
         [0054]    As used in this application, the terms “component” and “system” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical, solid state, and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. The word “exemplary” may be used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. 
         [0055]    Referring now to  FIG. 9 , there is illustrated a block diagram of a computing system  900  operable to execute echo detection and adaptive NLP control in accordance with the disclosed architecture. In order to provide additional context for various aspects thereof,  FIG. 9  and the following discussion are intended to provide a brief, general description of the suitable computing system  900  in which the various aspects can be implemented. While the description above is in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that a novel embodiment also can be implemented in combination with other program modules and/or as a combination of hardware and software. 
         [0056]    The computing system  900  for implementing various aspects includes the computer  902  having processing unit(s)  904 , a system memory  906 , and a system bus  908 . The processing unit(s)  904  can be any of various commercially available processors such as single-processor, multi-processor, single-core units and multi-core units. Moreover, those skilled in the art will appreciate that the novel methods can be practiced with other computer system configurations, including minicomputers, mainframe computers, as well as personal computers (e.g., desktop, laptop, etc.), hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
         [0057]    The system memory  906  can include volatile (VOL) memory  910  (e.g., random access memory (RAM)) and non-volatile memory (NON-VOL)  912  (e.g., ROM, EPROM, EEPROM, etc.). A basic input/output system (BIOS) can be stored in the non-volatile memory  912 , and includes the basic routines that facilitate the communication of data and signals between components within the computer  902 , such as during startup. The volatile memory  910  can also include a high-speed RAM such as static RAM for caching data. 
         [0058]    The system bus  908  provides an interface for system components including, but not limited to, the memory subsystem  906  to the processing unit(s)  904 . The system bus  908  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), and a peripheral bus (e.g., PCI, PCIe, AGP, LPC, etc.), using any of a variety of commercially available bus architectures. 
         [0059]    The computer  902  further includes storage subsystem(s)  914  and storage interface(s)  916  for interfacing the storage subsystem(s)  914  to the system bus  908  and other desired computer components. The storage subsystem(s)  914  can include one or more of a hard disk drive (HDD), a magnetic floppy disk drive (FDD), and/or optical disk storage drive (e.g., a CD-ROM drive DVD drive), for example. The storage interface(s)  916  can include interface technologies such as EIDE, ATA, SATA, and IEEE 1394, for example. 
         [0060]    One or more programs and data can be stored in the memory subsystem  906 , a removable memory subsystem  918  (e.g., flash drive form factor technology), and/or the storage subsystem(s)  914  (e.g., optical, magnetic, solid state), including an operating system  920 , one or more application programs  922 , other program modules  924 , and program data  926 . 
         [0061]    The one or more application programs  922 , other program modules  924 , and program data  926  can include the components and entities of the system  100  of  FIG. 1 , the SLAFs  200  of the adaptive filter array  112 , components and entities of the near end  300  of  FIG. 3 , and the methods represented in the flow charts of  FIGS. 4-8 , for example. 
         [0062]    Generally, programs include routines, methods, data structures, other software components, etc., that perform particular tasks or implement particular abstract data types. All or portions of the operating system  920 , applications  922 , modules  924 , and/or data  926  can also be cached in memory such as the volatile memory  910 , for example. It is to be appreciated that the disclosed architecture can be implemented with various commercially available operating systems or combinations of operating systems (e.g., as virtual machines). 
         [0063]    The storage subsystem(s)  914  and memory subsystems ( 906  and  918 ) serve as computer readable media for volatile and non-volatile storage of data, data structures, computer-executable instructions, and so forth. Computer readable media can be any available media that can be accessed by the computer  902  and includes volatile and non-volatile media, removable and non-removable media. For the computer  902 , the media accommodate the storage of data in any suitable digital format. It should be appreciated by those skilled in the art that other types of computer readable media can be employed such as zip drives, magnetic tape, flash memory cards, cartridges, and the like, for storing computer executable instructions for performing the novel methods of the disclosed architecture. 
         [0064]    A user can interact with the computer  902 , programs, and data using external user input devices  928  such as a keyboard and a mouse. Other external user input devices  928  can include a microphone, an IR (infrared) remote control, a joystick, a game pad, camera recognition systems, a stylus pen, touch screen, gesture systems (e.g., eye movement, head movement, etc.), and/or the like. The user can interact with the computer  902 , programs, and data using onboard user input devices  930  such a touchpad, microphone, keyboard, etc., where the computer  902  is a portable computer, for example. These and other input devices are connected to the processing unit(s)  904  through input/output (I/O) device interface(s)  932  via the system bus  908 , but can be connected by other interfaces such as a parallel port, IEEE 1394 serial port, a game port, a USB port, an IR interface, etc. The I/O device interface(s)  932  also facilitate the use of output peripherals  934  such as printers, audio devices, camera devices, and so on, such as a sound card and/or onboard audio processing capability. 
         [0065]    One or more graphics interface(s)  936  (also commonly referred to as a graphics processing unit (GPU)) provide graphics and video signals between the computer  902  and external display(s)  938  (e.g., LCD, plasma) and/or onboard displays  940  (e.g., for portable computer). The graphics interface(s)  936  can also be manufactured as part of the computer system board. 
         [0066]    The computer  902  can operate in a networked environment (e.g., IP) using logical connections via a wired/wireless communications subsystem  942  to one or more networks and/or other computers. The other computers can include workstations, servers, routers, personal computers, microprocessor-based entertainment appliance, a peer device or other common network node, and typically include many or all of the elements described relative to the computer  902 . The logical connections can include wired/wireless connectivity to a local area network (LAN), a wide area network (WAN), hotspot, and so on. LAN and WAN networking environments are commonplace in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network such as the Internet. 
         [0067]    When used in a networking environment the computer  902  connects to the network via a wired/wireless communication subsystem  942  (e.g., a network interface adapter, onboard transceiver subsystem, etc.) to communicate with wired/wireless networks, wired/wireless printers, wired/wireless input devices  944 , and so on. The computer  902  can include a modem or has other means for establishing communications over the network. In a networked environment, programs and data relative to the computer  902  can be stored in the remote memory/storage device, as is associated with a distributed system. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used. 
         [0068]    The computer  902  is operable to communicate with wired/wireless devices or entities using the radio technologies such as the IEEE 802.xx family of standards, such as wireless devices operatively disposed in wireless communication (e.g., IEEE 802.11 over-the-air modulation techniques) with, for example, a printer, scanner, desktop and/or portable computer, personal digital assistant (PDA), communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi (or Wireless Fidelity) for hotspots, WiMax, and Bluetooth™ wireless technologies. Thus, the communications can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.11x (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wire networks (which use IEEE 802.3-related media and functions). 
         [0069]    What has been described above includes examples of the disclosed architecture. It is, of course, not possible to describe every conceivable combination of components and/or methodologies, but one of ordinary skill in the art may recognize that many further combinations and permutations are possible. Accordingly, the novel architecture is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.