Patent Publication Number: US-8121312-B2

Title: Wide-band equalization system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application Ser. No. 60/782,369 entitled “Wide Band Equalization in Small Spaces,” filed Mar. 14, 2006, which application is incorporated herein, in its entirety, by this reference. 
    
    
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention is generally related to an equalization system that improves the sound quality of an audio system in a listening room. In particular, the invention relates to an equalization system that improves the sound quality of an audio system based upon near- and far-field measurement data. 
     2. Related Art 
     The aim of a high-quality audio system is to faithfully reproduce a recorded acoustic event, such as a concert hall experience, in smaller enclosed spaces, such as a listening room, a home theater or entertainment center, a PC environment, or an automobile. 
     The perceived sound quality of an audio system in smaller enclosed spaces depends on several factors: quality and radiation characteristics of the loudspeakers (e.g., on- and off-axis frequency responses); placement of the loudspeakers at their connect positions according to the standard (for example, ITU 5.1/7.1); acoustics of the room in general (low frequency modes, reverb time, frequency-dependent absorption, effects of room geometry and dimensions, location of furniture, etc.); and nearby reflective surfaces and obstacles (e.g., on-wall mounting, bookshelves, TV sets, etc.). 
     In order to provide an optimum listening experience in such enclosed spaces, a digital “room equalization” system may be used. In general, equalization is the process of either boosting or attenuating certain frequency components in a signal. There are several types of equalization, each with a different pattern of attenuation or boost. Examples are a high-pass filter, bandpass filter, graphic equalizer, and parametric equalizer. 
     In a multiband parametric equalizer (“EQ”), center frequency, bandwidth (Q-factor) or peak shape, and gain (peak amplitude above a given reference) in each of the bands may be adjusted to flatten a measured frequency response at a listening location (e.g., a seat in a listening room), Typically, a cascade of second-order IIR (“infinite impulse response”) filter sections (“biquads”) is used to control frequency response. A digital signal processor (“DSP”) may generate test signals for each loudspeaker (e.g., either white or pink noise or logarithmic sweeps), in order to capture room responses at a desired listening location. For that purpose, an omni-directional microphone may be positioned at the listening location and connected to a signal analyzer or back to the DSP. 
     In  FIG. 1 , a test system  100  that uses an equalizer to produce a signal at the listening location that resembles the input signal is shown. In an example of operation, signal source  104  produces a test signal, which is amplified by the preamplifier  106  and processed by the equalizer  108 . The test signal is then amplified by the power amplifier  110  and transmitted to a loudspeaker  112 . The loudspeaker  112  reproduces the test signal as an acoustic pressure wave that is emitted from the loudspeaker  112 , which is then picked up by the test microphone  116  and passed to a signal analyzer  120 . 
     In this example of operation, the received test signal is observed at the signal analyzer  120  and, in response, the test signal may be adjusted accordingly through the equalizer  108 . In other implementations, the test microphone  116  may be directly in signal communication with the equalizer  108 , where the received test signal may be automatically processed by the equalizer  108 , which may include digital signal processors (“DSPs”). Additionally, the test microphone  116  may be positioned at a listening location in a room or hall, where it can then capture the impulse responses at that particular listening location. 
     In this example, if the equalizer  108  is a parametric EQ with multiple filters, the multiple filters may be set manually, so that, for example, a displayed response curve, on an output device (not shown) in signal communication with the equalizer  108 , becomes smoother, or automatically, with the aid of an external processor such as, for example a personal computer (“PC”) or design logic built into the DSP itself. In general, it is difficult and suboptimal to adjust a set of cascaded parametric filter sections because of overlap. Two or more of the parametric filter sections may affect the same frequency band of interest, which leads to the difficulty that a large number of parameters need to be adjusted simultaneously. At low frequencies, it is important to accurately suppress individual room modes. In order to avoid approximation errors and quantization noise, a FIR (“finite impulse response”) filter may be directly used and operated at a low sample rate (for example, utilizing decimation) to minimize processing cost. 
