Patent Publication Number: US-2006020449-A1

Title: Method and system for generating colored comfort noise in the absence of silence insertion description packets

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
      This application claims priority from provisional applications Ser. No. 60/297,265, filed Jun. 12, 2001 and Ser. No. 60/305,157, filed Jul. 16, 2001, which are incorporated by reference. 
    
    
     FIELD OF THE INVENTION  
      The present invention relates generally to enhancing bandwidth efficiency and to voice transmission and silence compression and, more particularly, for generating colored comfort noise in absence of silence insertion description packets containing spectrum information.  
     BACKGROUND OF THE INVENTION  
      Digital Subscriber Line (DSL, Digital Subscriber Loop, xDSL) involves a technology that enables high-speed transmission of digital data over traditional copper telephone lines. This technology involves digital telecommunications protocols designed to allow high-speed data communication over existing copper telephone lines between end-users and telephone companies.  
      When two conventional modems are connected through the telephone system (e.g., Public Switched Telephone Network (PSTN)), the communication may be treated the same as voice conversations. This has the advantage that there is no investment required from the telephone company (telco) but the disadvantage is that the bandwidth available for the communication is the same as that available for voice conversations, usually 64 kb/s (DS0) at most. The twisted-pair copper wires into individual homes or offices can usually carry significantly more than 64 kb/s, provided the telco handles the signal as digital rather than analog.  
      There are many implementations of the basic scheme, differing in the communication protocol used and providing varying service levels. The throughput of the communication can be anything from about 128 kb/s to over 8 Mb/s, the communication can be either symmetric or asymmetric (i.e., the available bandwidth may or may not be the same upstream and downstream). Equipment prices and service fees also vary considerably.  
      In many different kinds of modern telecommunications equipment, an important element is a voice processing subsystem, which may perform such functions as transcoding, Dual Tone Modulation Frequency (DTMF) processing, echo cancellation, etc. Examples of equipment requiring voice processing of this kind include everything from speakerphones, to Global System for Mobile communications (GSM) basestations, to broadband integrated access devices. Voice processing subsystems may be Digital Signal Processing (DSP) based and feature a set of algorithm implementations in software. These algorithms may be hand-coded in assembly-code form by algorithmic and DSP-programming experts. Also, an easy way to combine the required algorithms in the required combinations and then interface to the voice processing subsystem through a simple external interface is desired.  
      Voice over Digital Subscriber Line (VoDSL) involves leveraging copper infrastructure to provide quality voice services and support a wide variety of data applications over an existing line to a customer. VoDSL implements DSL platform in conjunction with platform adaptations that enable voice services. It further gives data competitive local exchange carriers (CLECs) a way to increase revenue potential, incumbent local exchange carriers (ILECs) an answer to the cable modem, and interexchange carriers (IXCs) a way to gain access to the local voice loop. Thus, any carrier type may increase the value of services available through VoDSL.  
      Generally, VoDSL involves a voice gateway, an integrated access device (IAD), among other components. The voice gateway may provide voice packets that are depacketized and converted to a format for delivery to a voice switch or other similar device. The voice gateway may enable traffic to be accessed from a data network and forwarded to PSTN for service and switching. The IAD may serve as a DSL modem and perform other functionality. The IAD may serve as an interface between a DSL network service and a customer&#39;s voice and data equipment. The IAD may provide the interface between the DSL network service and a customer&#39;s network equipment. Further, an IAD may be used to connect voice and data enabled equipment.  
      VoDSL may also be transmitted via Internet Protocol (IP). VoIP may be defined as voice over Internet Protocol, which includes any technology that enables voice telephony over IP networks. Some of the challenges involved with VoIP may include delivering the voice, fax or video packets in a dependable manner to a user. This may be accomplished by taking the voice or data from a source where it is digitized, compressed due to the limited bandwidth of the Internet, and sent across the network. The process may then be reversed to enable communication by voice. VoIP enables users, including companies and other entities, to place telephony calls over IP networks, instead of PSTN.  
      A consideration associated with the use of VoDSL, VoIP and other voice applications involves silence suppression which may be used to enhance bandwidth and throughput. Silence suppression removes the necessity of packetizing the silence portion of a phone conversation (e.g., when no one is talking). To optimize bit-rates in simultaneously transmitting voice and data information, a voice signal detector detects silence portions of the speech signal. Rather than transmit the silence portion of the voice signal, data (e.g., silence insertion descriptor) may be inserted into the packet stream thereby recovering bandwidth that would otherwise be allocated for voice traffic. While providing effective bit-rate reduction, the deletion of background noise that typically accompanies the “silence” portions of the voice data has the undesired effect on the person receiving and listening to the voice data of absolute silence and the perception of on/off transmission rather than a continuous connection.  
      In conjunction with silence suppression, comfort noise generation may be implemented to reconstruct or construct and replace the silence part of speech and other voice signals. A drawback associated with conventional comfort noise generators is that they require a large MIPS (million instructions per second) and memory capacity and reduce efficiency and effective voice transmission.  
      Existing International Telecommunications Union (ITU) recommendation G. series G729AB uses a simpler approach for the gaussian noise generation, which has the drawback of periodicity. Other generators are more MIPS intensive and are not generally suitable for real time systems or the complexity is not warranted.  
      Gaussian white noise generators may be implemented in applications involving synthesizing speech and other voice signals. One of the ways in which the gaussian generator may be implemented may include using a central limit theorem on a uniform random generator. However, this has a drawback of periodicity especially when dealing with the long-term generation of constant amplitude speech, noise signal or other applications. Other generators are more MIPS intensive and are not generally suitable for real time systems or the complexity is not warranted.  
      Typically there are very tight latency requirements on telecommunications devices, as excessive latency degrades the quality of a telephone conversation. Consequently, signal processing algorithms used in telecommunications often have to execute on very small blocks of voice data. For example, in VoDSL Customer Premise Equipment (CPE), the Digital Signal Processor operates on 4 sample blocks of 8 kHz data.  
      An advanced feature of voice compression in voice over data network systems is adaptive silence compression and reconstruction. One aspect of this feature is that a simulated background noise signal is generated by filtering white gaussian noise with a filter intended to spectrally shape the noise to closely match a ‘true’ background noise, which was not transmitted in order to save bandwidth.  
      The filter coefficients, however, do not necessarily contain the correct gain, so the resultant signal is not the same power as the true background noise. Also the excitation to the filter generally has some gain which causes the output to be of a different gain from that of the true background noise. In addition, an efficient generation of the simulated signal may only generate four samples at a time, making it difficult (and computationally expensive, given that this function is called approximately 2000 times per second) to measure the signal strength and compensate the gain accordingly.  
      Therefore, there is a need in the art of VoDSL and VoIP for a more efficient method and system for transmitting voice signals.  
     SUMMARY OF THE INVENTION  
      Aspects of the present invention overcome the problems noted above, and realize additional advantages. One such inventive aspect provides methods and systems for implementing a low complexity spectrum estimation technique for comfort noise generation. One aspect of this invention is the manner of estimating the signal spectrum and generating comfort noise (CN) with reduced complexity as compared to existing methods. Another aspect of this invention involves segregating filter parameter encoding from the adaptation process for transmission in the form of silence insertion descriptors. In systems where MIPS and memory are expensive, the invention employs a method, which utilizes the fact that the signal spectrum essentially stays constant over an extended period of time and the method adapts to the spectrum over time. This has an advantage in that the comfort noise generated is a more realistic representation of the input noise and the comfort noise generated is uniform. The segregation of filter parameter encoding for transmission offers enhanced flexibility as such a separation leads to greater interoperability between various systems. Another benefit is that the MIPS and memory are more efficiently used.  
      Further, existing ITU recommendation G. series G729AB uses a different approach for comfort noise generation (CNG), which approach requires a high level of MIPS and memory. Various other implementations for CNG exist. This inventive aspect of the present invention has, for example, one or more of the following advantages over such approaches: a more pleasing colored comfort noise (as opposed to white) is generated; a less complex algorithm is utilized having a reduced demand for MIPS and memory, which are critical elements in real time systems; and filter parameter encoding (into reflection coefficients) is done independent of the adaptation process, which affords greater flexibility of using the MIPS only when necessary, which allows the filter parameters to be encoded into some other form of encoding, while the fundamental algorithm remains the same (the only change would be to the encoding algorithm)  
      Another inventive aspect of the present invention provides methods and systems for implementing a simple gaussian white noise generator for real time speech synthesis. This inventive aspect involves a method to generate gaussian random noise having a very long period without much computation complexity for fixed point systems. When synthesizing speech a gaussian random noise generator is required. For simplicity, such a sequence is achieved by using a pseudo-random sequence generator and then using central limit theorem. When the period of the pseudo-random generator is limited, as is often the case, this form of noise generation leads to periodic artifacts, especially when synthesizing a stable spectrum signal. This inventive aspect employs a means to overcome this drawback, without compromising on the simplicity of the application.  
      Another inventive aspect of the present invention provides methods and systems for implementing colored comfort noise generation in absence of silence insertion description packets containing spectrum information. This inventive aspect involves a means of generating colored comfort noise in absence of Silence Insertion Descriptor (SID) packets containing spectrum estimation. In voice communications systems, where the bandwidth utilization of a voice call is to be minimized, voice activity detection and silence compression is used to decrease the bandwidth used during non-voiced segments of a conversation. Bandwidth is saved by sending little or no information about the non-voiced audio, in SID packets. On the receiving end of the conversation, the silence has to be synthesized. If spectral information about the non-voiced background signal is not transmitted, then the synthesized background signal typically does not have the same spectral characteristics of the true background noise. This causes unpleasant sounding differences in the background noise when someone is speaking versus when they are not speaking.  
      Some silence compression schemes enable the transmission of information describing the spectral characteristics of the background noise. Other techniques may only provide the background noise power level, or no information whatsoever about the background noise. When the spectral information is not contained in the SID, the decoder has no information from which to generate spectrally adaptive background noise. There are various system design considerations that may prevent the SID from containing spectral information, including low complexity and interoperability. Low complexity relates to the simplicity of the equipment on the transmitting side, which prevents generation of SIDs containing spectral information. Interoperability relates to the existence of several standards in which there are well-defined SIDs that contain only the background noise power, or no information about the background noise. This inventive aspect generates colored noise reflecting the spectrum of the actual noise in the absence of SID packets containing spectral information.  
      Another inventive aspect of the present invention provides methods and systems for determining filter gain and automatic gain control for fixed point low delay algorithms in real time systems. This inventive aspect involves computing the gain of a filter using an approximate calculation by filtering a signal similar in spectrum to the input to be filtered and then fine-tuning it, based on a short term moving mean square calculation in the low delay, low MIPS state of the algorithm.  
      Typically there are very tight latency requirements on telecommunications devices, as excessive latency degrades the quality of a telephone conversation. Consequently, signal processing algorithms used in telecommunications often have to execute on very small blocks of voice data. For example, in Voice over DSL CPE, the Digital Signal Processor operates on 4 sample blocks of 8 kHz data. An advanced feature of voice compression in voice over data network systems is adaptive silence compression and reconstruction. One aspect of this feature is that a simulated background noise signal is generated by filtering white gaussian noise with a filter intended to spectrally shape the noise to closely match a ‘true’ background noise, which was not transmitted in order to save bandwidth. The filter coefficients, however, do not necessarily contain the correct gain, so the resultant signal is not the same power as the true background noise. Also, the excitation to the filter would have some gain, which causes the output to be of a different gain from that of the true background noise. In addition, an efficient generation of the simulated signal would only generate 4 samples at a time, making it difficult (and computationally expensive, given that this function is called 2000 times per second) to measure the signal strength and compensate the gain accordingly. This inventive aspect provides a method of controlling the output gain using lower MIPS compared to a brute force calculation of the gain and then scaling output based on that gain. This invention is especially applicable in single sample (or few samples) input scenarios.  
      According to an embodiment of the present invention, a method for generating comfort noise comprises the steps of identifying a plurality of silence packets in speech data at a decoder side; adapting to the plurality of silence packets in the speech data by using an adaptation algorithm that adapts with time; determining a start of a silence segment; and generating comfort noise by the adaptation algorithm at the start of the silence segment.  
      According to another embodiment of the present invention, a system for generating comfort noise comprises an identifier for identifying a plurality of silence packets in speech data at a decoder side; an adaptation algorithm for adapting to the plurality of silence packets in the speech data wherein the adaptation algorithm adapts with time; a determinator for determining a start of a silence segment; and a comfort noise generator for generating comfort noise by the adaptation algorithm at the start of the silence segment.  
      The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various embodiments of the invention and, together with the description, serve to explain the principles of the invention.  
      List of Acronyms  
     
