fig1 is a block diagram of one example of one implementation of an equalization system 100 . the equalization system 100 is able to improve bass response from one or more subwoofers positioned within a room such that the bass response within a desired listening area of the room is relatively flat across a predetermined low - frequency range . the equalization system 100 may be used to equalize frequency responses from in a variety of rooms having unique characteristics . fig2 illustrates one example of a listening environment in which the equalization system 100 of fig1 may be employed . while the equalization system 100 may be used to equalize bass response in a variety of rooms of various types , sizes and shapes , for purposes of illustration , the equalization system 100 may be utilized in a rectangular room 200 having four walls 202 , a floor 204 and a ceiling ( not shown ). subwoofers sub 1 , sub 2 , sub 2 , and sub 4 may be positioned such that one subwoofer is in each corner of the room . further , the room may have seating positions for four people identified as p 1 , p 2 , p 3 , and p 4 . as illustrated in fig1 , to adjust the bass response of the subwoofers sub 1 , sub 2 , sub 2 , and sub 4 , each of the seating positions may have an associated microphone m 1 110 , m 2 112 , m 3 114 , and m 4 116 . a receiver 108 provides the input signal 106 to digital processor 104 which may be coupled to four subwoofers sub 1 , sub 2 , sub 3 , and sub 4 located in the corners of the rectangular room 200 of fig2 . alternatively , the equalization system may utilize only one microphone that may be moved among the seating positions p 1 , p 2 , p 3 , and p 4 to equalize the system and adjust bass response . as further illustrated in fig1 , the equalization system 100 may include a preprocessor 102 that is capable of generating test signals and determining the coefficients for each filter fir 1 126 , fir 2 128 , fir 3 130 , and fir 4 132 corresponding respectively to each of the subwoofers sub 1 , sub 2 , sub 3 , and sub 4 . the filters fir 1 126 , fir 2 128 , fir 3 130 , and fir 4 132 may be located in a processor 104 . as illustrated , the equalization system 100 may utilize finite impulse response ( fir ) filters . the preprocessor 102 may contain a signal generator that may send output signals , one at a time , to each of the subwoofers sub 1 , sub 2 , sub 3 , and sub 4 to measure the impulse response of that subwoofer at each of the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 . for example , the signal generator may output a logarithmic frequency sweep for a predetermined amount of time to each of the subwoofers sub 1 , sub 2 , sub 3 , and sub 4 sequentially . the logarithmic frequency sweep allows the signal generator to send out a signal covering a broad frequency spectrum through the subwoofers . as an example , the output signal may be sent out for approximately four seconds . with each of the subwoofers sub 1 , sub 2 , sub 3 , and sub 4 sequentially sending the output signal over a period of time , the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 located at the listing positions p 1 , p 2 , p 3 , and p 4 within the room 200 may measure the signal responses independently or simultaneously . for instance , the signal generator 102 may send an output signal through subwoofer sub 1 , so that the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 may measure the impulse response of the room 200 from the signals generated at the location of subwoofer sub 1 within the room 200 . the signal generator 102 may then send another output signal through the subwoofer sub 2 , so that the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 may measure the impulse response of the room due to the output signal source generated from the location of subwoofer sub 2 within the room 200 . similarly , subwoofer sub 3 and sub 4 may each , independently , send the output signal for the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 to measure the impulse response of the room 200 due to the output of each respective subwoofer sub 3 and sub 4 . the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 may convert the acoustic signals into electrical signals . before the electrical signals are provided to the preprocessor 102 , the electrical signals may be digitized at the predetermined rate using a / d converters , such as a / d converters 118 , 120 , 122 , and 124 . through the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 , the preprocessor 102 may capture a predetermined number of impulse response samples per second for each combination of subwoofer sub 1 , sub 2 , sub 3 , and sub 4 and microphone m 1 110 , m 2 112 , m 3 114 , and m 4 116 . the captured impulse response may be down - sampled to yield n samples for each measured impulse response . with four subwoofers ( sub 1 , sub 