Abstract:
A computer-implemented method comprising: determining one or more features of a subject signal; revising one or more control signals on the basis of the one or more features; modifying a level of the subject signals based on the control signals. At least one of the features is determined by: comparing a given one of the subject signals against a boundary signal to produce a corresponding given boundary comparison signal; and summarizing the behavior of the given boundary comparison signal over a time interval.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a divisional of U.S. patent application Ser. No. 13/743,487, filed Jan. 17, 2013, which claims the benefit of U.S. Provisional Patent Application No. 61/591,410, filed Jan. 27, 2012. The entire disclosure and contents of the above applications are hereby incorporated by reference herein. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to devices having Automatic Gain Control (AGC) systems, e.g., hearing prostheses, and, more particularly, to processing an input sound signal in order to adjust signal levels. 
     RELATED ART 
     Automatic Gain Control (AGC) systems are commonly used in devices, e.g., audio processing devices, to cope with a large range in sound levels. AGC systems are applicable to hearing prostheses, audio headsets, telecommunications systems, and the like. AGC systems are particularly beneficial in hearing prostheses because hearing prostheses generally have a restricted dynamic range. In some systems, the audio signal is split into multiple frequency bands by a filter bank or a transform (e.g., a Fast Fourier Transform). The gain of each band can then be controlled separately. This is referred to as a multi-band type of AGC. 
     SUMMARY 
     In one aspect of the present invention, there is provided a computer-implemented method comprising: determining one or more features of a subject signal; revising one or more control signals on the basis of the one or more features; modifying a level of the subject signal based on the control signals. At least one of the features is determined by: comparing the subject signal against a boundary signal to produce a boundary comparison signal; and summarizing the behavior of the boundary comparison signal over a time interval. 
     In another aspect, there is provided a device comprising: one or more feature extractors, each being configured to extract a respective feature from a subject signal; a controller configured to refine one or more control signals based on the one or more features; and a compressor configured to adaptively perform signal compression on a subject signal according to the one or more control signals to produce a compressed signal. 
     In yet another aspect, there is provided a method, comprising: determining one or more features of a subject signal; and revising a scaling value for the subject signal on the basis of a comparison between the one or more features and one or more decision thresholds. At least one of the features is determined by: comparing the scaled analysis signal to a predetermined boundary value, to produce a boundary comparison signal; and summarizing the behavior of the boundary comparison signal over a time interval. 
     In yet another aspect, there is provided a device comprising: a set of feature-based regulators configured to produce a set of control signals from a set of subject signals, respectively; and compressors configured to perform signal compression by adaptively applying loudness growth functions (LGFs) to the set of subject signals according to the set of control signals, respectively. Such a set of control signals includes: a compressor-common control signal applicable to all of the compressors; and a set of compressor-specific control signals, respectively. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Illustrative embodiments of the present invention are described herein with reference to the accompanying drawings, in which: 
         FIG. 1A  illustrates a multi-band AGC system for use in a cochlear implant, according to an embodiment of the present invention; 
         FIGS. 1B and 1C  illustrate adaptive LGF systems for use in a cochlear implant, according to embodiments of the present invention; 
         FIG. 2  illustrates a multi-band AGC system for use in an application that provides an audio output, according to another embodiment of the present invention; 
         FIGS. 3A, 3B, and 3C  illustrate feature-based regulators according to embodiments of the present invention; 
         FIG. 3D  illustrates a feature combiner according to an embodiment of the present invention; 
         FIG. 3E  illustrates a feature-based gain module according to an embodiment of the present invention; 
         FIG. 4  illustrates a feature decision module according to an embodiment of the present invention; 
         FIGS. 5A, 5B, 6, 7, 8 and 9  illustrate feature extractors according to embodiments of the present invention; 
         FIG. 10A  illustrates a gain rule according to an embodiment of the present invention; 
         FIGS. 10B, 10C, and 10D  illustrate decision logic according to embodiments of the present invention; 
         FIG. 11  illustrates a short segment of one band of a speech signal; 
         FIG. 12A  illustrates an input-output function of a Loudness Growth Function (LGF) and  FIG. 12B  is a block diagram of an LGF according to an embodiment of the present invention; 
         FIGS. 13 through 15  illustrate feature-based gain modules according to embodiments of the present invention; 
         FIG. 16  is a schematic diagram of a sound processor module configured to be incorporated into a multi-band feature-based level control system (which can be implemented in a cochlear implant system) according to an embodiment of the present invention; 
         FIGS. 17, 18, and 19  illustrate flow diagrams that describe multi-band processing procedures implemented by, e.g., a sound processor module, according to embodiments of the present invention; 
         FIG. 20  illustrates a feature-based saturation level regulator, according to an embodiment of the present invention; 
         FIG. 21  illustrates a feature-based base level regulator, according to an embodiment of the present invention; and 
         FIG. 22  illustrates a feature-based multiband base level and saturation level regulator, according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Embodiments of the present invention may be implemented in sound-processing technologies that benefit from gain control systems, e.g., hearing prostheses, telecommunications systems, and the like. Such systems typically perform a frequency analysis (e.g., via a filter bank or a Fast Fourier Transform unit) that splits an audio signal into analysis signals distributed across multiple frequency bands and then separately adjust the gain of each band. 
     The processing of an audio signal for a device having an AGC system, e.g., a hearing prosthesis such as a cochlear implant, according to an embodiment of the present invention is shown in  FIG. 1A . This example shows a four-band AGC system for simplicity of illustration, but a higher number of bands (for example 22) is more typical. An audio signal  100  (e.g., derived from a source such as a microphone, telecoil, etc., typically with a pre-amplifier or front-end AGC, not shown in  FIG. 1A ) is split into four frequency bands by four band-pass filters  101  through  104  (which can comprise a frequency analysis unit, e.g., a Fast Fourier Transform unit, not shown). Each band-pass filter (BPF) passes a different band of frequencies. Band-pass filters  101  through  104  produce band signals  111  through  114  that are applied to envelope detectors  121  through  124  to produce envelopes  131  through  134 . Some implementations use a quadrature pair of band-pass filters in each band, followed by quadrature envelope detection to produce envelopes  131  through  134 . Envelopes  131  through  134  are applied to variable-gain amplifiers  141  through  144  to produce scaled envelopes  161  through  164 . This operation is equivalent to multiplying envelopes  131  through  134  by band gains  151  through  154 . Scaled envelopes  161  through  164  are applied to instances  171  through  174  of a type of feature-based regulator (FBR) that determines gain (namely, an FBR-G) (discussed in more detail below) to determine gains  151  through  154 . Scaled envelopes  161  through  164  are applied to instantaneous non-linear compression blocks  181  through  184 , also known as loudness growth functions (LGFs), to produce magnitude signals  191  through  194 . Magnitude signals are further processed (not shown in  FIG. 1A ) to determine the stimulation on a corresponding electrode, for example as described in Loizou (1998), Mimicking the human ear, IEEE Signal Processing Magazine, 15: 101-130. 
     When fitting a cochlear implant system to a recipient, the appropriate stimulation levels for each electrode must be determined. Typically, electrical stimulation is delivered in the form of biphasic pulses. The loudness of a pulse train on an electrode depends on the phase width (typically 10 to 50 microseconds), the gap between phases (typically 0 to 20 microseconds), the current (typically 10 to 1000 microamperes), and the pulse rate (typically 250 to 4000 pulses per second). Typically, the timing parameters (phase width, phase gap and pulse rate) are held constant, and the loudness is varied by varying the current. The lowest current that is delivered on an electrode is here denoted as the lower current level. The highest current that is delivered on an electrode is here denoted as the upper current level. The lower and upper current levels vary between recipients, and also vary between electrodes in a single recipient. 
     An example LGF input-output function is shown in  FIG. 12A . As seen in  FIG. 12A , scaled envelope amplitudes equal to a specified saturation level are mapped to magnitude value of 1.0, which will result in stimulation at the upper current level. The saturation level is often taken as a reference point, e.g., labeled as 0 dB in  FIG. 12A . Scaled envelope amplitudes equal to a specified base level are mapped to magnitude value 0.0, which will result in stimulation at the lower current level. The dynamic range is defined as the ratio of the saturation level to the base level. Typical dynamic range values are from 30 to 50 dB;  FIG. 12A  shows a dynamic range of 40 dB. The LGF prevents excessive loudness by limiting the current on each electrode to the corresponding upper current level. However, scaled envelope amplitudes greater than the saturation level are clipped to magnitude value 1.0, and hence the upper current level. This clipping is a form of distortion, and one goal of the present invention is to reduce this clipping. 
       FIG. 12B  shows an LGF module  1200  according to an embodiment of the present invention. Envelope  1201  is applied to level modifier  1202  to produce signal  1203 . The operation of level modifier  1202  can be expressed in the MATLAB® brand of high-level programming language made available by MathWorks®, Inc., e.g., as follows: 
                                                     if v &gt;= sat_level                r = 1;               elseif v &lt;= base_level                r = 0;               else                r = (v − base_level) / (sat_level − base_level);               end                        
where v is the envelope sample, base_level is the LGF base level  1206 , sat_level is the LGF saturation level  1207 , and r is signal  1203 . In some embodiments of the present invention (e.g.,  FIG. 1A ), base level  1206  and saturation level  1207  are predetermined values. In other embodiments (e.g.,  FIG. 1B ), base level  1206  and saturation level  1207  are control signals that are generated by feature-based regulators (discussed in more detail below).
 