         
          AAL—ATM Adaption Layer  
          ADSI—Analog Display Services Interface  
          ADSL—Asymmetric Digital Subscriber Line  
          AGC—Automatic Gain Control  
          ASICs—Application-Specific Integrated Circuits  
          ATM—Asynchronous Transfer Mode  
          BUN—Broadband Unified Framework  
          CBR—Constant Bit Rate  
          CIDCW—Caller Identifier On Call Waiting  
          CLECs—Competitive Local Exchange Carriers  
          CN—Comfort Noise  
          CNG—Comfort Noise Generation  
          CO—Central Office  
          CO/DLC—Central Office/Digital Loop Carrier  
          CPCS—Common Part Convergence Sublayer  
          CPE—Customer Premise Equipment  
          CRC—Cyclic Redundancy Check  
          CS-ACELP—Conjugate-Structure Algebraic-Code-Excited Linear-Predictive  
          DLCI—Data Link Connection Identifier  
          DSL—Digital Subscriber Line  
          DSL PHY—Digital Subscriber Line Physical Layer Device  
          DSLAM—Digital Subscriber Line Access Multiplexer  
          DSP—Digital Signal Processing  
          DSVD—Digital Simultaneous Voice and Data  
          DTM—Dual Tone Modulation  
          DTMF—Dual Tone Modulation (or Multi) Frequency  
          ECSR—Echo Canceller with Single Reflector  
          EEPROM—Electrically Erasable Programmable Read Only Memory  
          EPD—Early Packet Discard  
          GSM—Global System for Mobile  
          IAD—Integrated Access Device  
          IADs—Integrated Access Devices  
          IETF—Internet Engineering Task Force  
          ILECs—Incumbent Local Exchange Carriers  
          IMA—Inverse Multiplexing over ATM  
          IP—Internet Protocol  
          ISOS—Integrated Software On Silicon™ 
          ISP—Internet Service Provider  
          ITU—International Telecommunications Union  
          IXCs—Interexchange Carriers  
          L—Length  
          LMS—Least Mean Square  
          MIPS—Million Instructions Per Second  
          NAT—Network Address Translation  
          NLMS—Normalized Least Mean Square  
          NRT—Non Real Time  
          OAM—Operations and Management  
          OSI—Open Systems Interconnection  
          PBX&#39;s—Private Branch Exchange&#39;s  
          PC—Personal Computer  
          PCP/IP—Transmission Control Protocol on top of the Internet Protocol  
          PDU—Protocol Data Unit  
          PPD—Partial Packet Discard  
          PPoA—Point to Point Protocol over ATM  
          PPPoE—Point to Point Protocol over Ethernet  
          PPTP—Point Tunneling Protocol  
          PSTN—Public Switched Telephone Network  
          RMS—Root Mean Square  
          RT—Real Time  
          RTP—Real-Time Transport Protocol  
          SDRAM—Synchronous Dynamic Random Access Memory  
          SDSL—Symmetric Digital Subscriber Line  
          SF—Scale Factor  
          SID—Silence Insertion Descriptors  
          SNMP—Simple Network Management Protocal  
          SOHO—Small Office/Home Office  
          SSCS—Service Specific Convergence Sublayer  
          SVCs—Switched Virtual Circuits  
          UNI—User Network Interface  
          USB—Universal Serial Bus  
          V—Volt  
          VAGC—Voice Activity Detection with Automatic Gain Control  
          VBR—Variable Bit Rate  
          VoDSL—Voice over Digital Subscriber Line  
          VPI/VCI—Virtual Path Identifier/Virtual Channel Identifier  
          WAN—Wide Area Network 
       
    
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The present invention can be understood more completely by reading the following Detailed Description of the Invention, in conjunction with the accompanying drawings, in which:  
       FIG. 1  is a flowchart illustrating an example of an encoder, according to an embodiment of a first aspect of the present invention.  
       FIG. 2  is a flowchart illustrating another example of an encoder, according to an embodiment of the first aspect of the present invention.  
       FIG. 3   a  is an example of a system for implementing multiple generators, according to an embodiment of a second aspect of the present invention.  
       FIG. 3   b  is a block diagram illustrating an example of a speech synthesis filter, according to an embodiment of a second aspect of the present invention.  
       FIG. 4   a  is a flowchart illustrating an example of a decoder, according to an embodiment of a third aspect of the present invention.  
       FIG. 4   b  is an example of a system for implementing decoder process, according to an embodiment of a third aspect of the present invention.  
       FIG. 4   c  is an example of a system for generator background noise, according to an embodiment of a third aspect of the present invention.  
       FIG. 5  is a block diagram illustrating an example of a filter, according to an embodiment of a fourth aspect of the present invention.  
       FIG. 6  is a block diagram illustrating an example of a filter, according to an embodiment of a fourth aspect of the present invention.  
       FIG. 7  is a flowchart illustrating an example of a process for fine tuning automatic gain control, according to an embodiment of a fourth aspect of the present invention.  
       FIG. 8  illustrates an example of a system using CNG on an encode side, according to an embodiment of the present invention.  
       FIG. 9  illustrates an example of a system using CNG on an encode side when SID is not sent, according to an embodiment of the present invention.  
       FIG. 10  illustrates an example of a system using CNG on a decode side, according to an embodiment of the present invention.  
       FIG. 11  illustrates an example of a system using CNG on a decode side, according to an embodiment of the present invention.  
       FIG. 12  illustrates a flowchart for a CNG_adapt function, according to an embodiment of the present invention.  
       FIG. 13  illustrates a flowchart for a CNG_generate function, according to an embodiment of the present invention.  
       FIG. 14  illustrates a flowchart for a CNG_decode function, according to an embodiment of the present invention.  
       FIG. 15  is a schematic drawing of a software architecture in which the inventive aspects of the present invention may be incorporated.  
       FIG. 16  is a schematic drawing of a software architecture in which the inventive aspects of the present invention may be incorporated.  
       FIG. 17  is a schematic drawing of a hardware architecture in which the inventive aspects of the present invention may be incorporated.  
       FIG. 18  is a schematic diagram of a hardware architecture in which the inventive aspects of the present invention may be incorporated.  
       FIG. 19  is a schematic diagram of a software architecture in which the inventive aspects of the present invention may be incorporated. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
      The following description is intended to convey a thorough understanding of the invention by providing a number of specific embodiments and details involving VoDSL and VoIP applications. It is understood, however, that the invention is not limited to these specific embodiments and details, which are exemplary only. It is further understood that one possessing ordinary skill in the art, in light of known systems and methods, would appreciate the use of the invention for its intended purposes and benefits in any number of alternative embodiments, depending upon specific design and other needs.  
      According to an embodiment of the present invention, a low complexity spectrum estimation technique for comfort noise generation may be provided. A comfort noise generator (CNG) may be implemented to compress and reconstruct the silence part of speech signals. CNG may work with any voice activity detector, an echo canceller or other similar device to compress silence or generate comfort noise. The present invention provides a simplified technique for estimating a signal spectrum to generate comfort noise.  
      One aspect of the present invention involves estimating the signal spectrum and generating comfort noise (CN) with less complexity as compared to existing methods. Another aspect of the present invention may involve the segregation of filter parameter encoding from an adaptation process, for transmission in the form of silence insertion descriptors.  
      In systems where Million Instructions Per Second (MIPS) and memory are expensive, the method of the present invention utilizes the fact that the signal spectrum essentially stays constant for an extended period of time where the method may adapt to the spectrum over a predetermined period of time. As a result, the comfort noise may be generated as a more realistic representation of the input noise. Further, the comfort noise generated may be more uniform.  
      According to another embodiment of the present invention, the segregation of filter parameter encoding for transmission may offer enhanced flexibility. For example, greater interoperability between various systems may be recognized. In addition, the MIPS and memory may be efficiently used.  
      The present invention may generate a more pleasing colored comfort noise (as opposed to white, for example). The present invention may involve a less complex algorithm and saves MIPS and memory, which are critical elements in real time systems. Filter parameter encoding (into reflection coefficients, for example) may be accomplished independently of the adaptation process, which provides greater flexibility of using the MIPS only when necessary. In another example, if the filter parameters are to be encoded into some other form of encoding, the fundamental algorithm may remain constant or essentially the same. Thus, in this example, the only change would be to the encoding algorithm.  
       FIG. 1  illustrates a flowchart of an encoder, according to an embodiment of the present invention. This mode of operation may be used when a vocoder (or other similar device) may not have associated or built-in silence compression capacity. To improve the compression of the system, CNG may adapt to the background noise perceived between portions of speech data and create silence insertion descriptors (SID) representative of characteristics of the perceived noise, when the speech is inactive, as illustrated in  FIG. 1 .  
      Input data, including voice and silence/background data, is received, at step  110 . At step  112 , “near end” speech activity, i.e., that portion of speech or voice data at the front end or beginning of the voice/speech data, is determined. If a positive response is elicited, then G7xx encoding occurs, at step  114 . Further, codeword data is sent to the channel (transmitted to the decoder) at step  116 , and the state of the system may the be returned to receive input data, at step  110 . If a negative response is elicited, Comfort Noise Generator adaptation occurs, at step  118 . Filter Parameter encoding then sends SID to the channel (transmitted to the decoder), at step  120 , and the state of the system may then be returned to receive input data, at step  110 . In short,  FIG. 1  illustrates a manner in which the input data may be classified as speech or silence and accordingly where speech codeword or SID are sent respectively to the channel to be transmitted to the decoder. The system of  FIG. 1  may be used in section  1540  of a DSP chip, as shown in  FIG. 15 , as discussed below.  
       FIG. 2  illustrates a flowchart of an alternative encoder when SID is not sent, according to another embodiment of the present invention. The system of  FIG. 2  may be implemented in section  1540  of a DSP chip, as shown in.  FIG. 15 , as discussed below. When near end speech is inactive, CNG adapts and generates noise, which may be encoded by a vocoder. This mode may be used when residual echo and noise combination is perceptually unpleasant. CNG may generate perceptually enhanced noise based on the average spectrum of the input.  
      Input data may be received at step  210 . At step  212 , it may be determined whether near end speech is active or not. If near end speech is inactive, comfort noise may be adapted and generated, as illustrated at step  214 . G7xx encoding may occur at step  216 . Further, codeword data may be sent and forwarded to input data, at step  210 .  
      According to an embodiment of the present invention, a comfort noise generation algorithm may be implemented to approximate the spectrum of an input noise using a Least Mean Square (LMS) function, for example. However, other functions, such as Normalized Least Mean Square (NLMS) or Linear Predictive Coding (LPC) may be implemented. The adaptation may utilize the fact that an inverse predictor shapes the input white noise to the required spectrum of the predicted signal. This adaptation may then be used to generate noise whenever speech is not present. As the spectrum of the noise is approximately constant over a period of time, the method of the present invention may produce favorable results, without using more complex signal processing. The individual modules are described in further detail below. To prevent adaptation to noise spikes or speech segments, an internal check may be done to ascertain that the input is within 6 dB (or other predetermined value) of the noise floor.  
      Empirically a 10th order synthesis filter may be determined to provide a favorable balance between performance and MIPS. Other filters may be implemented in accordance with the present invention. To ensure increased stability of the adaptation, a variant of the LMS algorithm called the Leaky LMS, for example, may be used. Other variants may be implemented in accordance with the present invention. To make the algorithm independent of variations to noise levels within a range (e.g., −30 dBm to −100 dBm), a variable precision calculation of the LMS error and LMS coefficient may be accomplished. In addition, the leaky LMS may be normalized to make the adaptation independent of signal amplitude variations. In the equations below, the value in parentheses refer to the time and variables in bold refer to arrays (e.g., vec(n) refers to values of the array “vec” at time n).  
      Parameters:  
     