     In adjusting a frequency response, it is important to distinguish between resonances (e.g., loudspeaker cabinet material resonances, or standing waves at low frequencies in rooms) and interferences due to multiple reflections that lead to nulls (dips) in the frequency response. Resonances and room modes need to be suppressed, e.g., with a notch filter, while narrow-band interference dips strongly depend on the measurement position and generally should be left unaltered. An attempt to correct narrow-band interference dips may introduce high-gain peak filters that are perceived as resonances. 
     In an intermediate frequency band (between approximately 100 Hz to 1000 Hz), it is desirable to correct errors related to the source only, not the whole listening room. For example, eliminating sonic differences between the main stereo speakers and the center speaker, which may be close to a reflective surface such as a TV set, leads to an improved stereo image. This so-called “source-related” correction is independent of a particular listening location, whereas a complete room correction would be valid at a single point only. 
     At high frequencies (i.e., greater than 1000 Hz), the in-room response is normally not flat, but decreases with frequency. This may be addressed by a so-called “target function.” Equalization is performed such that the final response approximates the target function. However, the correct target function choice depends on the absorption properties of the particular room and the radiation characteristics of the loudspeakers, and is thus a priori unknown. In a (domestic) listening room solution, a set of near-field measurements close to the loudspeakers provides frequency response data above typically 1000 Hz, thus eliminating the need for a target function. In all automobile, an adjustable target function may be provided with the EQ algorithm. 
     Along with the foregoing considerations, there are many other factors to be considered when trying to optimize the sound quality audio systems utilized in small spaces such as listening rooms or cars. Therefore, there is always a continuing need to improve the sound quality of these audio systems, in particular, by improving the fully-automated equalization of the responses of loudspeakers located in these small spaces. 
     SUMMARY 
     A Wide-band Equalization System (“WBES”) for equalizing an audio system based on near- and far-field measurement data is disclosed. The WBES may include a subwoofer EQ having an FIR filter together with decimator and interpolator filters for processing low frequency signals. The WBES may also include satellite channels for processing mid- and high-frequency signals, where each satellite channel includes cascaded IIR filters that process mid-frequency and high-frequency signals. The WBES may also include one or more DSPs that perform the functions required by the IIR and FIR filters and may also generate test signals for a device under test. 
     In an example operation, the WBES may perform a method whereby low-frequency, mid-frequency, and high-frequency FIRs are generated from a captured set of room impulse responses (“RIRs”), with a low-frequency filter of the audio system then implemented using the low-frequency FIR, a decimator filter, and an interpolator filter. Mid- and high-frequency filters of the audio system may be implemented utilizing cascaded infinite impulse response (“IIR”) filters derived from the mid- and high-frequency FIRs. 
     Other systems, methods, features and advantages of the invention will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       The invention can be better understood with reference to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like reference numerals designate corresponding parts throughout the different views. 
         FIG. 1  shows a block diagram illustrating all example of a known room equalization system. 
         FIG. 2  shows a block diagram illustrating an example of an implementation of a Wide-band Equalization System (“WBES”) in accordance with the invention. 
         FIG. 3  shows a flow diagram illustrating an example of a method performed by the WBES of  FIG. 2  for correcting the response of an individual loudspeaker based upon near-field, high-frequency measurements. 
         FIG. 4  shows a graphical representation of an example of a plot of amplitude versus time (in samples) of a raw (i.e., unwindowed) and a windowed impulse response produced by the method described in  FIG. 3 . 
         FIG. 5  shows a graphical representation of an example of a plot of the frequency response obtained using an N-point FFT (N=8192), and the frequency response smoothed by a smoothing factor produced by the method described in  FIG. 3 . 
         FIG. 6  shows a graphical representation of an example of plots of the frequency responses of an ideal EQ filter, a smoothed version of that frequency response, and the smoothed version with those parts of the frequency response of an ideal EQ filter that lie above the smoothed version of the frequency response cut from the plot produced by the method described in  FIG. 3 . 
         FIG. 7  shows a graphical representation of an example of a plot of a frequency response of an EQ filter impulse response that has been scaled, limited to an upper frequency, and clipped to a maximum gain by setting filter values above a defined gain value to that value produced by the method described in  FIG. 3 . 
         FIG. 8  shows a graphical representation of an example of a plot of amplitude versus time (in samples) of an EQ filter impulse response that is time-limited produced by the method described in  FIG. 3 . 