     In some embodiments of level modifier  1202 , the division operation is avoided by taking logarithms. In this case, the operation of level modifier  1202  can be expressed in the MATLAB® language, e.g., as follows:
 
log( r )=log( v −base_level)−log(sat_level−base_level);
 
     Signal  1203  is applied to logarithmic compression module  1204  to produce magnitude signal  1205 . The operation of module  1204  can be expressed in the MATLAB® language, e.g., as follows:
 
 m =log(1+alpha* r )/log(1+alpha);
 
where alpha is a coefficient determining the amount of compression, and m is magnitude  1205 . In some embodiments, module  1204  is implemented as a look-up table. In other embodiments, module  1204  is implemented as a piece-wise linear function using interpolation. In an embodiment in which level modifier  1202  takes logarithms, then module  1204  can compensate by incorporating an exponential operation.
 
     The processing of an audio signal for a cochlear implant according to another embodiment of the present invention is shown in  FIG. 1B . This example shows a four-band system for simplicity of illustration, but a higher number of bands (for example 22) is more typical. Audio signal  100  is processed to produce envelopes  131  through  134  in the same manner as in  FIG. 1A . Envelopes  131  through  134  are applied to variable LGF blocks  1081  through  1084 , to produce magnitude signals  1091  through  1094 . Envelopes  131  through  134  are also applied to instances of a type of feature-based regulator (FBR) that determines base level (namely, an FBR-B)  1041  through  1044  (discussed in more detail below), producing base level signals  1051  through  1054 . Envelopes  131  through  134  are also applied to instances of a type of feature-based regulator that determines saturation level (namely, an FBR-S)  1061  through  1064  (discussed in more detail below), producing saturation level signals  1071  through  1074 . In contrast to the LGF blocks  181  through  184  in  FIG. 1A , which have a fixed base level and saturation level, the variable LGF blocks  1081  through  1084  in  FIG. 1B  have base levels and saturation levels which are determined by the control signals  1051  through  1054  and  1071  through  1074  respectively. 
     The processing of an audio signal for a cochlear implant according to another embodiment of the present invention is shown in  FIG. 1C . This example shows a four-band system for simplicity of illustration, but a higher number of bands (for example 22) is more typical. Audio signal  100  is processed to produce envelopes  131  through  134  in the same manner as in  FIG. 1B . Envelopes  131  through  134  are applied to variable LGF blocks  1081  through  1084 , to produce magnitude signals  1091  through  1094 . Envelopes  131  through  134  are also applied to feature-based multi-band base level and saturation level regulator  1100  (discussed in more detail below), producing base level signals  1151  through  1154  and saturation level signals  1171  through  1174 . The difference between the regulator  1100  in  FIG. 1C  and the regulators  1041  through  1044  and  1061  through  1064  in  FIG. 1B  is that regulator  1100  allows dependency or co-ordination between the bands. 
     The processing of an audio signal for an application that provides an audio signal output, according to an embodiment of the present invention is shown in  FIG. 2 . Such applications include conventional hearing aids, bone-anchored hearing aids, and telecommunication systems. This example shows a four-band AGC system for simplicity of illustration, but a lower or higher number of bands may be used. An audio signal  200  is split into four frequency bands by four band-pass filters  201  through  204  (which can comprise a frequency analysis unit, e.g., a Fast Fourier Transform unit, not shown). Each band-pass filter (BPF) passes a different band of frequencies. Band-pass filters  201  through  204  produce band signals  211  through  214  that are applied to variable-gain amplifiers  221  through  224  to produce scaled band signals  241  through  244 . This operation is equivalent to multiplying band signals  211  through  214  by band gains  231  through  234 . Scaled band signals  241  through  244  are applied to envelope detectors  251  through  254  to produce scaled envelopes  261  through  264 . Scaled envelopes  261  through  264  are applied to gain-type feature based regulators (FBR-Gs)  271  through  274  (discussed in more detail below) to determine gains  231  through  234 . Scaled band signals  241  through  244  are applied to combine module  280  to produce an audio output signal  290 . Combine module  280  typically includes a summing operation. Alternatively, if audio signal  200  is divided into bands by an FFT, then combine module  280  incorporates an inverse FFT. 
     Embodiments of the present invention as shown in  FIG. 1A ,  FIG. 1B ,  FIG. 1C  and  FIG. 2  may be implemented as analog signal processing, digital signal processing (DSP), or a mixture of analog and digital. In a DSP implementation, the audio sample rate is defined as the rate at which audio signal  100  or  200  is sampled. Some telecommunications systems use an audio sample rate of 8000 Hz. A typical cochlear implant system uses an audio sample rate of 16000 Hz. To reduce the processing load, envelopes  131  through  134  of  FIG. 1A  or  FIG. 1B  or  FIG. 1C  can be down-sampled to a lower rate. For example, if the cochlear implant stimulates at 1000 pulses per second on each channel, the envelopes can be calculated at 1000 Hz. This rate will be termed the envelope sample rate. 
     The gain-type feature-based regulators (FBR-Gs)  171  through  174  in  FIG. 1A and 271 through 274  in  FIG. 2 , and the base-level-type feature-based regulators (FBR-Bs)  1041  through  1044  and the saturation-level-type feature-based regulators (FBR-Ss)  1061  through  1064  in  FIG. 1B  are all examples of feature-based regulators. 
     A feature-based regulator (FBR)  460  according to an embodiment of the present invention is shown in  FIG. 3A . The overall operation is that signal  300  is processed by FBR  460  to produce control signal  450 . In more detail, signal  300  is applied to a set of feature extractors (FX)  311  through  313 , to produce feature value (FV) signals  321  through  323 , which are applied to feature combiner  330 . Feature combiner  330  produces combined feature signal  410 , which is applied to parameter scaler  420 , producing signal  430 . Signal  430  is applied to parameter limiter  440  to produce control signal  450 . 