         
          M: number of taps  
          μ: adaptation step size  
          a: positive value  
          n: error at time n 
 
 Data: 
 
          u(n): M by tap input vector  
          w(0): appropriate value if known; 0 otherwise  
          d(n): desired response at time n  
          e(n): error at time n 
 
 Computation: 
 
n=0, 1, 2, . . . 
 
 e ( n )= d ( n )− w ( n ) T   u ( n ) 
 
         w   ⁡     (     n   +   1     )       =         [     1   -   μα     ]     ⁢     w   ⁡     (   n   )         +     [       μ   ⁢           ⁢     u   ⁡     (   n   )       ⁢     e   ⁡     (   n   )           a   +            u   ⁡     (   n   )            2         ]           
 
 (1−μα) very close to, but less than 1 
 
       
    
      As the LMS adaptation is essentially a prediction process, the following relations may exist: 
      If x k , . . . , x k-M  is the input sample sequence     w(n)=predictor coefficients: w 0 , . . . , w m       u(n)=x k-1 , . . . , x k-M       d(n)=X k      

      The synthesis filter may be defined by  
         H   ⁡     (   z   )       =     1       ∑     i   =   0     M     ⁢       w   i     ⁢     z     -   i                 
 
      The white noise may be filtered by the above synthesis filter H(z).  
      The approximate gain may be calculated by filtering a fixed sequence of noise through the filter and its output gain calculated. This divided by the required gain (the noise floor) gives the ratio to be used while generating the output.  
      The SID may be generated by converting the direct form to lattice coefficients (e.g., reflection coefficients).  
             A     m   -   1       ⁡     (   z   )       =           [         A   m     ⁡     (   z   )       -         B   m     ⁡     (   z   )       ⁢     k   m         ]       [     1   -     k   m   2       ]       ⁢           ⁢   m     =     M   -   1         ,     M   -   2     ,   …   ⁢           ,   1       
           B   m     ⁡     (   z   )       =       z     -   m       ⁢       A   m     ⁡     (     z     -   1       )             
 
      In the decode function, a reverse operation may be used to convert the reflection coefficients to direct form coefficients. 
 
 A   0 ( z )= B   0 ( z )=1 
 
 A   m ( z )= A   m-1 ( z )+ z   −1   B   m−1 ( z ) k   m    m= 1,2, . . . , M -1 
 
 B   m ( z )= z   −m   A   m ( z   −1 )  m= 1,2, . . . , M- 1 
 
      The approximate gain calculation may also be performed in the decode function. The method is the same (or similar) as that in adapt.  
      To ensure that the output is in the telephony/speech band (150 Hz-3400 Hz), the output of a synthesis filter may be filtered through the following band pass filter.  
           H   bp     ⁡     (   z   )       =       1   +     z     -   1       -     z     -   2       -     z     -   3           1   +     0.473968   ⁢     z     -   1         -     0.666365   ⁢     z     -   2         -     0.449734   ⁢     z     -   3                 
 
      According to another embodiment of the present invention, a simple gaussian white noise generator for real time speech synthesis applications may be implemented. In speech synthesis and other applications, a gaussian white noise generator may be implemented. The present invention provides a method and system for using two or more uniform (or substantially uniform) generators to increase the periodicity to be aperiodic for various speech applications. The present invention provides a method and system for generating gaussian random noise with a long period without minimal computation complexity for fixed point and other systems.  
      When synthesizing speech, a gaussian random noise generator may be implemented. For simplicity, such a sequence may be received from a pseudo random sequence generator and then from a central limit theorem, for example. When the period of the pseudo random generator is limited, as is usually the case, this form of noise generation may lead to audible artifacts due to periodicity especially when synthesizing a stable spectrum signal, for example. The present invention provides a method and system for overcoming this drawback, without compromising the simplicity of the application.  
      To generate a practically aperiodic signal, two or more different random number generators may be implemented having a period which may be equal to a power of two (P=2 k ), for example.  
       FIG. 3   a  is an example of a system for implementing multiple generators in accordance with the present invention. Random number generators may include 16-bit generators where the period may repeat every 65536 times, for example. In this case, the number of inputs may be equal to 6, but may be set at other values. Random Number Generator  320  may include inputs  321 ,  322  and  323  coupled to an average computing component  340  and inputs  324 ,  325 ,  326  coupled to an average computing component  342 . Random Number Generator  330  may include inputs  331 ,  332  and  333  coupled to an average computing component  340  and inputs  334 ,  335 ,  336  coupled to an average computing component  342 : Average  340  may output an average Avg  1  of inputs  321 ,  322 ,  323 ,  331 ,  332  and  333 . Average  342  may output an average Avg  2  of inputs  324 ,  325 ,  326 ,  334 ,  335  and  336 .  
      As an example, the following generators have a period of 2 16  and may be implemented in accordance with the present invention.  
      Generator  1  (e.g., Random Number Generator  320 ):  
     
         
          a=seed 1 ×31821+13849  
          seed 1 =sign extended lower 16 bits of a  
          rand 1 =seed 1  
 
 Generator  2  (e.g., Random Number Generator  322 ): 
 
          b=seed 2 × 31421 + 13849   
          seed 2 =sign extended lower 16 bits of b  
          rand 2 =seed 2   
       
    
      As per a central limit theorem, a total of 2*N samples (N samples from each generator) may be averaged to give a single value of the gaussian noise output, as illustrated in further detail below.  
         avg   ⁢           ⁢   1     =       ∑     N   ⁢           ⁢   successive   ⁢           ⁢   values   ⁢           ⁢   of   ⁢           ⁢   rand   ⁢           ⁢   1       N         
         avg   ⁢           ⁢   2     =       ∑     N   ⁢           ⁢   successive   ⁢           ⁢   values   ⁢           ⁢   of   ⁢           ⁢   rand   ⁢           ⁢   2       N         
       gaussian   =         avg   ⁢           ⁢   1     +     avg   ⁢           ⁢   2       2         
 