         FIG. 9  shows a graphical representation of an example of a plot of frequency responses of an approximated IIR EQ filter impulse response produced by the method described in  FIG. 3 . 
         FIG. 10  shows a graphical representation of an example of a plot of frequency responses of a captured room impulse response, an EQ filter impulse response, and the result of applying the EQ filter impulse response to the captured room impulse response produced by the method described in  FIG. 3 . 
         FIG. 11  shows a flow diagram illustrating an example of a method performed by the WBES of  FIG. 2  for correcting the response of an individual loudspeaker based upon far-field, low-frequency measurements. 
         FIG. 12  shows a graphical representation of an example of a plot of amplitude versus frequency (in Hz) of an approximated low-frequency FIR EQ filter impulse response produced by the method described in  FIG. 11 . 
         FIG. 13  shows a flow diagram illustrating an example of a method performed by the WBES of  FIG. 2  for correcting the response of an individual loudspeaker based upon far-field, mid-frequency measurements. 
         FIG. 14  shows a graphical representation of an example of a plot of amplitude versus time (in samples) of a windowed far-field room impulse response produced by the method described in  FIG. 13 . 
         FIG. 15  shows a graphical representation of an example of plots of amplitude versus frequency (in Hz) of a raw, measured and a smoothed far-field spectrum at mid frequencies produced by the method described in  FIG. 13 . 
         FIG. 16  shows a graphical representation of an example of plots of amplitude versus frequency (in Hz) of a smoothed spectrum and an EQ filter frequency response produced by the method described in  FIG. 13 . 
         FIG. 17  shows a graphical representation of another example of plots of amplitude versus frequency (in Hz) of low- and mid-frequency EQ filter frequency responses produced by the method described in  FIG. 13 . 
         FIG. 18  shows a graphical representation of an example of plots of amplitude versus frequency (in Hz) of EQ filter frequency response and room responses before and after room correction produced by the method described in  FIG. 13 . 
         FIG. 19  shows a graphical representation of an example of a plot of a frequency response of a target function produced by the method described in  FIG. 13 . 
         FIG. 20  shows a graphical representation of an example of a plot of the frequency responses of three bands of an EQ filter produced by the method described in  FIG. 13 . 
     
    
    
     DETAILED DESCRIPTION 
     In the following description of examples of implementations of the present invention, reference is made to the accompanying drawings that form a part hereof, and which show, by way of illustration, specific implementations of the invention that may be utilized. Other implementations may be utilized and structural changes may be made without departing from the scope of the present invention. 
     In  FIG. 2 , a block diagram illustrating an example of an implementation of a wide band equalization system (“WBES”)  200  in accordance with the invention is shown. WBES  200  may include several signal processing modules that process low-, mid-, and high-frequency signals. As an example of operation, a low frequency signal  204  is generated by the bass manager  202 , which may also generate m mid- and high-frequency signals  206 , where m typically may be 5-7. The low-frequency signal  204  may be processed by a subwoofer EQ 208 utilizing a room equalization algorithm. The subwoofer EQ 208 includes a decimation filter  210 , a subwoofer equalizer FIR filter  212  of order n fir  (typically n fir =256 . . . 512), and an interpolation filter  214  to resample the signal to the original sample rate (typically, the decimation/interpolation ratio r=32 . . . 64). 
     Mid- and high-frequency signals  206  generated by the bass manager  202  may be processed by “satellite” channels  216   1 ,  2 , . . . , and m (typically, m=5 or 7). Each satellite channel  216  may include a cascade of mid-frequency-EQ second-order IIR biquad sections  218  A_ 1 , . . . , A_n 1 , and high-frequency-EQ biquads  220  sections B_ 1 , . . . , B_n 2 , where, as an example, n 1 =n 2 =3. 