     In both  FIG. 1A  and  FIG. 2 , the control signal produced by each FBR is a gain, and the input is a scaled envelope (i.e., an envelope whose amplitude is affected by that gain). In  FIG. 1B  and  FIG. 1C , the inputs to the FBRs are envelopes, and the control signals produced are the base levels and saturation levels of the LGF modules. In each case, the control signal  450  affects the signal level in one band of the overall system, based on the information obtained by the feature extractors. 
     For the purposes of illustration, FBR  460  shown in  FIG. 3A  utilizes three features. However, an FBR may utilize any number of features. Suitable methods for the feature combiner  330  to combine the feature value signals include a weighted sum, the maximum value, the minimum value, or the median value. 
       FIG. 3B  shows an FBR  461  according to another embodiment of the present invention. FBR  461  utilizes a single feature extractor  311 , which produces feature value signal  321 . As there is only one feature, no feature combiner is needed, and feature value signal  321  is taken directly to parameter scaler  421 . 
       FIG. 3C  shows a FBR  462  according to another embodiment of the present invention. FBR  462  is similar to FBR  461 , except that feature value signal  321  is applied to feature decision module (FDM)  331  (described in more detail below), to produce feature decision signal  341 . Feature decision signal  341  is a Boolean signal (true or false), or equivalently a binary signal, taking the values 1 or 0. The feature decision signal  341  is applied to parameter scaler  422 . 
       FIG. 3D  shows a feature combiner  700  according to another embodiment of the present invention. Feature value signals  321  through  323  are applied to corresponding feature decision modules  331  through  333 . Feature decision signals (Boolean signals)  341  through  343  are applied to decision logic  1701 , which produces direction signal  1702 . Decision logic  1701  is typically implemented as a sequence of if-then-else logical statements. In one embodiment, direction  1702  is represented by a variable with three possible values: +1 (meaning increase the control signal), −1 (meaning decrease the control signal), or 0 (meaning no change in the control signal). 
     For example, if FBR  460  of  FIG. 3A  was implemented as an FBR-G, control signal  450  would be gain, and so parameter scaler  420  would be referred to as a gain scaler, and parameter limiter  440  could be referred to as a gain limiter. Similarly, for example, with reference to  FIG. 3D , decision logic  1701  can be referred to as gain logic. 
     A gain-type feature-based regulator (FBR-G)  370  according to an embodiment of the present invention is shown in  FIG. 3E . FBR-G  370  is an example of FBR-G modules  171 - 174  and  271 - 274 . The overall operation is that scaled envelope  300  is processed by FBR-G  370  to produce gain  360 . Note that in both  FIG. 1  and  FIG. 2 , the output of each FBR-G is a gain, and the input is a scaled envelope (i.e., an envelope whose amplitude is affected by that gain). In more detail, scaled envelope  300  is applied to a set of feature extractors (FX)  311  through  313 , to produce feature value signals  321  through  323 , which are applied to feature decision modules (FDMs)  331  through  343 , to produce feature decision signals  341  through  343 . Each feature decision signal is a Boolean signal (true or false), or equivalently a binary signal, taking the values 1 or 0. The feature decision signals are applied to gain rule  350  (described in more detail below) which produces gain  360 . 
     A characteristic of a FBR (e.g.,  460 ,  461 ,  462 , or  370 ) is the update rate, defined as the rate at which the control signal (gain, base level or saturation level, e.g.,  450 ,  451 ,  452 , or  360 ) is changed. This may be equal to the envelope sample rate (for example 1000 Hz), or may be lower. Typically, the feature extractors (e.g.,  311  through  313 ) produce their feature value signals  321  through  323  at the update rate. 
     The FDMs (e.g.,  331  through  333  in  FIG. 3C ,  FIG. 3D  and  FIG. 3E ), e.g., all have the same structure. An FDM  331  according to an embodiment of the present invention is shown in  FIG. 4A , where FDM  331  is an example of FDMs  331 - 334 . FDM  331  includes comparator  403  that produces feature decision signal  341  indicating whether feature value signal  321  exceeds feature decision threshold  402 . Feature decision threshold  402  is, e.g., a predetermined value stored in memory  401 . 
       FIG. 10A  shows a gain rule  350  according to an embodiment of the present invention. Feature decision signals (Boolean signals)  341  through  343  are applied to gain logic  701 , which produces gain direction  702 . Gain logic  701  is typically implemented as a sequence of if-then-else logical statements. In one embodiment, gain direction  702  is represented by a variable with three possible values: +1 (meaning increase gain), −1 (meaning decrease gain), or 0 (meaning no gain change). For the purposes of illustration, gain rule  350  shown in  FIG. 3E  and  FIG. 10A  utilizes three features. However, a gain rule may utilize any number of features. 
     Gain logic  701  in  FIG. 10A  is equivalent to decision logic  1701  in  FIG. 3D .  FIGS. 10B, 10C, and 10D  show examples  710 ,  720  and  730  of gain logic  701  or decision logic  1701  according to additional embodiments of the present invention, with different numbers of features, e.g., expressed in the MATLAB® language.  FIG. 10B  shows decision logic  710  that utilizes a single feature.  FIG. 10C  shows decision logic  720  that utilizes two features.  FIG. 10D  shows decision logic  720  that utilizes three features. 
     In some embodiments utilizing decision logic (e.g.,  1701  in  FIG. 3D ), the parameter scaler modifies the existing control signal in a proportional manner, according to the value of direction  1702 . The operation of parameter scaler can be expressed in the MATLAB® language, e.g., as: 
                                                     if direction &lt; 0                param = param * down_factor               elseif direction &gt; 0                param = param * up_factor               end                        
where param is a variable representing the control signal (e.g., one of gain, base level, saturation level, etc.), up_factor is a pre-determined factor for increasing the control signal, and down_factor is a pre-determined factor for decreasing the control signal.
 