      After each period, one of the generator&#39;s sample generation may be advanced by one (or other value) so that the period of this generator may be essentially one less than the period of the other generator. The periods of the two generators may now be relatively prime where the periodicity of the generators may be increased to P*(P-1)/(gcd(P,N)*gcd(P-1,N)), where P is the period of the first generator, P-1 is the period of the second generator and gcd(x,y) is the greatest common divisor of the two numbers x,y. This method of the present invention may be generalized to M random generators with various periods.  
      For example, Random Number Generator  330  may be set so that one sample is discarded thereby throwing the period off by a predetermined amount (e.g., one sample). As a result, Random Number Generator  330  may repeat every 65535 times while Random Number Generator  320  may repeat every 65536 times. Avg  1  and Avg  2  may be used to compute a gaussian value which produces an improved sounding background noise. This may be a result of discarding one sample from a generator (e.g.,  330 ) thereby minimizing an audible artifact due to periodicity. For example, if a second generator (e.g.,  330 ) is not implemented with a different period than a first generator ( 320 ) in accordance with the present invention, a resulting audible repeat may be perceived at approximately 1.2 seconds, for example. The present invention may be implemented to essentially eliminate (or minimize) this audible repeat.  
      Excitation of the speech synthesis filter may be formed to generate speech, as. illustrated in  FIG. 3   b.  Gaussian excitation signal  312  may be filtered by Speech Synthesis filter  310  to generate a filtered signal, as represented by Synthesized speech, at filter output  314 .  
      As an example, the following instance at a sampling rate of 8000 Hz may be compared. In an example, P may be equal to 65536 and N may be equal to 6. The period of the generator may be about 24 hours, whereas the period of each of the gaussian generators taken individually would be approximately 2 seconds.  
      According to yet another embodiment of the present invention, colored comfort noise generation (CNG) in absence of SID packets containing spectrum information may be provided.  
      In voice communications systems, where the bandwidth utilization of a voice call is to be minimized, voice activity detection and silence compression or elimination may be used to decrease the bandwidth otherwise required for non-voice segments of a conversation. Bandwidth may be saved by sending little or no information about the non-voice audio. Such information may be transmitted in a SID packet.  
      Currently, when no spectral information is transmitted, white noise may be generated, which may be unpleasant to hear because white noise often has no relation to the compressed or non-transmitted, non-voice background noise. This results in perceptible incongruities. On the receiving end of the conversation, the silence may be synthesized. If spectral information associated with the non-voiced background signal is not transmitted, the synthesized background signal typically does not have the same spectral characteristics of the true background noise. This may cause unpleasant sounding differences in the background noise when someone is speaking versus when they are not speaking. The present invention provides a method and system to overcome the aforementioned problems. In particular, the present invention provides a method and system for generating colored comfort noise in absence of SID packets containing spectrum estimation.  
      Some silence compression schemes may enable the transmission of information describing spectral characteristics of the background noise. Other techniques may only provide the background noise power level, or no information whatsoever about the background noise. When the spectral information is not contained in the SID, the decoder has no information from which to generate spectrally adaptive background noise. There are various system design considerations that may prevent spectral information from being contained in the SID. Considerations may include low complexity and interoperability, among others. For example, low complexity considerations may involve the simplicity of the equipment on the transmitting side that prevents or greatly limits the generation of SIDs containing spectral information. In another example, interoperability considerations may involve several standards that may exist in which there are well-defined SIDs which may contain background noise power, or minimum or no information about the background noise.  
      The present invention provides a method and system for generating colored noise reflecting the spectrum of the actual noise in the absence of SID packets containing spectral information. The low complexity spectrum estimation technique for CNG discussed above may be implemented to generate the comfort noise, for example.  
      The present invention provides a method and system for utilizing information content in the speech and the transition hangover between speech and noise, on the decoder side to generate comfort noise. This adaptation to noise may be accomplished using various algorithms of estimating the spectrum of color noise. According to an embodiment of the present invention, an adaptation algorithm may be implemented that adapts with time, rather than a block based algorithm to prevent the repeated generation of artifacts present in the block that are being adapting to. The adaptation of the present invention coupled with the transmitted noise floor provides the capability of generating colored comfort noise. The following figure shows the idea in the form of a flow chart, as illustrated in  FIG. 4   a.    
       FIG. 4   a  is an example of a flowchart for a decoder process, according to an embodiment of the present invention. At step  410 , speech/hangover content may be identified. If speech/hangover content exists, comfort noise adaptation may be performed, at step  412 . If speech/hangover content does not exist, comfort noise may be generated, at step  414 . Information from step  412  and step  414  may be forwarded to the input of step  410 .  
       FIG. 4   b  illustrates one example of a system  400  for implementing a decoder process, according to an embodiment of the present invention.  FIG. 4  further illustrates an exemplary detection input signal  424 , which is processed for transmission, and a received signal  438  (both show signal amplitude as x-reference over time as y-reference).  422  may represent a phone or other voice communication device attached to an encoder  420  which processes and transmits voice and other signals to decoder  450 . Signals are received by a receiving phone or other voice communication device  452 . In this example, voice signal  430  and voice signal  432  are detected by a voice activity detector associated with devices  422  and/or  420  and transmitted to a receiving end ( 450  and/or  452 ), as signal  440  and signal  442 , respectively.  434  may represent a hangover portion of voice signal which may indicate a transition from voice to silence. A noise floor estimator may be implemented to detect background noise. Signal  436  represents background noise. In one example, a measurement of power associated with background noise  436  may be transmitted. For example, background noise  436  may have a power of −60 dB. Signal  444  may represent background noise generated at a power of −60 dB.  
      According to the present invention, on the decoder side, small pauses (e.g.,  446  and  448 ) during voice signal  440  may be used to generate background noise  444  via an adaptive algorithm. In other words, background noise may be learned from small pauses or gaps during a voice signal, such as  440 . This information may be used to generate a filter  462  of  FIG. 4   c.  As a result, information that is sent from encoder  420  to decoder  450  may be conserved by limiting transmission to voice signals. The present invention provides a method and system for adapting on a decode side when background noise itself is not transmitted. In other words, transmission may be limited to voice signals. As a result, bandwidth may be conserved by not sending information related to background noise. According to another example, hangover  434  may be used to generate background noise. Hangover  434  may represent a transition period between voice and non-voice portions. Thus, hangover  434  may contain information regarding background noise which may be used to generate background noise at  444 .  
       FIG. 4   c  is an example of a system for generating background noise, according to an embodiment of the present invention. White noise generator  460  generates white noise for use in creating replacement background noise for insertion during non-voice portions at the receiving end and may include a random number generator, although other types of generators may be implemented. Filter  462  receives the output of the white noise generator  460  and may represent an excitation filter, which may be fixed in one example. Further, via an adaptive algorithm, filter  462  may be created based on information related to small pauses, e.g.,  446  and  448 , or hangover portions, e.g.,  434 , during a voice signal. This information may be used to accurately and efficiently generate background noise during non-voice signals. As a result, filter  462  may output a noise sequence that represents true (or approximately true) noise or characteristics of such noise detected at the encoder side between voice signals.  
      According to still another embodiment of the present invention, a method and system for determining filter gain and automatic gain control for fixed point low delay algorithms in real time systems may be provided. In systems where low latency may be imperative and where the filter is not a constant but variable based on input signal, a method and system for determining filter gain and automatic gain control (AGC) may be implemented. The present invention provides a method and system for implementing low MIPS where the method and system is further useful in applications generating a single sample (or few samples) per call. Other applications may be implemented in accordance with the present invention.  
      An additional aspect of the present invention may involve computing the gain of a filter using an approximation calculation. This may involve filtering a signal similar in spectrum to the input to be filtered and then fine-tuning the signal. The fine tuning process of this aspect of the present invention may be based on a short term moving mean square calculation in the low delay, low MIPS state of the algorithm. Other variations may be implemented.  
      In yet another arrangement, the present invention provides a method and system for controlling the output gain using lower MIPS compared to a brute force calculation of the gain and then scaling output based on that gain. The method and system of the present invention may be particularly applicable in single sample (or few samples) input scenarios.  
      According to an embodiment of the present invention, the approximate output gain of a filter may be calculated by filtering a known (or representative) input signal. This calculation may be accomplished in a non time-critical routine or at the beginning of the algorithm if the filter taps are constant, for example. Using the gain (G o ), the scale factor (SF) may be computed, for a given Root Mean Square (RMS) value of the output (G R ). The value of G R  may be determined by other means or it can be a constant output level.  
       FIG. 5  and  FIG. 6  illustrate examples of block diagrams of a filter and filter gain G F , according to an embodiment of the present invention.  FIG. 5  shows a representative input signal  512  being filtered by Filter  510  to result in an approximate output gain, as shown by  514 .  FIG. 6  shows an input gain G I , as shown by  612 , being filtered by a Filter Gain G F , as shown by  610  with an output gain G O , as shown by  614 . The following calculations may apply to the filter of  FIG. 6 , according to an embodiment of the present invention. 
   G   O   =G   F   ×G   I      G   R   =G   O   ×SF    
      As for the fine-tuning of gain, the scale factor calculated during the non-critical phase of the algorithm may now be utilized to control the gain of the output, during the real time filtering, for example. As the output may be available sample by sample, the mean square value of a block of such samples may be calculated over a predetermined period of time, which may be equal to the block length, for example. When a predetermined block length (L) is reached, the mean square value may be compared to the square of an output RMS. The output RMS value may be determined by other methods. To facilitate finding the mean, the inverse of L may be calculated, resulting in a simple multiple or L may be made a multiple of  2 , or other number. Depending on whether the gain of the output is smaller than G R −D dB or greater than G R +D dB, the scale factor may be increased by a small predetermined amount delta (Δ) dB. Δ represents whether the change is fast or gradual and D represents a predetermined constant that may be user defined.  
       FIG. 7  illustrates a process for fine tuning AGC, according to an embodiment of the present invention. At step  710 , a sample, as represented by s, may be outputted. At step  712 , a sum squares calculation may be performed where sum squares may be equal to sum squares +s 2 , where s represents the sample and sum squares represents the sum of the squares of each sample. In addition, a counter may be advanced by one or other predetermined value. At step  714 , if the count is determined to be greater than a predetermined value of L (e.g., block length), a mean squares calculation may be performed wherein mean squares may be equal to the value of sum squares divided by L, as shown by step  716 . Otherwise, one or more samples may be outputted, at step  710 . At step  718 , the value of mean squares may be determined to be greater than G R +D dB. The constant D represents a predetermined constant that may be user defined. If so, the value of SF may be creased by delta dB, at step  720 . If the value of mean squares is determined to be less than G R −D dB, at step  722 , SF may be decreased by delta dB, at step  724 . Otherwise, one or more output samples may be received at step  710 . At an output of step  720  and/or step  724 , a feedback loop may be established back to step  710 .  
      After the approximate gain is applied to the output, to ensure that the noise generated is within ±2 dB, automatic gain control (AGC) may be applied. The output gain may be calculated as a block average over 4 ms. If this average is greater (lesser) than 6 dB of a required noise floor, the output gain may be reduced (increased) by 3 dB every 4 ms.  
      According to another embodiment of the present invention, CNG module compresses and reconstructs the silence part of speech signals. CNG works with any voice activity detector, e.g., Voice Activity Detection with Automatic Gain Control (VAGC) module, or with an echo canceller, e.g., Echo Canceller with Single Reflector (ECSR) module, to compress silence or generate comfort noise. Other applications may be implemented. CNG can be used in a variety of ways outlined below and in  FIGS. 8-14 .  
       FIG. 8  illustrates an example of a system using CNG on an encode side, according to an embodiment of the present invention. This mode of operation may be used when a vocoder does not have a silence compression capability. To improve the compression of the system, CNG adapts to the noise between the speech data and creates silence insertion descriptors (SID), when the speech is inactive, as shown in  FIG. 8 .  
      As shown in  FIG. 8 , an encoder may receive a BLOCK_SIZE of input data, at step  810 . At step  812 , near end speech activity may be determined. If near end speech is inactive, NFE_run and CNG_adapt functions may be executed, at step  818 . SID packets may be sent, if necessary, at step  820 . If near end speech is active, G7xx encoding is performed, at step  814 , and codeword is sent, at step  816 , to the channel (transmitted to the decoder), which may be used to decode the signal information at the decoder. After the output of step  820  and/or step  816 , the state of the system may be reset to receive new input data at step  810 . From this point onwards, the system restarts and converts the input data into a speech codeword of step  816  or SID packets of step  820  where the process continues until the speech input stops (e.g., the call has ended).  
       FIG. 9  illustrates an example of a system using CNG on an encode side when SID is not sent, according to an embodiment of the present invention. At step  910 , BLOCK_SIZE of input data may be received. When near end speech is determined to be inactive, at step  912 , CNG adapts and generates noise, at step  914 , which is encoded by the vocoder. In particular, at step  914 , NFE_run, CNG_adapt and CNG_generate functions may be executed. This mode may be used when the residual echo and the noise combination is perceptually unpleasant. CNG generates perceptually enhanced noise based on the average spectrum of the input, as shown in  FIG. 9 . If near end speech is determined to be active, at step  912 , G7xx encoding occurs, at step  916  and the encoded codeword is sent at step  918  to the channel (transmitted to the decoder) to synthesize speech at the decoder. After an output of step  918 , the system may be reset to step  910 . The system is ready to receive new BLOCK_SIZE number of data samples and the process continues until the speech input stops (e.g., the call has ended).  
       FIG. 10  illustrates an example of a system using CNG on a decode side, according to an embodiment of the present invention. If the received codeword, at step  1010 , is a SD as determined by step  1012 , CNG decodes this information, at step  1014  and generates comfort noise at step  1016 . In particular, CNG_decode function may be executed at step  1014  and CNG_generate function may be executed at step  1016 . The SD typically includes spectral information (e.g., reflection coefficients) of noise to be generated. This mode of operation may be used when CNG or any other comfort noise generation algorithm conforming to the IETF (or other) standard is used on the encode side. CNG_generate( ) may also be used in the Interrupt Service Routine (ISR), if so desired. If SD is not received at  1012 , G7xx decoding is performed at step  1018 . At step  1020 , BLOCK_SIZE of output data may be generated and forwarded to step  1010 . After speech/silence is output at step  1020 , the system is then reset to receive a new codeword or SID and the process continues until the call ends (e.g., the codeword or SID stops).  
       FIG. 11  illustrates another example of a system using CNG on the decode side, according to an embodiment of the present invention. In this case the SID has no information about the noise, except, the noise floor. CNG adapts to the noise between the speech data, during the vocoder decoding process, and generates noise when a SID is received. This scenario enables the user to generate noise closer to the actual background noise, rather than simple white noise.  
      At step  1110 , codeword data is received. SID may be detected at step  1112 . If SID is not received and therefore not detected, G7xx decoding is performed at step  1116 . Functions NFE_run and CNG_adapt are performed at step  1118 . In addition,  FIG. 11  shows a system where CNG_adapt( ) adapts to the decoded speech, at step  1118 . CNG_generate( ) may also be used, at step  1114 , in the ISR if so desired. At step  1120 , BLOCK_SIZE of output data may be generated and forwarded to step  1110 .  
      Additional details regarding exemplary constants, structures, prototypes, memory usage, and file descriptions, in accordance with one particular embodiment of the present invention, will now follow.  
                                  /* The following constants define the default parameter values */       #define CNG_ADAPTSIZE_DEF  (40)       #define CNG_GENSIZE_DEF  (40)       /* The following constants define the max. and min. parameter values */       #define CNG_MAX_ADAPTSIZE  (80)       #define CNG_MAX_GENSIZE  (80)       #define CNG_MIN_ADAPTSIZE  (40)       #define CNG_MIN_GENSIZE  (1)       /* The following constant defines the size of the SID */       #define CNG_SIDSIZE   (11)       /* The following constants define the modes of CNG operation */       #define CNG_NO_SID   (0)       #define CNG_REFLC_SID   (1)       Internal Object Definition       typedef struct {                      int stackMemAddr;     /* Saving stackMem address */        int xm1;   /* Band Pass filter history */        int xm2;   /* Band Pass filter history */        int xm3;   /* Band Pass filter history */        int ym1;   /* Band Pass filter history */        int ym2;   /* Band Pass filter history */        int ym3;   /* Band Pass filter history */        int lmsErr;    /* Error for lms */        int highBuf;    /* Buffer for AGC calc. */        int lowBuf;    /* Buffer for AGC calc. */        int highPrevBuf;    /* Buffer for AGC calc. */        int lowPrevBuf;    /* Buffer for AGC calc. */        int count;   /* Count for AGC */        int seed1;   /* Seed for Rand Generator 1 */        int c13849;    /* Constant for Rand */        int seed2;   /* Seed for Rand Generator 2 */        int c31821;    /* Constant for Rand */        int lmsPredCoef[11];     /* Predictor coeffs. */        int curRms;    /* Gain factor for output */        int c15330;    /* AGC constant */        int c21835;    /* AGC constant */        int c14736;    /* AGC constant */        int flag;   /* AGC flag */        int noise;    /* Noise floor */        int randCount;     /* Counter for randomizing */        int genSize1;     /* pGENSIZE-1 */        int adaptSize1;      /* pADAPTSIZE-1 */        int lmsHist[11];       /* History for lms */        int genMem[10];      /* History for synth filter */       } _CNG_Internal;                 Local Parameter Definition       typedef struct {                      int pADAPTSIZE;    /* Adapt block size */        int pGENSIZE;   /* Generate block size */       } CNG_Params;                 Object Definition       typedef struct {        long   buffer;   /* Even alignment */        _CNG_Internal internal;    /* internal object */        int   *dst_ptr;        int   *src_ptr;        int   *sid_ptr;        int   sidMode;       } CNG_Obj;       typedef CNG_Obj *CNG_Handle;       Function Prototypes may include the following:       void CNG_init(        CNG_Handle cng_ptr,        CNG_Params *params_ptr,        int   *stack_ptr);       void CNG_adapt(        CNG_Handle cng_ptr,        NFE_Handle nfe_ptr);       void CNG_decode(        CNG_Handle cng_ptr);       void CNG_generate(        CNG_Handle cng_ptr);                  
 