     The filter coefficients for the mid-frequency-EQ IIR filters  218  and the high-frequency-EQ IIR filters  220  are based on measured room responses and may be obtained by utilizing a room equalization method. These IIR filters are higher order filters approximated from mid- and high-frequency FIRs designed from far-field and near-field measurement data.  FIGS. 3 ,  11 , and  13  illustrate examples of room equalization methods used to obtain the filter coefficients for the IIR and FIR filters shown in  FIG. 2 . These room equalization methods may be implemented in a common DSP that also performs real-time signal processing (i.e., the actual filtering). Turning to  FIG. 3 , a flow chart illustrating an example of a room equalization method is shown, where the room equalization method is designed for a near-field, high-frequency EQ configured to correct the impulse response of an individual loudspeaker and its immediate surroundings in a room above approximately 1 kHz. The process  300  starts in step  302  and in step  304 , a room impulse response (“RIR”) may be captured at a defined location in a listening room. As an example, an omni-directional test microphone may be positioned near a loudspeaker, e.g., at a distance of approximately 0.5-1.5 meters. In general, an excitation signal, which may be a signal produced by a logarithmic sine sweep, is fed to the device under test (“DUT”), in this case, the loudspeaker, and the response of the DUT is captured and compared with the original signal, as shown in  FIG. 1 . 
     In step  306 , the sequence (i.e., the impulse response) is multiplied by a rectangular or other time window, thus setting samples above a defined value to t 1  zero (where t 1  is typically 2-4 milliseconds (“ms”) or 100-200 samples at a sample rate of 48 kHz). This “windowing” suppresses unwanted reflections from boundaries that are not considered near-field. Next, in step  308 , the magnitude spectrum F(i), with i=1, . . . , N/2, is generated using an N-point FFT, where, for example, N=8192. In step  310 , the magnitude spectrum generated in step  308  is smoothed with a smoothing factor sm 1 , resulting in Fs(i)=mean {F(i/sm 1 ) . . . F(i*sm 1 )}. Typically, the smoothing factor sm 1  may be equal to approximately 1.05-1.2. 
     Proceeding to step  312 , the log-magnitude spectrum As of the inverse system (which is the  EQ-filter ) is determined by As=−20*log 10(Fs). Next, in step  314 , the peaks of As are smoothed with smoothing factor sm 2 , which generally is larger than sm 1  (e.g., sm 2  is typically equal to 1.2-1.6), resulting in Asp (see plot  610 ,  FIG. 6 ). This “smoothing of peaks” is illustrated in  FIG. 6 . It ensures that the frequency-dependent filter gain does not exceed values of the average response, while fine details are preserved below that average response. 
     In step  316 , the EQ filter is scaled such that its gain is 0 dB at its operating frequency fg (for example, fg=1 kHz; see point  708 ,  FIG. 7 ). Below fg, the filter response is replaced by the constant 0 dB. Next, in step  318 , the filter response is limited to its value at a frequency fgu (typically 10-15 kHz), ensuring that there is no excessive gain to, for example, equalize a tweeter with a natural roll-off in case the microphone is not positioned exactly at the main axis. In step  320 , filter values above a defined gain value are set to that defined gain value, in effect, further limiting the maximum gain of the response and clipping the peaks of the response. 
     In step  322 , an EQ filter impulse response is determined from the scaled, limited, and clipped EQ filter spectrum generated in steps  316 ,  318 , and  320 , assuming minimum-phase. It is appreciated by those skilled in the art that the EQ filter impulse response generated in step  322  may be generated using several techniques, including the Hilbert transform. In step  324 , a rectangular time window is multiplied with the resulting impulse response according to the desired filter length of, e.g., 64 samples (see point  808 ,  FIG. 8 ). 
     In optional step  326 , an equivalent IIR filter impulse response of low order (typically 2-8) may be generated using a known method, such as the iterative Steiglitz-McBride method that approximates the original FIR impulse response in the time domain by the impulse response of an IIR system (see plot  908 ,  FIG. 9 ). (For example, the macro “stmbc,” which is part of the MATLAB package, may be used). The process  300  then ends in step  330 . 
     A graphical representation  400  of an example of a plot  406  of amplitude  402  (in dBs) versus time  404  (in samples) of a room impulse response (“RIR”) is shown in  FIG. 4 . The RIR impulse response, which is captured in step  306 ,  FIG. 3 , is multiplied by a time window  408  for samples above a defined value t 1  such that these samples are set to zero (see step  308 ,  FIG. 3 ). Typically, t 1  may be equal to 2-4 ms or 100-200 samples at a sample rate of 48 kHz (in  FIG. 4 , t 1  is equal to approximately 110 samples). This “windowing” suppresses unwanted reflections from boundaries that are not considered near-field. 