     An example calculation of the up_factor and down_factor can be expressed in the MATLAB® language, e.g., as follows: 
     up_step_dB=up_slew_rate/update_rate; 
     down_step_dB=down_slew_rate/update_rate; 
     up_factor=10^(up_step_dB/20); 
     down_factor=10^(down_step_dB/20); 
     where update_rate is the rate at which the control signal is updated, up_slew_rate is the increase in dB per second and down_slew_rate is the decrease in dB per second. 
     Parameter limiter ( 440 ,  441 , or  442 ) constrains the control signal to lie between a maximum and a minimum value. The operation of the parameter limiter can be expressed in the MATLAB® language, e.g., as: 
                                                     if param &gt; param_max                param = param_max               elseif param &lt; param_min                param = param_min               end                        
where param_min is the minimum value of the control signal and param_max is the maximum value.
 
     As a specific example, in  FIG. 10A , gain scaler  703  modifies the existing gain in a proportional manner, according to the value of gain direction  702 . The operation of gain scaler  703  can be expressed in the MATLAB® language, e.g., as: 
                                                     if gain_direction &lt; 0                gain = gain * gain_down_factor               elseif gain_direction &gt; 0                gain = gain * gain_up_factor               end                        
where gain_up_factor is a pre-determined factor for increasing the gain, and gain_down_factor is a pre-determined factor for decreasing the gain.
 
     An example calculation of the gain_up_factor and gain_down_factor can be expressed in the MATLAB® language, e.g., as follows: 
     gain_up_step_dB=gain_up_slew_rate/update_rate; 
     gain_down_step_dB=gain_down_slew_rate/update_rate; 
     gain_up_factor=10^(gain_up_step_dB/20); 
     gain_down_factor=10^(gain_down_step_dB/20); 
     where update_rate is the rate at which the gains are updated, gain_up_slew_rate is the gain increase in dB per second (e.g., 3 dB per second) and gain_down_slew_rate is the gain decrease in dB per second (e.g., −10 dB per second). 
     Gain limiter  705  constrains the gain to lie between a maximum and a minimum value. The operation of gain limiter  705  can be expressed in the MATLAB® language, e.g., as: 
                                                     if gain &gt; gain_max                gain = gain_max               elseif gain &lt; gain_min                gain = gain_min               end                        
where gain_min is the minimum gain and gain_max is the maximum gain.
 
     A variety of features may be usefully employed in an embodiment of a feature-based regulator (FBR), for example, peak level, minimum level, noise floor, percentiles, modulation depth, specific loudness, and signal-to-noise ratio. Some additional feature extractors  500 ,  600 ,  620 ,  640  and  660  are disclosed below in the context of  FIGS. 5 through 9 , respectively. 
       FIG. 5A  shows a feature extractor  500  according to an embodiment of the present invention. Comparison module  530  compares envelope  300  to boundary signal  520 , and produces boundary comparison (BC) signal  540 . Several embodiments of comparison module  530  are described below. BC  540  can contain fluctuations on the same time scale as envelope  300 . BC signal  540  is applied to time interval observer  550 . Time interval observer  550  summarizes the behavior of BC  540  over the most recent time interval, and produces summarized boundary comparison (SC) type of feature value (FV), namely FV(SC), signal  560 , which fluctuates more slowly than envelope signal  300 . Examples of suitable time interval durations range from 50 milliseconds to several seconds. Several embodiments of time interval observer  550  are described below.  FIG. 5B  shows feature extractor  501 , according to another embodiment of the present invention, in which boundary signal  520  is a predetermined value stored in memory  510 . In other embodiments, boundary signal  520  is a time-varying signal (described in more detail below), rather than a predetermined value. 
       FIG. 6  shows feature extractor  600  according to an embodiment of the present invention, where feature extractor  600  is an example of feature extractor  500 . Comparator  531  is an example of comparison module  530  of  FIGS. 5A-5B . Comparator  531  produces BC signal  541  indicating whether scaled envelope  300  exceeds boundary value  520 . BC signal  541  is a Boolean signal (true or false), or equivalently a binary signal, taking the values 1 or 0. Time interval observer  601  is an example of time interval observer  550  in  FIGS. 5A-5B . Accumulator  602  operates according to a series of time intervals, where the segment length is defined as the number of samples of scaled envelope signal  300  in each time interval. Accumulator  602  is cleared at the beginning of each time interval. During each time interval, accumulator  602  sums BC  541  (a binary signal). In other words, it counts the number of samples for which scaled envelope  300  exceeds boundary signal  520 . The output of accumulator  602  is count  603 , which is an example representation of the proportion of scaled envelope samples that exceeds the boundary signal. At the end of each time interval, the value of count  603  lies in the range zero up to the segment length (i.e., up to the maximum number of samples that can be operated upon during the interval). The segment length determines the minimum number of bits for accumulator  602 . For example, if the segment length is 25, then accumulator  602  must have at least 5 bits, while if the segment length is 250, then accumulator  602  must have at least 8 bits. Although accumulator  602  operates at the envelope sample rate, only the final value of count  603  at the end of each time interval is provided as the output of time interval observer  601 , namely as FV(SC)  610 , which is an example of a summarized boundary comparison (SC) type of feature value (FV) signal. This reduction in processing rate is represented by down-sample block  604 . For example if the envelope sample rate is 1000 Hz, and the time intervals are 100 milliseconds in duration, then the segment length is 100, and the update rate is 10 Hz. 
     The operation of feature extractor  600  can be expressed in the MATLAB® language, e.g., as: 
                                                     count = 0;               for n = 1:segment_length                if v(n) &gt; boundary(n)                 count = count + 1;                end               end                        
where v is a segment of the envelope signal  300  containing segment_length samples, n is the sample index within the segment, boundary is a segment of boundary signal  520 , and count is the feature value signal  610 .
 