      The following is a list and description of exemplary files associated with the CNG module.  
                                       Directory   File   Description                  INCLUDE   CNG.H   C header file for CNG       LIB   CNG.O54   Object file containing CNG init( ), CNG_adapt( ),               CNG_detect( )and CNG_decode( ), near mode       LIB   CNG.F54   Object file containing CNG_init( ), CNG_adapt( ),               CNG_detect( )and CNG decode( ), far mode       EXAMPLES   CNG_EX.C   C usage example file for CNG       EXAMPLES   CNG_EX.CMD   C54x linker command file for CNG_EX.C       EXAMPLES   B_CNG.BAT   DOS batch file for building CNG_EX.C       EXAMPLES   CNG_EX.O54   C54x object file for CNG_EX.C, after running               B_CNG.BAT       EXAMPLES   CNG_EX.X54   C54x DSP executable file for CNG_EX.C, after running               B_CNG.BAT       EXAMPLES   CNG_EX.MAP   C54x map file for CNG_EX.C, after runninG B_CNG.BAT       DOC   CNG_MAN.PDF   Manpage file for CNG                  
 
      The following example code shows how the CNG module adapts and generates the silence part of speech signals. G 726  in Linear mode is used to encode and decode the active voice. The VAGC module is used to detect silence. The Silence Insertion Descriptor (SID) may be assumed to have the Internet Engineering Task Force (IETE) draft SID format.  
                                  #include &lt;comm.h&gt;       #include &lt;nfe.h&gt;       #include &lt;vagc.h&gt;       #include &lt;cng.h&gt;       #include &lt;g726.h&gt;       #undef USE_C_STACK  /* If defined, CNG will use the C stack. */       #define BLOCK_SIZE (40)       #define SPEECH  (0)       #define SID  (1)       NFE_Obj nfeObj;       VAGC_Obj vagcObj;       CNG_Obj cngObj;       G726_Obj encObj;       G726_Obj decObj;       GLOBAL_Params globals = {         4004,   /* 0 dBm level for input signal */         4004};   /* 0 dBm level for output signal */       NFE_Params nfePara = {         NFE_MINNOISE_DEF,  /* Minimum noise floor in dBm */         NFE_MAXNOISE_DEF,  /* Maximum noise floor in dBm */         BLOCK_SIZE);       VAGC_Params vagcPara = {         VAGC_tSUSTAIN_DEF,  /* Number of blocks for Sustain */         VAGC_tHCNGOVER_DEF,  /* Number of blocks for         HCNGover */         VAGC_tDECAY_DEF,  /* Decay time */         VAGC_pMAXGAIN_DEF,  /* Maximum gain */         VAGC_pINITGAIN_DEF,  /* Initial gain */         VAGC_pGLI_DEF};  /* Input speech gain*/       CNG_Params cngPara = {         BLOCK_SIZE,   /* Adapt block size */         BLOCK_SIZE};   /* Generate block size */       #ifndef USE_C_STACK       extern int *vpoStack_ptr; /* VP Open stack to be used by CNG */       #endif       int src[BLOCK_SIZE];       int dst[BLOCK_SIZE];       int mubuf[BLOCK_SIZE/2];       int cBuf[BLOCK_SIZE/2];       int sid[CNG_SIDSIZE];       /*        * ======== main ========        */       void main( )       {        NFE_Handle nfe_ptr = &amp;nfeObj;        VAGC_Handle vagc_ptr = &amp;vagcObj;        CNG_Handle cng_ptr = &amp;cngObj;        G726_Handle enc_ptr = &amp;encObj;        G726_Handle dec_ptr = &amp;decObj;        int buffer[BLOCK_SIZE];        int retVal;        int codeword;        int *stack_ptr;       /*        * Placeholder: Initialize host device driver        */       #ifdef USE_C_STACK        stack_ptr = NULL;       #else        stacK_ptr = vpoStack_ptr;       #endif        /*        * Initialize VP OPEN modules        */        nfe_ptr-&gt;src_ptr = src;        nfe_ptr-&gt;xmit_ptr = NULL;        NFE_init(nfe_ptr, &amp;globals, &amp;nfePara, NFE_COLDSTART,        stack_ptr);        vagc_ptr-&gt;dst_ptr = NULL;        vagc_ptr-&gt;agcMode = VAGC_BYPASSAGC;        VAGC_init(vagc_ptr, &amp;globals, &amp;vagcPara,        VAGC_COLDSTART,         stack_ptr);        /* Initialize G726 encoder/decoder: 5 ms block, 32 kbps rate */        G726_init(enc_ptr, G726_ENCODE, stack_ptr,        BLOCK_SIZE, 4, 0);        G726_init(dec_ptr, G726_DECODE, stack_ptr,        BLOCK_SIZE, 4, 0);        cng_ptr-&gt;src_ptr = src;        cng_ptr-&gt;dst_ptr = dst;        cng_ptr-&gt;sid_ptr = sid;        cng_ptr-&gt;sidMode = CNG_REFLC_SID;        CNG_init(cng_ptr, &amp;cngPara, stack_ptr);        while(1) {         /*         * Placeholder: Read BLOCK_SIZE/2 words of mu-law input speech         *     data into mubuf[ ]         */         mu2linear(BLOCK_SIZE, mubuf, src);         NFE_run(nfe_ptr);         VAGC_run(vagc_ptr, nfe_ptr);         /* Encoder */         if (vagc_ptr-&gt;portActive) {         enc_ptr-&gt;src_ptr = src;         enc_ptr-&gt;dst_ptr = cBuf;         G726_encode(enc_ptr);         codeword = SPEECH;         }         else {         CNG_adapt(cng_ptr, nfe_ptr);         codeword = SID;         }         /* Decoder */         if(codeword == SPEECH) {         dec_ptr-&gt;src_ptr = cBuf;         dec_ptr-&gt;dst_ptr = dst;         G726_decode(dec_ptr);         }         else {         /*          * Placeholder: copy received information to sid buffer.          *    Set unused reflection coefficients to zero.          */         CNG_decode(cng_ptr);         CNG_generate(cng_ptr);         }         /*         * Placeholder: write BLOCK_SIZE samples of decoded speech from         *    dst[ ] to output file         */         }         /* Placeholder: close the host device driver */        }                  
 