     Tuning to  FIG. 5 , a graphical representation  500  of an example of plots  506  and  508  of magnitude  502  (in dBs) versus frequency  504  (in Hz) for the spectrum of the RIR  406  in  FIG. 4  is shown, Plot  506  is the magnitude spectrum F(i), with i=1, . . . , N/2, generated using an N-point FFT, where N=8192. Plot  508  is the magnitude spectrum of plot  506  smoothed with a smoothing factor sm 1 , resulting in Fs(i)=mean {F(i/sm 1 ) . . . F(i*sm 1 ))}. Typically, the smoothing factor sm 1  may be equal to approximately 1.05-1.2. 
       FIG. 6  shows a graphical representation  600  of an example of plots  606 ,  608 , and  610  of magnitude  602  (in dBs) versus frequency  604  (in Hz) of a frequency response of an ideal EQ filter, a smoothed version of that frequency response, and the smoothed version with those parts of the frequency response of an ideal EQ filter that lie above the smoothed version of the frequency spectrum cut from the plot, respectively. Plot  606  is a plot of the log-magnitude spectrum of the inverse system (which is the  EQ-filter ) As=−20*log 10(Fs), Plot  608  is a plot of the As of Plot  606  that has been smoothed with smoothing factor sm 2 , which generally is larger than sm 1  (e.g., sm 2  is typically equal to 1.2-1.6). Cutting that portion of plot  606  that lies above plot  608  results in plot  610 , denoted as Asp. This “smoothing of peaks” ensures that the frequency-dependent filter gain does not exceed values of the average response, while fine details are preserved below that average response. 
     In  FIG. 7 , a graphical representation  700  of an example of a plot  706  of magnitude  702  (in dBs) versus frequency  704  (in Hz) of an EQ filter frequency response is shown. The EQ filter generating the response illustrated by plot  706  has been scaled such that its gain is 0 dB at its operating frequency fg (at point  708 , where fg is equal to 1 kHz). Below fg, the filter response is replaced by the constant 0 dB. Above a frequency fgu (at point  710 , where fgu is typically equal to approximately 10-15 kHz), the filter response is limited to its value at fgu, ensuring that there is no excessive gain to, for example, equalize a tweeter with a natural roll-off in case the microphone is not positioned exactly at the main axis. The maximum gain may be further limited by setting filter values above a defined gain value to that value (i.e., clipping). 
       FIG. 8  shows a graphical representation  800  of an example of a plot  806  of magnitude  802  (in dBs) versus time  804  (in samples) of an EQ filter impulse response that is generated from the scaled, limited, and clipped EQ filter frequency response shown by plot  706  of  FIG. 7 , assuming minimum-phase. It is appreciated by those skilled in the art that the EQ filter impulse response depicted by plot  806  may be generated using several techniques, including the Hilbert transform. The result of the transform may be time limited to the desired filter length by applying a rectangular window, which in  FIG. 8  is the length of 64, denoted by point  808 . 
     In  FIG. 9 , a graphical representation  900  of an example of plots  706 ,  FIG. 7 , and  908  of magnitude  902  (in dBs) versus frequency  904  (in Hz) is shown. Plot  706 ,  FIG. 7 , depicts the EQ filter frequency response that has been scaled to frequency fg, limited above a frequency fgu, and clipped at a maximum gain. Alternatively, an equivalent IIR filter impulse response of low order (typically 2-8) may be generated using a known method, such as the iterative Steiglitz-McBride method that approximates the original FIR impulse response in the time domain by the impulse response of an IIR system. (For example, the macro “stmbc,” which is part of the MATLAB package, may be used). An example of an equivalent IIR filter frequency response is shown by plot  908 . 
       FIG. 10  shows a graphical representation  1000  of all example of plots  1006 ,  1008 , and  1010  of magnitude  602  (in dBs) versus frequency  604  (in Hz) that illustrate the effect of a near-field EQ on a loudspeaker in a small room. Plot  1008  is a plot of the log-magnitude frequency response of the loudspeaker obtained in the near field. Plot  1006  is a plot of the log-magnitude frequency response of the EQ filter frequency response generated as shown in  FIG. 7  that is applied to the frequency response depicted by plot  1008 , with the result being a frequency response depicted by plot  1010 . From plot  1010 , it is apparent that the measured frequency response is corrected within the band of interest, i.e., above 1 kHz, where the frequency response is flatter, while less audible, strongly position-dependent fine details or interference notches are left unaltered. 