     In an alternative embodiment of feature extractor  600 , the output of accumulator  602  is normalized (i.e., divided by the number of samples in the time interval) to obtain a feature value signal  610  that represents the proportion of envelope samples that exceed the boundary signal. This step can be expressed in the MATLAB® language, e.g., as: 
     proportion_exceeds_boundary=count/segment_length; 
     Such a proportion necessarily lies in the range 0 to 1. This normalization step is not strictly necessary, and is typically omitted from a fixed-point DSP implementation. And yet the normalization step has an advantage of providing a feature signal that has a more readily understood meaning, and it allows the corresponding decision threshold to also be expressed as a proportion, independent of the time interval duration or update rate. 
     It can be desirable to increase the time duration over which the system observes the envelope levels when making decisions on the appropriate control signal. Feature extractor  600  has the property that increasing the time interval duration leads to a lower update rate and larger control signal steps, which may become objectionable to the listener. This issue can be alleviated by utilizing overlapping time intervals. For example, the time interval duration can be increased to one second (i.e., segment_length=1000 samples), while keeping a 10 Hz update rate, so that each time interval has a 90% overlap with the previous time interval. To increase computational efficiency, for example, each time interval can be divided into 10 non-overlapping sub-segments, each having 100 samples. The number of envelope samples exceeding the boundary signal is counted for each sub-segment, and the resulting counts are stored in a first-in, first-out (FIFO) buffer, of length 10. The final count for the time interval ending at each sub-segment is obtained as the sum of the last 10 sub-segment counts. This operation is equivalent to a running sum operation on the sequence of sub-segment counts, i.e., a finite-duration impulse response (FIR) filter with all coefficients equal to one. An embodiment that operates in this manner is shown in  FIG. 7 . 
       FIG. 7  illustrates feature extractor  620  according to an embodiment of the present invention, where feature extractor  620  is another example of feature extractor  500 . Feature extractor  620  is similar to feature extractor  600 , the difference being in time interval observer  621 , which is another example of time interval observer  550  from  FIGS. 5A-5B . Time interval observer  621  comprises accumulator  622  and down-sample block  624 , which operate as in feature extractor  600 , and further includes a low-pass filter  626  applied to count signal  625 . Low-pass filter  626  operates at the update rate, and extends the time interval over which BC  541  is observed. Normalization, as previously described, can optionally be applied to count signal  625  or to filter output FV(SC)  630 , which is an example of a summarized boundary comparison (SC) type of feature value (FV) signal. 
     In one example embodiment, low-pass filter  626  is an FIR filter with all coefficients equal to one, as previously described. Alternatively, the FIR coefficients can be designed to give a specified time response, for example to give less weight to past segments. Alternatively an order-statistics filter can be applied to the sequence of counts, such as for median filtering, or for example taking the maximum count over the last 10 segments. 
     Alternatively filter  626  can be an infinite-duration impulse response (IIR) filter. This has an advantage that the signals can be observed over much longer time intervals whilst minimizing the memory requirements. For example, a first-order smoothing filter can be expressed in the MATLAB® language, e.g., as:
 
smoothed_count=(1−alpha)*count+alpha*smoothed_count;
 
where smoothed_count is the filtered count signal  630 , and alpha is a decay factor in the range 0 to 1. This requires one additional word of storage per band and per target.
 
     As a special case, if alpha is set equal to 0.5, the expression becomes:
 
smoothed_count=0.5*(count+smoothed_count);
 
In this special case, no additional storage is required: at the start of each segment, instead of initializing the counts to zero, the previous values of the counts are halved, and then the counts are incremented from these non-zero starting points; thus, one memory location holds the sum of the current count and its history. Effectively the accumulator  622  and the filter  626  have been combined. This arrangement can be further generalized as shown in  FIG. 8 .
 
       FIG. 8  illustrates feature extractor  640  according to an embodiment of the present invention, where feature extractor  640  is another example of feature extractor  500 . Time interval observer  641  is an example of time interval observer  550  in  FIGS. 5A-5B , and comprises low-pass filter  642  and down-sample block  644 . Filter  642  acts directly on the BC  541 . Because BC  541  is a binary signal, filter  642  can be implemented efficiently, e.g., in hardware, with many multiplications replaced by conditional addition. Filter output signal  643  represents the average value of BC  541 , in other words, it represents the proportion of time that envelope signal  300  exceeds the boundary signal  520 . Filter  642  can be designed to give a specified time response; for example, to give more weight to more recent samples. In one example embodiment, filter output signal  643  is calculated at the envelope sample rate, and is down-sampled by down-sample block  644  to give FV(SC)  650  at the update rate, where FV(SC) is which is an example of a summarized boundary comparison (SC) type of feature value (FV) signal. The skilled artisan would understand how to merge down-sample block  644  into filter  642 , e.g., for the purposes of enhancing efficiency. Alternatively, down-sample block  644  can be omitted if it is desired to make the update rate equal to the envelope sample rate. 
       FIG. 9  illustrates feature extractor  660  according to an embodiment of the present invention, where feature extractor  660  is another example of feature extractor  500 . Block  661  is an example of comparison module  530  of  FIGS. 5A-5B . In contrast to feature extractors  600 ,  620 , and  640 , which use comparator  531  to determine whether the envelope signal  300  exceeds boundary signal  520 , block  661  calculates the amount by which envelope signal  300  exceeds boundary signal  520 . Subtractor  662  subtracts boundary signal  520  from envelope signal  300  to produce difference signal  663 , which is rectified by half-wave rectifier (HWR)  664 . The operation of block  661  can be expressed in the MATLAB® language, e.g., as:
 