      The following example code shows how the CNG module compresses and reconstructs the silence part of speech signals in an echo cancellation application. G726 is used to encode and decode the active voice. In this example, CNG is working with ECSR. The SID may be assumed to have only the noise level information.  
                                  #include &lt;comm.h&gt;       #include &lt;nfe.h&gt;       #include &lt;ecsr.h&gt;       #include &lt;cng.h&gt;       #include &lt;g726.h&gt;       #undef USE_C_STACK  /* If defined, CNG will use the C stack. */       #define BLOCK_SIZE (40)       #define TAIL_LENGTH (256)  /* 32 ms Tail Length */       #define SPEECH  (0)       #define SID   (1)       NFE_Obj nfeObj1;       NFE_Obj nfeObj2;       ECSR_Obj ecObj;       CNG_Obj cngObj;       G726_Obj encObj;       G726_Obj decObj;       GLOBAL_Params globals = {         4004,     /* 0 dBm level for input signal */         4004};     /* 0 dBm level for output signal */       NFE_Params nfePara = {         NFE_MINNOISE_DEF,  /* Minimum noise floor in dBm */         NFE_MAXNOISE_DEF,  /* Maximum noise floor in dBm */         BLOCK_SIZE};       ECSR_Params ecsrPara = {         BLOCK_SIZE,   /* Block size for ECSR_run( ) */         TAIL_LENGTH,   /* Tail length in samples */         0,    /* ISR block size for ECSR_run( ) */         0,    /* minimum voice power */         100};    /* hangover time */       CNG_Params cngPara = {         BLOCK_SIZE,   /* Adapt block size */         BLOCK_SIZE};   /* Adapt block size */       #ifndef USE_C_STACK       extern int VPO_STACKMEM;       int *vpoStack_ptr = &amp;VPO_STACKMEM; /* VP Open stack       used by modules */       #endif       int src[BLOCK_SIZE];       int dst[BLOCK_SIZE];       int muBuf[BLOCK_SIZE/2];       int cbuf[BLOCK_SIZE/2];       int rin[BLOCK_SIZE];       int sin[BLOCK_SIZE];       int sout[BLOCK_SIZE];       int nlpOut[BLOCK_SIZE];       int dlyBuf[ECSR_32MS_DLY_BUF_SIZE];       /*        * ======== main ========        */       void main( )       {        NFE_Handle nfe1_ptr = &amp;nfeObj1;        NFE_Handle nfe2_ptr = &amp;nfeObj2;        ECSR_Handle ec_ptr = &amp;ecObj;        CNG_Handle cng_ptr = &amp;cngObj;        G726_Handle enc_ptr = &amp;encObj;        G726_Handle dec_ptr = &amp;decObj;        int   buffer[BLOCK_SIZE];        int   retVal;        int   *stack_ptr;        /*         * Placeholder: Initialize host device driver         */       #ifdef USE_C_STACK        stack_ptr = NULL;       #else        stack_ptr = vpoStack_ptr;       #endif       /*        * Initialize VP OPEN modules        */       ec_ptr-&gt;control = ECSR_ERL | ECSR_NLP;       ec_ptr-&gt;rin_ptr = rin;       ec_ptr-&gt;sin_ptr = sin;       ec_ptr-&gt;soutFull_ptr = sout;       ec_ptr-&gt;nlpOut_ptr = nlpOut;       ec_ptr-&gt;dlyBuf_ptr = dlyBuf;       ECSR_init(ec_ptr, &amp;globals, &amp;ecsrPara, ECSR_COLDSTART,       stack_ptr);       /*        * nfe1 is used for noise estimation on the encoding side        */       nfe1_ptr-&gt;src_ptr = sout;       nfe1_ptr-&gt;xmit_ptr = rin;       NFE_init(nfe1_ptr, &amp;globals, &amp;nfePara, NFE_COLDSTART,       stack_ptr);       /*        * nfe2 is used for noise estimation on the decoding side        */       nfe2_ptr-&gt;src_ptr = dst;       nfe2_ptr-&gt;xmit_ptr = NULL;       NFE_init(nfe2_ptr, &amp;globals, &amp;nfePara, NFE_COLDSTART,       stack_ptr);       /* Initialize G726 encoder/decoder: 5 ms block, 32 kbps rate */       enc_ptr-&gt;src_ptr = src;       enc_ptr-&gt;dst_ptr = cbuf;       G726_init(enc_ptr, G726_ENCODE, stack_ptr, BLOCK_SIZE, 4, 0);       dec_ptr-&gt;src_ptr = cbuf;       dec_ptr-&gt;dst_ptr = dst;       G726_init(dec_ptr, G726_DECODE, stack_ptr, BLOCK_SIZE, 4, 0);       /*        * As dst buffer is mutually exclusive, we can use the same buffer        */       cng_ptr-&gt;src_ptr = dst;       cng_ptr-&gt;dst_ptr = dst;       /* SID is not required, set sidMode to CNG_NO_SID:Lower MIPS */       cng_ptr-&gt;sidMode = CNG_NO_SID;       CNG_init(cng_ptr, &amp;cngPara, stack_ptr);       while (1) {        /*          * Placeholder: read BLOCK_SIZE/2 samples of input far-end          *    data from a mu-law file into muBuf[ ]         */        mu2linear(BLOCK_SIZE, muBuf, rin);        /*          * Placeholder: read BLOCK_SIZE/2 samples of input near-end          *    data from a mu-law file into muBuf[ ]        */        mu2linear(BLOCK_SIZE, muBuf, sin);        ECSR_run(ec_ptr, nfe1_ptr);        NFE_run(nfe1_ptr);        /* Encoding */        if(!(ec_ptr-&gt;status &amp; ECSR_NLP_BLK_ACT)) {         G726_encode(enc_ptr);         codeword = SPEECH;        }        else {         codeword = SID;         /*          * As the SID is just the noise floor, transmit NFE          * noisefloor. CNG_adapt is not necessary          */        }        /* Decoding */        if(codeword == SPEECH) {         G726_decode(dec_ptr);         NFE_run(nfe2_ptr);         CNG_adapt(cng_ptr,nfe2_ptr);        }        else {         CNG_generate(cng_ptr);        }        /*           * Placeholder: write BLOCK_SIZE samples of decoded           speech from           *    dst[ ] to output file           */          /* Placeholder: break when done */        }        /* Placeholder: close the host device driver */       }                  
 
      Module functions of the present invention may include CNG_init( ), CNG_adapt( ), CNG_decode( ), and CNG_generate( ), although other module functions may be implemented.  
      Exemplary code associated with the CNG_init( ) module function includes:  
                                  void CNG_init(        CNG_Handle cng_ptr,  /* pointer to local CNG object */        CNG_Params *cngParams_ptr, /* pointer to CNG parameters */        int *stack_ptr);  /* pointer to stack memory */                  
 
      Modules may have an initialization function that is called first. Prior to calling CNG&#39;s initialization function, CNG_init( ), two data structures are created. A first structure that is be created may include the CNG object. One object may be implemented for each simultaneous use of CNG. CNG_init( ) initializes this object. A second structure may include CNG parameters. This structure is initialized to the individual requirements. Table 2 below shows exemplary parameters and their ranges.  
      CNG_init( ) may use three (or more) calling arguments. A first calling argument may include a pointer to the CNG object structure. A second calling argument may include a pointer to the CNG parameters structure. A third calling argument may include a pointer to stack scratch space, *stack_ptr. It points to the bottom (e.g., highest address) of the memory allocated for scratch space (e.g., temporary variables).  
      If *stack_ptr points to NULL, the existing C stack is used for scratch space. If a separate scratch space is used, there must be sufficient memory allocated for the module with the largest scratch space usage, plus overhead for any ISR usage that may be required if the module can be interrupted. The constant CNG_STACKMEMSIZE indicates the amount of scratch space required by CNG, not including any overhead for ISR usage.  
                                           Local Parameter/                   Units   Defaults   Range   Definition                  pADAPTSIZE/   40   40, 44, 48, 52, 56, 60, 64,   Block size in samples.       samples       68, 72, 76, 80   CNG_adapt( ) is never used               (5 to 10 ms in 0.5 ms step   in the ISR.               size)       pGENSIZE/   40   40, 44, 48, 52, 56, 60,   Block size in samples.       samples       64, 68, 72, 76, 80 (5 to   If CNG_generate( ) is to be               10 ms in 0.5 ms step   used in the ISR then set the               size)   pGENSIZE to 1, 2, 4 or 8.               or 1, 2, 4, 8 for ISR                  
 
     
       
         
           
               
               
             
               
                   
                   
               
               
                   
                   
               
             
            
               
                   
                 Function CNG_adapt( ): 
               
               
                   
                 void CNG_adapt( 
               
               
                   
                 CNG_Handle cng_ptr,  /* pointer to local CNG object */ 
               
               
                   
                 NFE_Handle nfe_ptr);  /* pointer to NFE object */ 
               
               
                   
                   
               
            
           
         
       
     
       FIG. 12  illustrates a flowchart for a CNG_adapt function, according to an embodiment of the present invention. Two calling arguments may include pointers that point to the CNG object and NFE object, respectively. Table 3 illustrates exemplary pointers and mode assignment for use in the CNG_adapt function of  FIG. 12 . Before calling CNG_adapt( ), the source pointer, the SID mode and SID buffer pointer are to be assigned. The source pointer is a pointer to the source buffer of size pADAPTSIZE on which adaptation is done. This is assigned to cng_ptr→src_ptr.  
      The SID mode value determines if CNG_adapt calculates the SID coefficients. The SID mode is specified through cng_ptr→sidMode. For applications not requiring SID calculations this mode may be set to CNG_NO_SID, else this value is set to CNG_REFLC_SID. If the CNG_REFLC_SID mode is used, then the user needs to assign the SID buffer pointer, cng_ptr→sid_ptr. The SID buffer should be of size CNG_SIDSIZE.  
      After the CNG object has been initialized, adaptation to silence (if found) may be performed by calling CNG_adapt( ) once every pADAPTSIZE samples. CNG_adapt( ) is called whenever speech inactivity is detected. CN_adapt( ) may not be called in an ISR. If the SID mode is set to CNG_REFLC SID, CNG_adapt( ) may output the noisefloor and reflection coefficients in the SID buffer.  
      If the comfort noise payload contains only the noise floor and no other information regarding the noise spectrum, CNG_adapt( ) may be called to adapt to the noise between speech signals, to ensure that the noise generated is of a better quality and is closer to and more representative of the actual noise. To prevent adaptation to generated noise, CNG_adapt( ) may be called when the pADAPTSIZE number of samples contain the decoded speech and no CNG generated noise, as shown in  FIG. 11 .  
       FIG. 12  shows steps for executing a CNG_adapt function, according to an embodiment of the present invention. At step  1210 , it may be determined whether signal power −6 dB is less than or equal to noise power. If not, LMS adaptation may be performed at step  1212 . At step  1214 , filter gain normalization and output gain calculation may be performed. At step  1216 , it may be determined whether sidMode is equal to CNG_REFLC_SID. If so, direct form to reflection coefficient conversion may be performed, at step  1218 . Return  1220  indicates the end of the CNG adapt function.  
                                   Assignments           (condition/function(s))   Definition                  src_ptr   Points to source buffer of size       (required/CNG_adapt( ))   pADAPTSIZE, on which adaptation is           done.       sidMode   Determines if CNG_adapt( ) has to       (required/CNG_adapt( ))   calculate SID coefficients.       sid_ptr   Points to a buffer of CNGS_SID_SIZE.       (optional/CNG_adapt( ))   Used by CNG_adapt( ) only if the sidMode       (required/CNG_decode( ))   is set to CNG_REFLC_SID.           Always used by CNG_decode( ).       dst_ptr   Always used by CNG_generate( ). Points       (required/   to a buffer of size pGENSIZE       CNG_generate( ))                    
      Exemplary code associated with the CNG_decode( ) module function is:  
                                                  void CNG_decode(            CNG_Handle cng_ptr);  /* pointer to local CNG object */                      
 