     Turning to  FIG. 11 , a flow chart illustrating another example of a room equalization method is shown, where the method is designed for a far-field, low-frequency EQ. The process  1100  may be a subset of the process  300  shown in  FIG. 3 , with the following exceptions. The process starts in step  1102 . Next, in step  1104 , the captured frequency response may be multiplied by a “target function” in order to obtain the ideal EQ filter response. Typically this may be a bandpass filter with a passband of 20-80 Hz (e.g., a 4 th  order Butterworth characteristic). More complex target functions may be utilized, particularly in automotive applications. 
     Step  306 ,  FIG. 3 , where the sequence (impulse response) is multiplied by a rectangular or other time window, is not included in process  1100  because correction of the complete room impulse response (“RIR”) is possible and also desirable at low frequencies. Smoothing of peaks, however, applies similarly as in the near-field, HF-EQ process and this takes place in step  1106 . In step  1108 , the resulting FIR filter may be scaled to an average loudness level, and directly implemented at a lower sample rate (typically 375 Hz, which corresponds to a decimation ratio of 64 at a frequency of 48 kHz) using decimation and interpolation filters, as shown by decimation filter  208  and interpolation filter  214 ,  FIG. 2 .  FIG. 12  shows a graphical representation  1200  of an example of a plot  1206  of magnitude  1202  (in dBs) versus frequency  1204  (in Hz) of a typical Bass EQ filter frequency response. 
     A mid-frequency (“MF”) EQ operates in the frequency range of, for example, 100 Hz-1 kHz. Room impulse responses may be captured by a microphone that is located at the desired listening location. In  FIG. 13 , a flow chart illustrating an example of another room equalization method is shown, where this method is designed for a far-field, mid-frequency EQ. The process  1300  starts in step  1302  and in step  1304 , a room impulse response (“RIR”) may be determined at a listening location, Steps  1304 ,  1306 ,  1308 ,  1310 , and  1312  are similar to the corresponding steps of  FIG. 3 ; however, the parameters are chosen differently. 
     In step  1306 , the sequence (i.e., the impulse response) is multiplied by a rectangular or other time window, thus setting samples above a defined value t 2  to zero. This time windowing now has a larger impact, because major parts of the measured impulse response are cut off (see  FIG. 14 ). As a result, only the source (i.e., the loudspeaker) and its direct adjacent surfaces are included, thus focusing on source, not room, correction. This leads to increased robustness with respect to microphone placement, and thus optimum correction over the entire listening area, not just a single point. 
     Next, in step  1308 , the magnitude spectrum F(i), with i=1, . . . , N/2, is generated using an N-point FFT, where, for example, N=8192, In step  1310 , the magnitude spectrum determined in step  1308  is smoothed with a smoothing factor sm 3 , resulting in Fs(i)=mean {F(i/sm 3 ) . . . F(i*sm 3 )}. Typically, the smoothing factor sm 3  used in the far-field, MF EQ, is much larger than the smoothing factor used in the HF EQ (typically, sm 3 =1.4-2.0), so that only the overall trend will be considered, not fine details. Also, the MF EQ does not apply separate smoothing of peaks and dips, as shown in step  314 ,  FIG. 3 . 
     In step  1312 , the logarithmic magnitude spectrum is determined and normalized to a prescribed maximum gain. In step  1314 , the EQ filter frequency response may be determined by negating the log-magnitude spectrum of step  1312  and adding a high-pass target function (typically, 80-200 Hz), and in step  1316 , the EQ filter frequency response is set to zero dB above its operating range. The process  1300  then ends in step  1320 . 
       FIG. 14  shows a graphical representation  1400  of an example of a plot  1406  of amplitude  1402  (in dBs) versus time  1404  (in samples) of the RIR generated in step  1304  of  FIG. 1304 . The RIR is multiplied by a time window  1408  for samples above a defined value t 2  such that these samples are set to zero. Typically, t 2  may be equal to 16 . . . 32 millisecs (“ms”) or 100-200 samples at a sample rate of 8 kHz (in  FIG. 4 , t 2  is equal to approximately 130 samples). As noted above when discussing  FIG. 13 , this “windowing” cuts off major parts of the RIR. 