 d=v −boundary;
 
excess=max( d, 0);
 
where v is envelope signal  300 , boundary is boundary signal  520 , d is difference signal  663 , and excess is a boundary comparison (BC) signal  665 . BC  665  is then applied to time interval observer  666 , which is an example of time interval observer  550  in  FIGS. 5A-5B , which comprises low-pass filter  667  and down-sample block  669 , and which produces a signal FV(SC)  670 , which is an example of a summarized boundary comparison (SC) type of feature value (FV) signal. FV(SC)  670  characterizes the average amount by which the envelope  300  exceeds boundary signal  520  over the most recent time interval.
 
       FIG. 11  illustrates a short segment of one band of a speech signal and can serve to illustrate differences between feature extractor  660  and feature extractors  600 ,  620 , and  640 . This example is a gain-type feature-based regulator (FBR-G), in which the control signal  670  is gain (as in  FIG. 1A ), and the boundary signal  520  is a predetermined boundary level (as in  FIG. 5B ). The shaded region indicates the amount by which the scaled envelope signal  300  exceeds the boundary level  520 , which is 0 dB in this example. There are two excursions of the scaled envelope signal above the boundary level: the first excursion  1001  starts at a time of about 0.15 seconds and the second excursion  1002  starts at a time of about 0.45 seconds. If feature extractor  600 ,  620 , or  640  is used, then the first excursion  1001  produces a smaller gain decrement than the second excursion  1002 , because the first excursion has a shorter duration. However, if feature extractor  660  is used, filter  667  acts as an integrator, estimating the area of the shaded regions, and so the first excursion  1001  produces a greater gain decrement than the second excursion  1002 , because the first excursion has a larger area. Thus, feature extractor  660  is more responsive to large-amplitude, short-duration transients. 
     Some specific examples of gain-type feature-based regulators (FBR-Gs) are described below. Each, e.g., has the same overall structure as FBR-G  370  in  FIG. 3E , but with different numbers and types of internal components. 
     An FBR-G  810  according to an embodiment of the present invention is shown in  FIG. 13 . Like FBR-G  370 , FBR-G  810  is an example of FBR-Gs  171 - 174  and  271 - 274 . FBR-G  810  utilizes a single feature extractor, namely peak detector  811 . Peak detector  811  produces feature value signal, designated peak level  812 . An embodiment of peak detector  811  can be expressed in the MATLAB® language, e.g., as:
 
 x ( n )=max( v ( n ), x ( n− 1)*peak_decay_weight);
 
where v(n) is a sample of scaled envelope signal  300 , x(n) is a sample of peak level  811 , x(n−1) is the previous sample of peak level  811 , and peak_decay_weight is determined from the envelope sample rate and a predetermined peak decay time, which can be expressed in the MATLAB® language, e.g., as follows:
 
 T= 1/envelope_sample_rate;
 
peak_decay_weight=exp(− T /peak_decay_time);
 
     Feature decision module  813  compares peak level  812  to peak threshold  815 , and produces feature decision signal  817 , denoted peak_too_high. Feature decision signal peak_too_high  817  is applied to gain rule  818 , which utilizes, e.g., decision logic  710  from  FIG. 10B . The overall operation of FBR-G  810  is that if the peak level is too high, the gain is reduced, otherwise the gain is increased. In a cochlear implant system, an example value for the peak threshold is a point mid-way between the base level and the saturation level, for example 20 dB below the saturation level in  FIG. 12A . Example values for other parameters are: peak decay time of 0.5 seconds, gain slew up rate of 6 dB per second, and gain slew down rate of 6 dB per second. Thus, FBR-G  810  acts so that during speech activity, the peak levels spend about half of the time above the peak threshold, thus, aiding audibility. In the absence of speech activity, the gain increases up to the maximum gain, which is set so that background noise is not objectionable. 
       FIG. 14  illustrates an FBR-G  820  according to an embodiment of the present invention. Like FBR-Gs  370  and  810 , FBR-G  820  is an example of FBR-G  171 - 174  and  271 - 274 . FBR-G  820  can be used, e.g., with a cochlear implant, as in  FIG. 1A . FBR-G  820  utilizes two feature extractors,  821  and  831 . Feature extractor  821 , denoted clipping extractor, is an instance of feature extractor  620  from  FIG. 7 , and incorporates normalization as described previously. It is configured with boundary value  520  equal to the LGF saturation level shown in  FIG. 12A . Thus, BC  541  indicates whether scaled envelope  300  exceeds the LGF saturation level; in other words, it indicates whether the scaled envelope is clipped. Thus, feature value signal  822  represents the proportion of scaled envelope samples that were clipped during the last time interval, and is denoted clipping_proportion. Feature decision module  823  compares feature value signal clipping_proportion  822  to decision threshold  825 , to produce feature decision signal  827 . An example decision threshold is 0.1. In this case, feature decision signal  827  will be high (true) if more than 10% of scaled envelope samples were clipped in the last time interval. Feature decision signal  827  is denoted as clipping_too_often in a logic statement below. 
     Feature extractor  831  is a noise floor estimator. An example embodiment of feature extractor  831  is described in Martin R (2001), “Noise power spectral density estimation based on optimal smoothing and minimum statistics,” IEEE Transactions on Speech and Audio Processing, 9: 504-512. Feature value signal  832  is the estimated noise floor. Feature decision module  833  compares the noise floor to decision threshold  835 , to produce feature decision signal  837 . An example decision threshold is the LGF base level shown in  FIG. 12A . If feature value signal (estimated noise floor)  832  exceeds decision threshold  835 , then feature decision signal  837  is set to a state indicating that the noise floor exceeds the LGF base level. Feature decision signal  837  is denoted as noise_floor_too_high in a logic statement below. 
     Feature decision signals clipping_too_often  827  and noise_floor_too_high  837  are applied to gain rule  838 . The gain logic can be expressed in the MATLAB® language, e.g., as: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                   
                 if clipping_too_often 
               
               
                   
                   
                  gain_direction = −1; 
               