       FIG. 13  illustrates a flowchart for a CNG_decode function, according to an embodiment of the present invention. As its only calling argument this function may take in a pointer to the CNG object. Table 3 illustrates pointers and mode assignment. Before calling CNG_decode( ), SID pointer cng_ptr→sid_ptr may be assigned to point to a buffer of size CNG_SIDSIZE. The unused reflection coefficients in the SID may be set to zero.  
      CNG_decode( ) may decode the silence insertion descriptor (SID) and initialize filter coefficients and object variables that are used by CNG_generate( ) for generation of the comfort noise. CNG_decode( ) may be called once every pADAPTSIZE number of samples. CNG_decode( ) may be used when the SID contains noise spectrum characteristics, namely, the reflection coefficients of the all pole filter.  
      In applications where the SID contains only the noise level, CNG_decode( ) may not be used. CNG_adapt( ) may used in the decoder as shown in  FIG. 11 . In the latter case, cng_ptr→sidMode may be set to CNG_NO_SID to reduce MIPS.  
       FIG. 13  shows steps for executing a CNG_decode function, according to an embodiment of the present invention. At step  1310 , SID parameters may be decoded and reflection coefficients to direct form conversion may be performed. At step  1312 , filter gain normalization and output gain calculation may be performed. Return  1314  indicates the end of CNG_decode function.  
      Exemplary code associated with the CNG generate( ) module function is:  
                                                  void CNG_generate(            CNG_Handle cng_ptr);  /* pointer to local CNG object */                      
 