2 , sub 3 , and sub 4 ) and four microphones ( m 1 110 , m 2 112 , m 3 114 , and m 4 116 ), this results in a set of sixteen impulse responses where each set has n numbers of samples . for example , the preprocessor 102 may capture n = 2048 samples at a sampling rate of 750 samples per second . in other implementations , different numbers of subwoofers , microphones , and samples may be used along with a different sampling rate . the preprocessor 102 receives the measured sweep signal responses of the room from the microphones m 1 110 , m 2 112 , m 3 114 , and m 4 116 . the preprocessor 102 then calculates the filter coefficients for use by the fir filters fir 1 126 , fir 2 128 , fir 3 130 , and fir 4 132 . the preprocessor 102 is coupled to and in signal communication with a processor 104 that implements the filters fir 1 126 , fir 2 128 , fir 3 130 , and fir 4 by modifying each of the audio signals sent to the corresponding subwoofers sub 1 , sub 2 , sub 3 , and sub 4 to substantially equalize the in - room frequency responses for the sound generated by the subwoofers sub 1 , sub 2 , sub 3 , and sub 4 . as further illustrated in the fig1 , the system may receive an input audio signal 106 from a receiver 108 . the processor 104 may filter the input audio signal 106 with the four fir filters 126 , 128 , 130 , and 132 resulting in four audio signal outputs ( one for each subwoofer ). the input audio signal 106 is shown as originating with a receiver 108 , but in practice any type of audio or audio - visual device may generate the input audio signal 106 , for example tvs , dvd players , vcrs , dvrs , stereos , amplifiers and turntables , to name a few . further , the processor 104 may be a variety of processors , such as a dsp or microprocessor . further , the filters may be a filter type other than a fir filter . additionally , the different modules and / or components of fig1 may be implemented in one or more semiconductor packages , e . g ., chips . fig3 is a flow diagram 300 of an overview of one example of the filter design method as implemented in preprocessor 102 of the equalization system 100 of fig1 . for purposes of illustrations , the following discussion regarding the steps utilized by the equalization system will explain the operation of the equalization system 100 utilizing four subwoofers n sub = 4 and for microphones n mic = 4 , within the listening environment , as depicted in fig2 . those skilled in the art will , however recognize that the equalization system 100 may be utilized with any combination of subwoofers and microphones in a listening environment . regardless of the number of subwoofers and microphone in a listening environment , the audio signal sent to one or more subwoofers may be filtered in accordance with the example method outlined in fig3 and in accordance with the following description . the flow diagram 300 starts with step 302 where the input data may be prepared to substantially equalize the frequency responses of the room . preparing the input data 302 generally includes computing the impulse responses of the room and transforming the impulse response into the frequency domain . the step of preparing the input data 302 will be described in further detail in fig4 . once the impulse responses are transformed into their frequency domain , in step 304 , an inverse of the frequency response matrix may be determined . the resulting inverse frequency responses may be utilized to , in effect , undo the coloration added by the walls of a room . in other words , filtering each of the audio signals with its respective inverse filter frequency response and sending the filtered signals to their respective subwoofers may produce ideal frequency responses . the inverse filters , however , may have local sudden peaks and dips where such sudden gains may exceed the allowable gains that a subwoofer may handle . as such , in step 306 , the local peaks and dips in the inverse filter frequency response may be smoothed using a complex smoothing method . this provides approximate inverses for the frequency responses of the room . in step 308 , global equalization may occur , which is accomplished by globally equalizing the result after approximate inverse filtering so that a target function describing transitions at the low and high frequency band edges may be approximated . global equalization may also use a smoothing method that addresses peaks and dips separately . as subwoofers generally operate below 100 hz , in block 310 , a limit may be placed on the gain that may be applied to the subwoofer outside of the desired low - frequency range to protect the subwoofer , such as below 20 hz and / or above 100 hz . in step 311 , while still in the frequency domain , group delay equalization may occur with respect to a global minimum that replaces filters by their minimum - phase