               
                   
                   
                 elseif noise_floor_too_high 
               
               
                   
                   
                  gain_direction = −1; 
               
               
                   
                   
                 else 
               
               
                   
                   
                  gain_direction = +1; 
               
               
                   
                   
                 end 
               
               
                   
                   
               
             
          
         
       
     
       FIG. 15  illustrates another FBR-G  840  according to an embodiment of the present invention. Like FBR-Gs  370 ,  810  and  820 , FBR-G  840  is an example of FBR-Gs  171 - 174  and  271 - 274 . FBR-G  840  is similar to FBR-G  820 , except for the addition of a third feature extractor  841 . Feature extractor  841  is denoted a mid-level extractor, and is an instance of feature extractor  620  from  FIG. 7 , and incorporates normalization as described previously. It is configured with boundary value  520  lying midway between the LGF saturation level and base level shown in  FIG. 12A . Thus, BC  541  indicates whether scaled envelope  300  is mapped into the upper section of the dynamic range; in other words, is relatively loud. Thus, feature value signal  842  represents the proportion of scaled envelope samples that were relatively loud during the last time interval. In a logic statement below, feature value signal  842  is denoted as loud_proportion. Feature decision module  843  compares feature value signal loud_proportion  842  to decision threshold  845 , to produce feature decision signal  847 . An example decision threshold is 0.3. If feature value signal loud_proportion  842  exceeds decision threshold  845 , then feature decision signal  827  is set to a state indicating that more than 30% of scaled envelope samples were relatively loud in the last time interval. Feature decision signal  827  is denoted as loud_enough in a logic statement below. 
     Feature decision signals clipping_too_often  827 , noise_floor_too_high  837 , and loud_enough  847  are applied to gain rule  848 . The gain logic can be expressed in the MATLAB® language, e.g., as: 
     
       
         
               
               
               
             
           
               
                   
                   
               
             
             
               
                   
                   
                 if clipping_too_often 
               
               
                   
                   
                  gain_direction = −1; 
               
               
                   
                   
                 elseif noise_floor_too_high 
               
               
                   
                   
                  gain_direction = −1; 
               
               
                   
                   
                 elseif not(loud_enough) 
               
               
                   
                   
                  gain_direction = +1; 
               
               
                   
                   
                 else 
               
               
                   
                   
                  gain_direction = 0; 
               
               
                   
                   
                 end 
               
               
                   
                   
               
             
          
         
       
     
       FIG. 20  illustrates a saturation-type feature-based regulator (FBR-S)  900  according to an embodiment of the present invention. It is an example of FBR  461  of  FIG. 3B , and FBR-Ss  1061  through  1064  in  FIG. 1B . The overall operation is that envelope signal  901  is processed by FBR-S  900  to produce saturation-level signal  908 . Feature extractor  910  is an example of feature extractor  500  of  FIG. 5A . Comparator  902  compares envelope  901  and saturation-level signal  908 . If the comparator output is high, it indicates that the envelope is higher than the existing LGF saturation level, i.e., clipping will occur. Time interval observer  903  can utilize e.g., embodiment  601  in  FIG. 6, 621  in  FIG. 7 , or  641  in  FIG. 8 . Feature value signal  904  represents the proportion of envelope samples that were clipped during the last time interval, and is denoted clipping_proportion. The operation of saturation level scaler  905  can be expressed in the MATLAB® language, e.g., as: 
                                                     if clipping_proportion &gt; 0                slew_rate = up_slew_rate * clipping_proportion;               else                slew_rate = down_slew_rate;               end               step_dB = slew_rate / update_rate;               factor = From_dB(step_dB);               sat_level = sat_level * factor;                        
where up_slew_rate is the maximum increase in dB per second and down_slew_rate is the decrease in dB per second, and sat_level is a variable representing the LGF saturation level.
 
     Saturation level limiter  907  constrains the saturation level between a minimum and maximum value. The minimum value of the saturation level in an adaptive LGF system is analogous to the maximum value of gain in an AGC system. It is also beneficial to set a minimum offset between the base level and the saturation level. 
     Thus, FBR-S  900  employs a feedback loop, where if clipping occurs, then the saturation level is increased, with a slew rate proportional to the proportion of clipping; and if no clipping occurs, then the saturation level is decreased, so that the LGF saturation level tends to follow the peak level of the envelope signal. 
       FIG. 21  illustrates a base-level-type feature-based regulator (FBR-B)  900  according to an embodiment of the present invention. It is an example of FBR  462  of  FIG. 3C , and FBR-Bs  1041  through  1044  in  FIG. 1B . The overall operation is that envelope signal  901  is processed by FBR-B  910  to produce base level signal  918 . Noise floor extractor  911  is an example of feature extractor  311  of  FIG. 3C , and is similar to noise floor extractor  831  in  FIG. 14  and  FIG. 15 . Feature value signal  912  is the estimated noise floor. Comparator  913  compares the noise floor  912  to base level  918 , to produce feature decision signal  914 , which is denoted as noise_floor_too_high. The operation of base level scaler  905  can be expressed in the MATLAB® language, e.g., as: 
                                                     if noise_floor_too_high                base_level = base_level * up_factor               else                base_level = base_level * down_factor               end                        
where up_factor and down_factor are predetermined values, as described previously. Base level limiter  917  constrains the base level between a minimum and maximum value.
 