       FIG. 14  illustrates a flowchart for a CNG_generate function, according to an embodiment of the present invention. A calling argument may include a pointer that points to the CNG object. Table 3 illustrates pointers and mode assignment. Prior to calling. CNG_generate( ), cng_ptr→dst_ptr should be assigned to point to the output buffer of size pGENSIZE.  
      CNG_generate( ) may generate pGENSIZE number of samples each call. This function may also be called in the ISR. This distinction is to be specified through pGENSIZE (see CNG_init). The information for generating comfort noise may be taken directly from the object, which may be updated by either CNG_decode( ) or CNG_adapt( ).  
       FIG. 14  shows steps for an exemplary process for executing a CNG_generate function, according to an embodiment of the present invention. At step  1410 , noise may be synthesized. At step  1412 , band pass filter may be performed. At step  1414 , automatic gain control is performed. Return  1416  indicates the end of CNG_generate function.  
      For module requirements of the present invention, functional specifications may include adapting to the silence part of speech and generate comfort noise and creating silence insertion descriptors. As for adapting to the silence part of speech and generating comfort noise, the reconstructed comfort or background noise shall preserve the energy and the spectrum shape of the original signal as much as possible. As for create silence insertion descriptors, SIDs may be created as described in the IETF draft on Real-Time Transport Protocol (RTP) payload for comfort noise, dated October 2001.  
      Performance specifications may include the quality of reconstructed silence (comfort noise) and may be, for example, in accordance with International Telecommunications Union (ITU) standard G.729/G.729A with Annex B.  
      In  FIGS. 15-19 , systems using the CNG in the absence of SID packets as discussed above are illustrated. The CNG software is used on the decode side. On the encode side, the silence is compressed as energy level and no spectral information is transmitted. CNG is not needed on the encode side. On the decode side, the CNG algorithm adapts to the speech generated by the G7xx decoder during speech segments and uses this information to synthesize silence/background noise in the absence of speech segments.  
      In its Magnesium™ product, Virata Corporation of Santa Clara, California, extends the benefits of integrated software on silicon (ISOS™)—pre-integrated software, pre-packaged systems, selectable software modules, system flexibility, all leading to rapid and low risk developments—to the voice processing market, providing a bundle of functions and interface drivers—vCore™—together with C54-compatible Digital Signal Processing (DSP) chips, such as those manufactured by Texas Instruments. Targeted for telecommunciations equipment, such as broadband Integrated Access Devices (IADs), Private Branch Exchange&#39;s (PBX&#39;s), key systems, wireless base stations, and IP Phones. This powerful combination of hardware and software is ideally suited to MIPS-intensive voice and telephony algorithms and may include VoDSL and VoIP applications.  
      The inventive concepts discussed above may be incorporated into Application-Specific Integrated Circuits (ASICs) or chip sets such as Virata Corporation&#39;s Magnesium™ DSP chip, which may be used in a wide variety of applications.  FIGS. 15 and 16  illustrate a hardware/software architectures  1500  and  1600  in which the present invention may be incorporated. The system of  FIG. 15  includes a protocol processor  1510 , a network processor  1520 , physical interface section  1530 , and external device section  1540 , as well as software to implement the desired functionality. As shown in  FIG. 15 , comfort noise generator functionality  1550  may be implemented as a voice algorithm or other software.  
      The system of  FIG. 16  includes a software interface  1624 , in communication with a variety of modules and/or applications, which may include a voice detection and automatic gain control (AGC) module  1610 , a caller identifier on call waiting (CIDCW) analog display services interface (ADSI) module  1612 , a full duplex speaker phone module  1614 , a call progress fax tone detection module  1616 , a voice coders module  1618 , a Dual Tone Modulation (or Multi) Frequency (DTMF) detect and remove module  1620 , and a line echo canceller module  1622 . A comfort noise generator module  1636  may be provided, in accordance with the present invention. In addition, other functionality may be provided by customer applications  1626 , a Helium™ host interface  1628 , a host driver  1630 , a channel driver  1632  and a telephone interface control  1634 . Other applications, modules and functionality may also be implemented.  
      Virata&#39;s Magnesium™ voice software, vCore™, is an object and source code software library proven in hundreds of applications around the world. Based on an open, flexible, and modular software architecture, vCore™ enables a system designer to provide an optimized and efficient custom solution with minimal development and test effort. Software modules associated with vCore™ are available for a wide range of applications including telephony functions, network echo cancellers, fax/data functions, voice coders and other functions.  
      Telephony functions that may be incorporated in the system include: DTMF—Dual Tone Modulation (or Multi) Frequency generation and removal; MFD—Multi-Frequency Tone Detection; UTD—Universal Call Progress Tone Detection; FMTD—FAX and Modem Tone Detection Tone Generator—single, dual, and modulated; and VAGC—Voice Activity Detection with Automatic Gain Control. Network Echo Cancellers may include ITU G.168-multiple reflector (up to 128 ms tail) and ITU G.168-single reflector (up to 48 ms tail). Fax/Data functions that may be incorporated in the system include caller ID, caller ID with call waiting, fax relay of T.38 and I.366.2, High Level Data Link Control (HDLC) transmit/receive, and full-duplex speaker phone. Voice coders may include G.726, G.728-low delay coders; G.729, G.729A, G.729B, G.729AB, G.729E; G.723.1, G.723.1A; Global System for Mobile Communication GSM-EFR, GSM-AMR; G.722.1-audio coders; and proprietary coders.  
      Referring now to  FIGS. 17-19 , Voice-over-DSL integrated access devices (IADs) often require the integration of a broad range of complex technologies, including: Asynchronous Transfer Mode (ATM), packet, bridging, IP, and routing networking; real-time, toll-quality, voice traffic processing; voice encode/decode, echo cancellation, Dual Tone Modulation Frequency (DTMF) and other algorithms; and voice control and public-telephone-system interworking protocols. These technologies impose silicon and software requirements, and require a high degree of integration to achieve seamless operation.  
      Virata&#39;s Azurite™ chipsets, for example, are integrated voice and data solutions. targeted at DSL Integrated Access Devices (IADs). These chipsets significantly increase performance, lower cost and speed time to market by integrating the Voice-over-DSL system components. Virata&#39;s Azurite™ 3000-series chipset features Virata&#39;s Magnesium™ DSP, Helium™ communications processor, and full software stack. Virata&#39;s PHY neutral Helium communications processor can be used with any external Digital Subscriber Line Physical Layer Device (DSL PHY), whether xDSL, Asymmetric Digital Subscriber Line (ADSL), Symmetric Digital Subscriber Line (SDSL), or other, making the 3000-series suitable for a broad range of DSL IADs. Virata&#39;s Azurite 4000-series chipset features Virata&#39;s Magnesium DSP, Beryllium communications processor, and full software stack. Virata&#39;s Beryllium communications processor includes a built-in ADSL PHY, enabling the 4000-series to achieve the very highest level of integration for ADSL IADs.  
      In one embodiment, the present invention may be incorporated in components used in DSL Central Office (CO) Equipment. CO equipment often comprises high performace processors with built-in peripherals and integrated communications protocol stacks directed to a variety of CO equipment applications. For instance, one possible application for the inventive solutions in Central Office/Digital Loop Carrier (CO/DLC) environments involves a Digital Subscriber Line Access Multiplexer (DSLAM) line card. For instance, Virata&#39;s Helium processor and ISOS software can be used to concentrate up to seven double-buffered (fast and interleaved path) ADSL ports or alternatively up to 13 single-buffered (interleaved path only) ports, assuming in both cases a double-buffered port facing upstream or connected to a backplane in DSLAM or miniSLAM applications. Helium&#39;s high speed UTOPIA 2 interface can support a variety of different DSL PHY devices (e.g., ADSL, SHDSL (single-line high-bit-rate digital subscriber line or symmetrical high-density digital subscriber line), etc. Multiple devices can be used together to support line cards with greater numbers of ports. Helium can be booted from either local memory or remotely from a central processor/memory.  
      The software provided may support a variety of Asynchronous Transfer Mode (ATM) functions such as Operations and Management (OAM), priority queuing, traffic shaping (constant bit rate (CBR), real time (rt)-variable bit rate (VBR), non real time (nrt)-VBR), policing (cell tagging) and congestion management (Early Packet Discard (EPD), Partial Packet Discard (PPD)). In the control plane, Helium comes with a Q.2931 call processing agent which sets up switched virtual circuits (SVCs) within which associate the assigned ATM label (Virtual Path Identifier/Virtual Channel Identifier (VPIVVCI)) to a physical T1 Wide Area Network (WAN) port. In the management plane, Helium comes with a simple network management protocol (SNMP) agent which can be used by Element Management to configure or monitor the performance of the module, for example, detecting out of service events due to link failure, maintaining and reporting cyclic redundancy check (CRC) error counts, etc.  
      In another example, Virata&#39;s Helium™ processor is used to support protocol conversion between ATM and Frame Relay. Such an adaptation could be used in a DSLAM or ATM switch to transport data to an Internet Service Provider (ISP), for example over a Frame Relay network. ATM cells from the switch backplane are received by Helium via the UTOPIA-2 interface and converted into an AAL-5 PDU (Protocol Data Unit). The resulting PDTJ is encapsulated into a HDLC header with a Data Link Connection Identifier (DLCI) to complete the conversion into Frame Relay. The process is reversed in the other direction as indicated in the protocol stacks diagram. In the control plane, Helium comes with a Q.2931 call processing agent which sets up SVCs within which associate the assigned ATM label (VPI/VCI) to a physical T1 WAN port. In the management plane, Helium comes with an SNMP agent which can be used by Element Management to configure or monitor the performance of the module, for example, detecting out of service events due to link failure, maintaining and reporting CRC error counts, etc.  
      In yet another example, Virata&#39;s Helium processor is used in the design of an Inverse Multiplexing over ATM (IMA) line card for an ATM edge switch or miniSLAM. Helium&#39;s UTOPIA 1/2 interface supports up to 14 separate devices. The software supports traffic management functions such as priority queuing, traffic shaping and policing. During congestion for example, low priority cells (Cell Loss Priority (CLP)=1) are either delayed or discarded to make room for high priority and delay intolerant traffic such as voice and video. Or alternatively, EPD (Early Packet Discard) may be invoked to discard all cells that belong to an error packet. In the control plane, Helium comes with a User Network Interface (UNI) 3.0/4.0 signaling stack for setting up and taking down SVCs. In the management plane, Helium comes with an SNMP agent and Telnet application that can be used by Element Management to configure or monitor the performance of the IMA module.  
       FIG. 17  illustrates an example of DSL Home/Office Routers and Gateways Hardware. As shown in  FIG. 17 , IAD  1700  includes standard telephony jacks  1710  whereby a standard telephone line is connected to a Voice DSP via a Codec/SLIC (Serial Line Interface Circuit)  1712 . This may occur locally, such at a Private Branch Exchange (PBX) or Small Office/Home Office (SOHO) gateway as often used in home office and small business situations, or can occur remotely at a central office. The SLIC  1712 , such as a four-port SLIC, may be connected to a Voice DSP  1720 , which may support comfort noise generator functionality, as shown by  1730 . The Voice DSP (e.g., Magnesium)  1720  and the higher level, such as ATM, information processing and packetization processor reside at the central office or at the PBX/gateway. Voice DSP  1720  may be connected to Helium  1722 . Virata&#39;s Helium is a single chip, highly integrated ATM switching and layer 2/3 processing device. Helium™ further includes a network processor that controls the direct connections to Ethernet and Universal. Serial Bus (USB), as well as other physical interfaces. For example, Helium  1722  may be connected to 10BaseT  1724 , Synchronous Dynamic Random Access Memory (SDRAM)  1726 , Electrically Erasable Programmable Read Only Memory (EEPROM)  1728 , DSL PHY  1740 , as well as other interfaces. DSL PHY  1740  may also be connected to ADSL  1744 , which may be connected to Line Drivers and Filter  1746 . An interface to DSL may be provided at  1748 . In addition, a power supply unit may be provided at  1750 , which may support +5 volts (V) or other amount.  
      The Voice DSP  1720  encodes/compresses the voice data and the silence portion of the signal may be deleted or compressed and encoded by a comfort noise generator function, as shown by  1730 . After being processed for IP or DSL transmission or the like at the higher level processor, the compressed voice data is transmitted over the network to a receiver device where the information is decoded layer by layer and the data packets are ultimately decoded to extract voice data. A comfort noise generator may reside at the receiver station, such as at a Voice DSP, for decoding the silence portion of the signal based on data from the source, or, if the silence data has been deleted altogether, may reconstruct the noise data for insertion during the silence portion of the signal. This reconstructed noise data may be based on noise data detected or estimated from the voice data, from historical data, or from a stored profile or the like. By removing the silence data, the system affords savings in bandwidth. However, it is desired to avoid the sensation of the signal cutting in and out by reconstructing and inserting comfort noise data during the periods of silence.  
      Voice data compression and encoding can be accomplished using Virata&#39;s G.729-Annex B, and G.729A-Annex B, Conjugate-Structure Algebraic-Code-Excited Linear-Predictive (CS-ACELP) voice coder algorithms. Virata&#39;s G.729A-Annex B CS-ACELP voice coder algorithm module implements the ITU-T G.729-Annex A and Annex B voice coder standard. Annex B to G.729A defines a voice activity detector and comfort noise generator for use with G.729 or G.729A optimized for V.70 DSVD (Digital Simultaneous Voice and Data) applications. It compresses codec (coder/decoder) or linear data to 8 KBps code using the Conjugate-Structure Agebraic-Code-Excited Linear-Predictive Coding function. Virata&#39;s G.729-Annex B CS-ACELP voice coder algorithm module implements the ITU-T G.729-Annex B voice coder standard. Annex B to G.729A defines a voice activity detector and comfort noise generator for use with G.729 or G.729A optimized for V.70 DSVD applications. It compresses codec or linear data to  8  KBps code using the CS-ACELP coding algorithms.  
      As an alternative to the MIPS intensive G729 compression algorithms, the present invention allows for compression using G726 standard in combination with the Comfort Noise Generator (CNG) techniques described hereinabove. The CNG resides, for example, in a vCore™ software module on the voice DSP, such as Virata&#39;s Magnesium processor. The voice data is compressed and encoded and the packets are forwarded for higher level packetization layering and ultimately transmitted along a communication network. Upon reaching a destination receiver, the voice data is decoded and a CNG decodes the data and constructs or reconstructs noise information to be included with the voice information as has been herein described.  
       FIG. 18  illustrates a software architecture, according to an embodiment of the present invention. DSP-Main  1822  application may be implemented to handle system-level data flow from an audio channel to a host processor via a host interface layer (HST). In particular, DSP-Main  1822  may support low overhead processing  1824  and low latency processing  1826 , as well as other types of processing. A FXS driver  1836  (TFXS) handles state transitions and signal debouncing for the FXS event interface. The lower layers include device drivers for codec  1838 , SLIC  1840 , and a device driver  1834  for the audio channel (CNL). A boot loader  1830  may load the DSP image after startup. The system provides a combination of minimal overhead, minimal CPU utilization, minimal latency and ease of integration, among other features.  
       FIG. 18  illustrates Virata&#39;s Helium processor  1810  connected to Virata&#39;s Magnesium processor  1820 , which is connected to a telephone  1850  or other device via Codec/SLIC  1852 . Helium processor  1810  may support a voice programming interface  1812  as well as a hardware abstraction layer  1814 . Other functionality may be supported by processor  1810 . Magnesium processor  1820  may include share memory  1828 , boot loader  1830 , host interface  1832 , various algorithms (e.g., comfort noise generator  1842 )  1842 - 1848 , various drivers (e.g.,  1834 - 1840 ) as well as other functions.  
       FIG. 19  illustrates a DSL integrated access device software, according to an embodiment of the present invention. As shown in  FIG. 19 , voice DSP software may include call setup  1910 , voice processing  1912 , and management  1914 . Other voice software may be provided. Comfort noise generator functionality, as shown by  1916 , of the present invention may be supported by the voice processing function at  1912 . Voice DSP Interface  1920  provides an interface between voice DSP software and communications processor software. Communications processor software may include telephony signaling  1922 , DSP interface  1924 , Common Service Specific Convergence Sublayer (SSCS) Interface  1926 , Jet Stream SSCS  1928 , Copperoom SSCS  1930 , Proprietary SSCS  1932 , Router  1934 , Network Address Translation (NAT), Point to Point Tunneling Protocol (PPTP)  1936 , Transmission Control Protocol on top of the Internet Protocol (TCP/IP)  1938 , Spanning-tree bridge  1940 , Open Systems Interconnection (OSI) Layer 2  1942 , Request for Comments RFC  1944 , Point to Point Protocol over ATM (PPPOA)  1946 , Point to Point Protocol over Ethernet (PPPoE)  1948 , ATM Adaptation Layer (AAL)-2 Common Part Convergence Sublayer (CPCS)  1950 , ATM Adaptation Layer (AAL)-5  1952 , Signaling  1954 , Traffic Management  1956 , Broadband Unified Framework (BUN) device driver framework  1958 , ATM Driver  1960 , and/or other functionality.  
      Data encapsulation functionality may be provided by various methods, including RFC  1483 , as shown by  1944 ; PPPoA  1946  and PPPoE  1948 , for example. Encapsulations, as well as the logical connections below them, may be treated generically. For example, encapsulations may be attached to the Spanning-tree bridge  1940  or IP router  1934 . An end result may include the ability to easily route or bridge between ports with traditional packet interfaces and ports with encapsulations or simply between ports with encapsulations. RFC  1483 , as shown by  1944 , provides a simple method of connecting end stations over an ATM network. PPPoA  1946  enables user data to be transmitted in the form of IP packets. In one example, PPPoE  1948  encapsulation may be used to transport PPP traffic from a personal computer (PC) or other device to a DSL device over Ethernet and then over a DSL link using RFC  1483  encapsulation. A PPPoE relay agent may act as bridge for determining on which session locally originated PPPoE traffic belongs. AAL-2 (e.g.,  1950 ) may be used for transporting voice traffic. AALs may include at least two layers. A lower layer may include a CPCS for handling common tasks such as trailer addition, padding, CRC checking and other functions. An upper layer may include a SSCS for handling service specific tasks, such as data transmission assurance. AAL-5 (e.g.,  1952 ) may provide efficient and reliable transport for data with an intent of optimizing throughput and perform other functions.  
      AAL 5  1952  is a type of ATM adaptation layer for defining how data segmentation into cells and reassembly from cells is performed. Various AALs may be defined to support diverse traffic requirements.  
      Signaling  1954  may provide a means for dynamically establishing virtual circuits between two points. Spanning-tree bridges  1940  may provide a transparent bridge between two physically disjoint networks with spanning-tree options. A spanning-tree algorithm may handle redundancies and also increase robustness.  
      BUN device driver framework  1958  provides a generic interface to a broad range of packet and cell-based hardware devices. BUN may be termed a device driver framework because it isolates hardware-independent functions from hardware-depenient primitives and, in doing so, simplifies device driver development, maintenance and debugging.  
      ATM Driver  1960  passes data between application software tasks and a physical ATM port, for example, ATM Driver  1960  may perform ATM cell segmentation and reassembly, AAL encapsulation, and multiplexes concurrent data streams.  
      While the foregoing description includes many details and specificities, it is to be understood that these have been included for purposes of explanation only, and are not to be interpreted as limitations of the present invention. Many modifications to the embodiments described above can be made without departing from the spirit and scope of the invention.  
      The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the present invention, in addition to those described herein, will be apparent to those of ordinary skill in the art from the foregoing description and accompanying drawings. Thus, such modifications are intended to fall within the scope of the following appended claims. Further, although the present invention has been described herein in the context of a particular implementation in a particular environment for a particular purpose, those of ordinary skill in the art will recognize that its usefulness is not limited thereto and that the present invention can be beneficially implemented in any number of environments for any number of purposes. Accordingly, the claims set forth below should be construed in view of the full breath and spirit of the present invention as disclosed herein.