versions outside of their pass - band . in addition , a decreasing butterworth type group delay response may be superimposed . in step 312 , the inverse of the obtained equalization filters is then used to determine the filter coefficients to process each of the audio signals . in block 314 , a final global equalization filter is determined to improve the overall result after time domain processing . fig4 is a flow diagram 400 showing further details of the steps for preparing the input data , as illustrated in step 302 of fig3 . as illustrated in fig4 , to prepare the input data , the impulse responses of the room are determined in step 402 . once the impulse responses have been measured 402 , any common time delay from the impulse responses may be removed , as set forth in step 404 . removing common time delay is done to allow the solvability of the mathematical problem of complex smoothing . for instance , with regard to the output signal sent by subwoofer sub 1 , the microphone m 1 , positioned at p 1 , may be closest to sub 1 . as such , the microphone m 1 will receive the output signal from sub 1 before the other microphones m 2 , m 3 and m 4 , positioned at p 2 , p 3 and p 4 respectively . the time it takes for the output signal from sub 1 to reach the microphone m 1 is common to the other microphones m 2 , m 3 and m 4 . this time may be defined as a common time delay with regard to the impulse measured by the four microphones m 1 - m 4 for the output signal sent by the subwoofer sub 1 . likewise , a corresponding common time delay may be measured for output signals sent by each of the other subwoofers sub 2 - sub 4 . for instance , a common time delay for the output signal sent by the sub 3 is the time it takes for the output signal from the sub 3 to reach its closest microphone . the minimum delay of all the measured impulse responses is the common time delay . the common time delay may be offset or deducted from all the impulse responses measured by the four microphones m 1 - m 4 . in step 406 , the input data of the time domain impulse responses of the room may be transformed into frequency domain using , for example , fast fourier transform (“ fft ”). for example , with four microphones m 1 - m 4 and four subwoofers sub 1 - sub 4 , a set of sixteen impulse responses may be measured where each set has n number of samples . each impulse response is transformed into the frequency domain using fft . in this example , an n point fft is employed that yields n complex values for each measured impulse response . as such the resulting set of [ n min × n sub ]× n complex fft points are represented as n number of n mic × n sub matrices ai , where i = 1 . . . n . at each i or frequency point , the fft provides amplitude and phase . fig5 is a flow diagram 500 showing further details of the steps for determining the inversion for the frequency response , as illustrated in step 304 of fig3 . the flow diagram starts at step 502 by determining the number of microphones n mic used to measure the impulse responses and the number of subwoofers n sub in the room . a decision is then made at step 504 , if n mic = n sub , then in step 506 , an exact matrix inversion method may be used to find the exact inverse of the impulse responses . otherwise , if n mic & gt ; n sub , then in step 508 , a pseudo - inverse method may be used to find the inverse of the impulse responses . with the impulse responses transformed into the frequency domain ( see step 406 of fig4 ), the inverse matrices may be calculated for each of the frequency points to determine the ideal equalization at that frequency point . in this regard , n number of inverse matrices b i , where i = 1 . . . n , may be determined . this results in n complex - value matrices b i , such that a i b i = 1 . in the case that n mic & gt ; n sub , the method of pseudo - inverse , step 508 , may be used to calculate b i . the well - known method of pseudo - inverse minimizes the mean squared error between the desired and actual result . expressed mathematically , b i is computed such that ( 1 − a i b i )* x ( 1 − a i b i ) is minimized where * denotes a complex - conjugate operation . in step 510 , once the inverse matrices have been determined , a target function may be chosen for each frequency point for each of the microphone positions m 1 through m 4 . the target function is the desired frequency response at each listening position . the target function may be a complex - value vector containing n mic elements t i ( i = 1 . . . n ). in this example , with four microphones m 1 - m 4 , t i contains four complex - valued elements per frequency point . a simple example of target t i is a unity vector . the vectors f i that describes n sub filters at a particular frequency point i ( i = 1 . . . n ) are then computed as matrix multiplication f i = b i t i . the vectors f i describe filters at a particular frequency point i ( i = 1 . . . n ) that would perform an exact inverse ( ideal equalization ). the vectors f i in effect undo the coloration added by the walls of the room so that multiplying a i f i = a i b i t i = t i results in an idealized equation . fig6 illustrates a plot 600 of the curves f ( 1 ), f ( 2 ), f ( 3 ), and f ( 4 ) representing the inverse frequency responses as obtained after the matrix inversion ( step 304 in fig3 ). the curves represent the logarithmic magnitude of the filters f ( k )( k = 1 . . . n sub = 4 ) as obtained after the matrix inversion . the target function used in this example may be a unity vector t i =[ 1 , 1 , 1 , 1 ], i = 1 . . . n . the frequency axis f is f =( 1 . . . n / 2 ) _ / n * fa , where n is fft length and fa = 750 hz is the sampling frequency . as can be seen in the plot 600 , there are sudden peaks and dips as indicated by markings a , b , c , d , for example . directly applying the filters f ( k ) to the output signals sent to the sub 1 - sub 4 to equalize the frequency responses within the room may damage the subwoofers because the peaks at certain frequencies require applying significant gains at those frequencies that may be too high for the subwoofers to handle . in other words , the vector f ( k ) may impose gains at certain frequencies that may exceed the maximum amount of gain that the subwoofers can handle . smoothing throughout the whole frequency range may be done to limit the length of the resulting filter in the time domain , which is known to converge to zero more rapidly after smoothing . the following is further discussion of smoothing the inverse of the matrices represented by the step 306 of fig3 . with the sudden peaks and dips in the frequency response vectors f ( k ), the ideal equalization may not be directly applied to the output signal sent to the subwoofers . the peaks and dips in the vectors f ( k ), however , may be minimized by smoothing the complex - value vectors f ( k ) across frequency . methods for smoothing of complex - value vectors f ( k ) across a frequency is know in the art . for example , smoothing may be accomplished through the method described in an article titled “ generalized fractional - octave smoothing of audio and acoustic responses ,” by panagiotis d . hatziantoniou and john n . mouijopoulos , published april of 2000 , j . audio eng . soc ., vol . 48 , no . 4 , pp . 259 - 280 . in particular , smoothing of the complex - valued vectors f ( k ) may be carried out by computing the mean values separately for the real and imaginary parts , along a sliding frequency - dependent window , resulting in fs ( k ). for example , a smoothing index q between 1 . 0 and 2 . 0 may be used , where i *( q − 1 / q ) denotes the width of the frequency - dependent sliding window , where i is the frequency index . sliding windows such as hanning or welch window may be used . note that it may be useful to perform smoothing in two or more separate frequency bands by using a different value for each frequency band . at higher frequencies , fluctuations across space and frequency in a room are usually larger , so that a higher q index may be utilized . since the subwoofer operates mainly below 80 hz , a high accuracy of the inversion filter above that frequency may not be necessary or even desirable because it may not apply to the whole listening area consistently , due to rapid fluctuations . turning to fig7 , a plot of curve f ( 2 ) and the smoothed curved fs ( 2 ) is shown . curve f ( 2 ) may be smoothed in accordance with the method described above . the magnitude of the unsmoothed spectrum of the filter f ( 2 ) that may be applied to the output signal sent to the subwoofer sub 2 , and curve fs ( 2 ) represents the smoothed version of filter f ( 2 ). note that in curve fs ( 2 ), the local peaks and dips are smoother than in curve f ( 2 ) such that much of the sudden peaks and dips present in curve f ( 2 ) are more gradual in curve fs ( 2 ). as such , curve fs ( 2 ) is an approximation of the complex - valued filter f ( 2 ) so that equalization may be applied to the output signal to the subwoofer sub 2 without the local excessive gain . likewise , in fig8 , an illustration 800 of four curves fs ( 1 ), fs ( 2 ), fs ( 3 ), and fs ( 4 ) is provided that represents the smooth version of the curves f ( 1 ), f ( 2 ), f ( 3 ), and f ( 4 ) in fig6 is shown . in fig9 , a flow diagram 900 is shown that further details the steps of determining the global equalization in step 308 of fig3 . the complex smoothing of each of the complex - valued filters f ( 1 ) through f ( 4 ), as illustrated in fig8 above , removes the local fluctuations of peaks and dips but the extreme gains may be still present . for example , subwoofers are generally designed to handle a maximum gain of about 