     Thus, FBR-B  910  employs a feedback loop, where if the noise floor is higher than the base level, then the base level is increased; and otherwise the base level is decreased, so that the LGF base level tends to follow the noise floor of the envelope signal. 
       FIG. 22  illustrates a feature-based multi-band base level and saturation level regulator (FBR-R &amp; FBR-S)  920  according to an embodiment of the present invention. It is an example of FBR-R &amp; FBR-S  1100  of  FIG. 1C . In  FIG. 22 , thick lines represent a collection of signals, one for each band of the system. The overall operation is that the set of envelope signals  921  is processed by FBR-R &amp; FBR-S  920  to produce a set of base level signals  941  (where a signal set is denoted in  FIG. 22  via curly brackets ({ } enclosing the corresponding label) and one saturation level signal  931 . In this embodiment, all LGF blocks  2281 - 2284  have a shared saturation level signal  931 . In more detail, a set of envelopes  921  are applied to a set of feature-based base level regulators  940 , to produce the set of base level signals  941  that includes member signals  941 A- 941 D that are provided to LGF blocks  2281 - 2284 , respectively. There is, e.g., one feature-based base level regulator, and a corresponding base level signal, for each band. Each feature-based base level regulator can be implemented as described previously, e.g., as in  FIG. 21 . The set of envelopes  921  are also applied to maxima block  922 , which at each instant produces an output signal  923  equal to the largest of its input signals, i.e., output signal  923  is the largest envelope. Largest envelope  923  is applied to a fast saturation level regulator  924 , producing a fast saturation level  925 . In one embodiment, fast saturation level regulator  924  comprises a peak detector with an instantaneous rise time, and a release time in the range 50 to 750 milliseconds. 
     Largest envelope  923  is also applied to a slow saturation level regulator  926 , producing a slow saturation level  927 . In one embodiment, slow saturation level regulator  926  is implemented as in  FIG. 20 . A purpose of slow saturation level regulator  926  is to improve transitions from one environment to the next, e.g., to compensate for transitions from one talker and another talker or from a quiet room and a noisy street. 
     The set of base level signals  941  is also applied to maxima block  942 , which at each instant produces an output signal  943  equal to the largest of its input signals, i.e., output signal  943  is the largest base level. The largest base level  943  is applied to constrain range block  944 , producing minimum saturation level  945 . The operation of constrain range block  944  can be expressed in the MATLAB® language, e.g., as:
 
min_sat_level=largest_base_level+min_range
 
where the quantities are all expressed in decibels, and min_range is for example 20 dB. This has the purpose of ensuring that the saturation level is kept at least a specified number of decibels above the base level, i.e., to ensure that the dynamic range between saturation level and base level exceeds a minimum allowed value.
 
     Fast saturation level  925 , slow saturation level  927  and minimum saturation level  945  are applied to maxima block  930 , which at each instant produces an output saturation level signal  931  equal to the largest of its input signals. 
       FIG. 16  is a schematic diagram of a sound processor module  1686  configured to be incorporated into a multi-band feature-based regulator system (which can be implemented in, for example, a hearing prosthesis  1684 , e.g., a cochlear implant system) according to an embodiment of the present invention. Sound processor module  1686  can include any of the feature-based regulator systems (and their various FBR-G, etc.) discussed herein. 
     In  FIG. 16 , the cochlear implant system comprises an external component  1685  (e.g., a behind-the-ear (BTE) unit) which is directly or indirectly attached to the body (not shown) of the recipient, and an internal or implantable component (not shown) which is temporarily or permanently implanted in the recipient. External component  1685  typically comprises one or more sound input elements for detecting sound such as a microphone  1683 , a sound processor module  1686 , a power source (not shown) and an external transmitter unit (not shown). Sound processor module  1686  processes the output of microphone  1683 , which is typically positioned by an auricle of the recipient. Sound processor module  1686  generates encoded signals, sometimes referred to as encoded data signals, which are provided to the external transmitter unit via a cable (not shown). Sound processor module  1686  can include a programmable processor  1688 , e.g., a digital signal processor (DSP), application-specific integrated circuit (ASIC), etc. Processor  1688  is operatively coupled to a memory  1689 , e.g., random access memory (RAM) and/or read-only memory (ROM). Processor  1688  also is operatively coupled via interface  1687 , e.g., to the microphone and the external transmitter unit. 
       FIG. 17  illustrates a flow diagram that describes a multi-band feature-based gain procedure implemented by, e.g., a sound processor module for a cochlear implant (e.g.,  1686 ), according to an embodiment of the present invention. Starting at block  1701 , a frequency analysis (e.g., by a filter bank or by an FFT) is performed upon a digitized audio signal to generate analysis signals. At block  1702 , envelope detection is performed on each analysis signal. At block  1703 , each of the envelopes is multiplied by a corresponding one of a plurality of gain values, to generate scaled envelopes. At block  1704 , one or more features (as discussed herein) are extracted from each scaled envelope. At block  1705 , the features are compared to decision thresholds, to generate feature decision signals. At block  1706 , the gains are revised (or in other words, refined), based on the feature decision signals. At block  1707 , loudness growth functions are applied to the scaled envelopes to generate magnitude signals. At block  1708 , the magnitude signals are further processed to produce a set of stimulus pulses. 
       FIG. 18  illustrates a flow diagram that describes a multi-band feature-based adaptive LGF procedure implemented by, e.g., a sound processor module for a cochlear implant (e.g.,  1686 ), according to an embodiment of the present invention. Starting at block  1801 , a frequency analysis (e.g., by a filter bank or by an FFT) is performed upon a digitized audio signal to generate analysis signals. At block  1802 , envelope detection is performed on each analysis signal. At block  1803 , one or more features (as discussed herein) are extracted from each envelope. At block  1804 , the LGF base levels and saturation levels are revised (or, in other words, refined), based on the extracted features. At block  1805 , loudness growth functions, utilizing the revised base levels and saturation levels, are applied to the envelopes to generate magnitude signals. At block  1806 , the magnitude signals are further processed to produce a set of stimulus pulses. 
       FIG. 19  illustrates a flow diagram that describes a multi-band feature-based gain procedure implemented by, e.g., a sound processor module for an application that provides an audio signal output, according to an embodiment of the present invention. Starting at block  1901 , a frequency analysis (e.g., by a filter bank or by an FFT) is performed upon a digitized audio signal to generate analysis signals. At block  1902 , each analysis signal is multiplied by a corresponding one of a plurality of gain values, to generate scaled analysis signals. At block  1903 , envelope detection is performed on each scaled analysis signal. At block  1904 , one or more features (as discussed herein) are extracted from each scaled envelope. At block  1905 , the features are compared to decision thresholds, to generate feature decision signals. At block  1906 , the gains are revised (or, in other words, refined), based on the feature decision signals. At block  1907 , the analysis signals are combined (e.g., by summation or by an inverse FFT) to produce an output signal. 
     Throughout the specification and the claims that follow, unless the context requires otherwise, the words “comprise” and “include” and variations such as “comprising” and “including” will be understood to imply the inclusion of a stated integer or group of integers, but not the exclusion of any other integer or group of integers. 
     Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, operation, or other characteristic described in connection with the embodiment may be included in at least one implementation of the present invention. However, the appearance of the phrase “in one embodiment” or “in an embodiment” in various places in the specification does not necessarily refer to the same embodiment. It is further envisioned that a skilled person could use any or all of the above embodiments in any compatible combination or permutation. 
     While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art that various changes in form and detail may be made therein without departing from the scope of the present invention. Thus, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.