     Turning to  FIG. 15 , a graphical representation  1500  of an example of spectral plots  1506  and  1508  of amplitude  1502  (in dBs) versus frequency  504  (in Hz) for the RIR  1406  of  FIG. 14  is shown. Plot  1506  is the amplitude spectrum F(i), with i=1, . . . , N/2, computed using an N-point FFT, where N=8192. Plot  1508  is the amplitude spectrum of plot  1506  smoothed with a smoothing factor sm 3 , resulting in Fs(i)=mean {F(i/sm 3 ) . . . F(i*sm 3 )}. As noted above when discussing  FIG. 13 , the larger smoothing coefficient sm 3  generates a plot  1508  that takes into account only the overall trend, not fine details. 
       FIG. 16  shows a graphical representation  1600  of an example of plots  1606  and  1608  of amplitude  1602  (in dBs) versus frequency  1604  (in Hz), where plot  1606  is a plot of the smoothed log-magnitude spectrum of the measured response and plot  1608  is a plot of the EQ filter impulse response obtained using a target high pass function. Turning to  FIG. 17 , a graphical representation  1700  of an example of plots  1706  and  1708  of amplitude  1702  (in dBs) versus frequency  1704  (in Hz) is shown, Plots  1706  and  1708  are the frequency responses of low- and mid-frequency EQ filters, respectively.  FIG. 18  shows a graphical representation  1800  of all example of plots  1806 ,  1808 , and  1810  of amplitude  1802  (in dBs) versus frequency  1804  (in Hz), where plot  1806  is a plot of the inverse system, plot  1808  is a plot of the log-magnitude spectrum that has been smoothed with a smoothing factor, and plot  1810  is the sum of  1806  and  1808 , shifted downwards for better visibility, showing the result after EQ. 
     In automotive applications, it is no longer necessary, or desirable, to distinguish between near- and far-field responses. More complex target functions, such as that shown in  FIG. 19 , may be utilized in order to predict average responses at the automobile seats that include direct and reflected sound fields.  FIG. 19  shows a graphical representation  1900  of an example of a plot  1906  of magnitude  702  (in dBs) versus frequency  704  (in Hz) of an EQ filter frequency response generated using another example of a target function. The equalization may be performed as described, using different smoothing factors in different frequency bands. Input data may be obtained by spatial averaging between different locations around the listener&#39;s head, and between the seats. Also, weighting factors may be applied to emphasize equalization quality at a particular seat, while compromising performance at other seats. 
     In order to save processing costs and minimize complexity, equalization may be performed throughout the whole frequency band at once. However, the resulting filter impulse response may be split into several bands, as shown in  FIG. 20 . In  FIG. 20 , a graphical representation  2000  of an example of plots  2006 ,  2008 , and  2010  of magnitude  2002  (in dBs) versus frequency  2004  (in Hz) of EQ filter impulse responses is shown, Plots  2006 ,  2008 , and  2010  depict the frequency spectra for the low, medium, and high frequency bands, respectively. It is then easier to approximate the individual, band-limited responses separately by low-order IIR filters using, for example, the Steiglitz-McBride method as described earlier. The resulting individual EQ-sections may then be connected in series. 
     Persons skilled in the art will understand and appreciate, that one or more processes, sub-processes, or process steps described in connection with  FIGS. 3 ,  11 , and  13  may be performed by hardware and/or software. Additionally, the WBES described above may be implemented completely in software that would be executed within a processor or plurality of processors 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” (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. 
     While the foregoing descriptions refer to the use of a wide band equalization system in smaller enclosed spaces, such as a home theater or automobile, the subject matter is not limited to such use. Any electronic system or component that measures and processes signals produced in an audio or sound system that could benefit from the functionality provided by the components described above may be implemented as the elements of the invention. 
     Moreover, it will be understood that the foregoing description of numerous implementations has been presented for purposes of illustration and description. It is not exhaustive and does not limit the claimed inventions to the precise forms disclosed. Modifications and variations are possible in light of the above description or may be acquired from practicing the invention. The claims and their equivalents define the scope of the invention.