15 db to about 20 db at certain frequencies , with respect to the average gain . in fig7 , gains of greater than 30 db below 20 hz and 60 db above 100 hz occur . the subwoofers typically may not handle such extreme gains . to manage the gains , a global equalization ( eq ) may be performed . one way of calculating the global eq is through the method described in the flow diagram 900 of fig9 . in step 902 , the actual responses at each of the microphones positions or seats ( listening positions ) fy ( j )( j = 1 . . . n seat ) may be calculated by multiplying the original matrix a with fs , as calculated in the above smoothing method . in other words , fy = a * fs . in step 904 , an upper curve fy max may be determined by taking the maximum magnitudes max { fy ( 1 . . . n seat )} for each of the frequency points . as such , all of the responses at the seats are below the curve fy max . in fig1 , the lower graph shows the frequency responses at the four microphones positions p 1 , p 2 , p 3 , and p 4 , after the filtering in accordance with the filters , fs ( 1 ), fs ( 2 ), fs ( 3 ), and fs ( 4 ) of fig8 , and the upper graph shows the upper limit of the curves , raised by 10 db . the curve fy max is raised by 10 db to better show the fy max curve . this means that no response is greater than the curve fy max along any frequency point . the curve fy max denotes the maximum magnitudes in db within the whole frequency range of 0 hz to half the sample rate . subwoofers , however , are designed to operate optimally in a more limited range than the above frequency range . as such , in step 906 of fig9 , the upper curve fy max may be limited within a predetermined frequency range that would allow the subwoofers to operate at their optimal frequency range . in this regard , a global eq filter fr may be computed to operate in the predetermined frequency range by dividing a target function t by fy max or fr = t / fy max . the target function t is real - valued having magnitude frequency responses of high pass and low pass filters that characterize the frequency range where the respective transducer ( subwoofer ) optimally works . typical filters are butterworth high pass filters of order n = 2 . . . 4 ( corner frequencies 20 . . . 40 hz ), and butterworth low pass filters of order n = 2 . . . 4 ( corner frequencies 80 . . . 150 hz ). in fig1 , a plot 1100 of an application of the global equalization filter and its smoothed version is shown . the log - magnitude response of the global eq filter fr has peaks that may interfere with the quality of the sound . in this regard , in step 908 of fig9 , the curve fr is smoothed to remove the peaks in the curve fr . the smoothing method may be used to determine an intermediate response frs that is the smoothed version of fr . the peaks in fr in essence may be “ shaved off ” by computing the difference between frs and fr , and rectifying the differences . in fig1 , a diagram 1200 with the top curve 1202 representing the difference between smoothed frs and unsmoothed frequency responses fs ( raised by 10 db ) and lower curve 1204 representing the rectified difference ( lowered by 10 db ) is shown . turning now to fig1 , an illustration 1300 of the frequency response of the global equalization filter is shown . the log - magnitude response of the global eq filter fr has peaks in the curve fr that may be removed . a smoothing method , as described above , may be used to determine an intermediate response frs that is the smoothed version of fr . the peaks in fr in essence may be “ shaved off ” by computing the difference between frs and fr , and rectifying the difference . referring back to fig1 , the top curve 1202 represents the difference between frs and fr ( raised by 10 db ), and the lower curve 1204 representing the rectified difference ( lowered by 10 db ). then , as shown in fig1 , the final frequency response of the global eq filter frsf 1300 may be obtained by subtracting the rectified difference from the original filter fr that is the unsmoothed filter of fig1 . while the final frsf 1300 shown in fig1 shows dips , the number of peaks is reduced . the unwanted peaks would attempt to amplify frequencies where dips occur in the original response , requiring significant additional acoustic output from the subwoofer , thus reducing the maximum acoustic output of the system and potentially creating large peaks in other areas of the room . in fig1 , a flow diagram 1400 is provided that further details limiting the maximum gains in the global equation filter as shown in step 310 of fig3 . in block 1402 , the final eq spectrum feq is computed by multiplying the complex spectra fs of the individual eq filters , as determined above , with the global , real - valued magnitude spectrum frsf ( as determined above ), respectively . fig1 illustrates a graph 1500 of the frequency responses of the equalization filters feq ( 1 ), feq ( 2 ), feq ( 3 ) and feq ( 4 ) for each of the subwoofers after complex smoothing of the curves fs ( 1 ), fs ( 2 ), fs ( 3 ), and fs ( 4 ) of fig8 ( step 306 of fig3 ) and application of the global equalization filter illustrated in fig1 . in fig1 , it can be seen that there are still substantial gains above 200 hz and below about 20 hz . this may be due to the chosen target function that is not sufficient to limit the final gains as desired . therefore , in step 1404 of fig1 , limits may be placed on the gains below a predetermined low frequency and a predetermined high frequency . for example , a limit on the maximum gain may be applied by replacing the complex - valued feq such that the maximum magnitude is clipped to “ maxgain ” without altering the phase . maxgain may be a value prescribed by the user that depends on the capabilities of the particular subwoofer . preferably , different values of maxgain may be applied in different frequency bands . the resulting filters may be scaled so that the maximum gain does not exceed one ( 0 db ). fig1 shows graph 1600 of the filter eq spectra after applying maxgain and normalization to 0 db . as illustrated , the eq spectra may be normalized to 0 db to maximize the average gain . in fig1 , a flow diagram 1700 of an example performance of the group delay equalization step 311 of fig3 is illustrated . in step 1702 , the filters are replaced by their minimum - phase versions outside of the pass band . graph 1800 in fig1 shows smoothed group delay responses of four eq filters , as obtained after step 311 of fig3 . since phase is only important in a limited band of interest , the pass band , which usually lies between 20 hz and 80 hz , frequency responses outside of those frequency points may be replaced with identical magnitudes but minimum phase , using methods such as hilbert transform . the results of applying such a transform may be seen in the graph 1900 of fig1 . in step 1704 of fig1 , the design of the group delay equalization ( eq ) with respect to a global minimum is performed . to practically realize the filters as shown in fig1 , a constant delay would have to be added in the amount of the negative peak value of the group delay function . that value exceeds 50 samples in the shown example . this amount of latency would still be required for an equalized system featuring linear phase in the pass band . however , since flat magnitude response , not linear phase is required within the listening space , only the relative phase relations need to be preserved between the filters . an arbitrary eq filter , applied to each of the channels equally , would not affect those phase relations ( global eq ). fig1 illustrates the group delay responses as obtained after step 1702 in fig1 , and their global minimum , shifted by 10 db for better visibility . the eq filter may be an allpass with a group delay response equal to the inverse of that lower boundary curve . after applying that filter globally , the graph 2000 of fig2 results . this approach may be understood as a normalization of the eq filters with respect to their group delay , such that frequency - dependent , absolute values are minimized , whereas relative group delay values are preserved . in step 1706 of fig1 , a decreasing butterworth type group delay response is added . subwoofers may be designed to be typically maximum - flat , butterworth - type forth or sixth order high pass systems with a correspondingly rising group delay response towards low frequencies . in step 1706 , such a response is added as an additional global eq allpass filter . the main purpose is to gain some safety margin between the lowest group delay value and zero to minimize the amount of constant delay that is still required to achieve causality . this step results in additional latency reduction in most cases , but not necessarily all cases . a typical filter group delay response as obtained after step 1706 is shown in the graph 2100 of fig2 . turning to fig2 , the original , measured frequency responses at four locations , and the resulting equalized responses , as obtained after step 312 in fig3 is shown . the described approach still introduces magnitude errors in step 312 of fig3 , where the filters are shifted and windowed in the time domain . the main reason for this is that the smoothed group delay responses are being worked with , while discarding fine details . to improve the overall result , an additional global eq filter is introduced in step 314 of fig3 , which is similar to the one described in step 308 of fig3 . fig2 depicts the final result of performing the final global eq in step 314 of fig3 . in fig2 , the group delay responses of the eq filters of step 314 of fig3 is shown . the corresponding filter impulse responses are shown in fig2 . they feature a minimum amount of pre - ringing before their peak . this confirms the success of the described method , the goal of which was to minimize latency . finally , turning now to fig2 , fig2 illustrates the frequency responses of the room after the input signal to the corresponding subwoofers have been filtered to equalize the responses using the example equalization system of fig1 . fig2 illustrates that the resulting amplitude frequency responses are substantially consistent in the low frequency range relative to each other . this indicates that the responses at different locations within the listening room are substantially constant . this means that each person located at a listening position p 1 , p 2 , p 3 , or p 4 is provided with a substantially similar loudness level at each frequency point . in addition , the magnitude level is substantially constant or flat across a desired low - frequency level of between about 40 hz and about 100 hz so that sound level dropping off is substantially minimized . persons skilled in the art will understand and appreciate , that one or more processes , sub - processes , or process steps described in connection with fig1 through 25 may be performed by hardware and / or software . additionally , the low frequency equalization system may be implemented all , or in part , in software that would be executed within a processor or plurality of processor in a networked environment . examples of a processor include but are not limited to microprocessor , general purpose processor , combination of processors , dsp , any logic or decision processing unit regardless of method of operation , instructions execution / system / apparatus / device and / or asic . if the process is performed by software , the software may reside in software memory ( not shown ) in the device used to execute the software . the software in software memory may include an ordered listing of executable instructions for implementing logical functions ( i . e ., “ logic ” that may be implemented either in digital form such as digital circuitry or source code or optical circuitry or chemical or biochemical in analog form such as analog circuitry or an analog source such an analog electrical , sound or video signal ), and may selectively be embodied in any signal - bearing ( such as a machine - readable and / or computer - readable ) medium for use by or in connection with an instruction execution system , apparatus , or device , such as a computer - based system , processor - containing system , or other system that may selectively fetch the instructions from the instruction execution system , apparatus , or device and execute the instructions . in the context of this document , a “ machine - readable medium ,” “ computer - readable medium ,” and / or “ signal - bearing medium ” ( herein known as a “ signal - bearing medium ”) is any means that may contain , store , communicate , propagate , or transport the program for use by or in connection with the instruction execution system , apparatus , or device . the signal - bearing medium may selectively be , for example but not limited to , an electronic , magnetic , optical , electromagnetic , infrared , or semiconductor system , apparatus , device , air , water , or propagation medium . more specific examples , but nonetheless a non - exhaustive list , of computer - readable media would include the following : an electrical connection ( electronic ) having one or more wires ; a portable computer diskette ( magnetic ); a ram ( electronic ); a read - only memory “ rom ” ( electronic ); an erasable programmable read - only memory ( eprom or flash memory ) ( electronic ); an optical fiber ( optical ); and a portable compact disc read - only memory “ cdrom ” “ dvd ” ( optical ). note that the computer - readable medium may even be paper or another suitable medium upon which the program is printed , as the program can be electronically captured , via , for instance , optical scanning of the paper or other medium , then compiled , interpreted or otherwise processed in a suitable manner if necessary , and then stored in a computer memory . additionally , it is appreciated by those skilled in the art that a signal - bearing medium may include carrier wave signals on propagated signals in telecommunication and / or network distributed systems . these propagated signals may be computer ( i . e ., machine ) data signals embodied in the carrier wave signal . the computer / machine data signals may include data or software that is transported or interacts with the carrier wave signal . although the invention has been described with reference to a particular example of an embodiment , it will be apparent to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention . such changes and modification are intended to be covered by the appended claims .