Abstract:
Speech enhancement in a breathing apparatus is provided using a primary sensor mounted near a breathing mask user&#39;s mouth, at least one reference sensor mounted near a noise source, and a processor that combines the signals from these sensors to produce an output signal with an enhanced speech component. The reference sensor signal may be filtered and the result may be subtracted from the primary sensor signal to produce the output signal with an enhanced speech component. A method for detecting the exclusive presence of a low air alarm noise may be used to determine when to update the filter. A triple filter adaptive noise cancellation method may provide improved performance through reduction of filter maladaptation. The speech enhancement techniques may be employed as part of a communication system or a speech recognition system.

Description:
BACKGROUND 
       [0001]    This document relates to speech enhancement in a breathing apparatus. 
         [0002]    There are numerous situations which require the use of a breathing apparatus such as the absence of a breathable atmosphere or the potential for this condition. An exemplary breathing apparatus consists of a face mask with a regulator that supplies air from a high pressure hose on demand from the user. The high pressure hose is usually connected to an air tank. When the pressure in the air tank falls below a set level, a low air alarm is generated to warn the user. A common low air alarm is generated by a valve in the regulator which releases pulses of air which can easily be sensed by the user. These pulses of air can produce pressure levels inside the mask which exceed the user&#39;s voice pressure levels. These high levels of pressure can act as interfering noise that can make tasks such as communication or automatic speech recognition more difficult. 
         [0003]    A second source of interfering noise results from the turbulence of the air or gas released into the breathing mask by the regulator during inhalation. Inhalation noise may be reduced by turning a microphone off when the pressure drops. 
         [0004]    Inhalation noise may be detected and attenuated by measuring the frequency response of a breathing mask to determine resonances and antiresonances, and by acting on this information. 
       SUMMARY 
       [0005]    In one aspect, generally, a breathing apparatus speech enhancement system includes a breathing mask, a primary sensor which produces a primary signal, and at least one reference sensor which produces a reference signal. A processor combines the sensor signals to produce an output signal with an enhanced speech component. 
         [0006]    Implementations may include one or more of the following features. For example, each of the primary sensor and the reference sensor may be a microphone, such as a microphone of the noise canceling or gradient type. 
         [0007]    The primary sensor may be mounted on the breathing mask so as to be near the mouth of a user wearing the breathing mask. When the breathing mask includes a voice port, the primary sensor may be mounted externally to the mask near the voice port. 
         [0008]    A reference sensor may be mounted near a noise source, such as the user&#39;s mouth. The breathing mask may include a breath screen to shield at least one reference sensor to reduce the impact of air flow from the user&#39;s mouth. 
         [0009]    The system may include a wireless transmitter connected to transmit the primary signal and/or the reference signal wirelessly. 
         [0010]    The system may be incorporated in a communication system and may further include a speech recognition system configured to process the output signal with the enhanced speech component 
         [0011]    The processor may employ a filter to filter the reference signal, and may subtract the filtered reference signal from the primary signal to produce the output signal. The processor may update the filter based on the output signal and the reference signal. The processor may do so in a transform domain to improve a convergence rate of the filter. 
         [0012]    The system may employ techniques for detecting the exclusive presence of an alarm signal. For example, the processor may detect the exclusive presence of an alarm signal by receiving the primary signal, determining the energy of the primary signal, determining a peak count of the number of consecutive energy samples below a first threshold, and determining a valley count of the number of consecutive energy samples above a second threshold. The processor then determines an alarm count of the number of consecutive samples for which the peak count and valley count are below a third threshold, and declares the exclusive presence of the alarm signal when the alarm count exceeds a fourth threshold. The processor may be configured to only update the filter upon detecting the exclusive presence of an alarm signal. 
         [0013]    More general systems and techniques for detecting the exclusive presence of an alarm signal may be provided. For example, a method for such detection may include receiving a digitized audio signal, determining the energy of the digitized audio signal, determining a peak count of the number of consecutive energy samples below a first threshold, determining a valley count of the number of consecutive energy samples above a second threshold, determining an alarm count of the number of consecutive samples for which the peak count and valley count are below a third threshold, and declaring the exclusive presence of the alarm signal when the alarm count exceeds a fourth threshold. A system for such detection may include a processor configured to perform the method described above. 
         [0014]    The system also may employ triple filter noise cancellation techniques to achieve improved noise cancellation performance through reduction of filter maladaptation. For example, the processor may filter the reference signal with an output filter to produce an output filtered reference signal and subtract the output filtered reference signal from the primary signal to produce an output signal. The processor also may filter the reference signal with an evaluation filter to produce an evaluation filtered reference signal, and subtract the evaluation filtered reference signal from the primary signal to produce an evaluation signal. Finally, the processor may filter the reference signal with an update filter to produce an update filtered reference signal, subtract the update filtered reference signal from the primary signal to produce an update signal, modify the update filter based on the reference signal and the update signal, modify the evaluation filter based on the update filter, and modify the output filter based on the output signal and the evaluation signal. 
         [0015]    More general systems and techniques for triple filter noise cancellation may be provided. For example, a method for such noise cancellation may include receiving a digitized primary audio signal, receiving at least one digitized reference audio signal, filtering the at least one reference signal with an output filter to produce an output filtered reference signal, subtracting the output filtered reference signal from the primary signal to produce an output signal, filtering the at least one reference signal with an evaluation filter to produce an evaluation filtered reference signal, subtracting the evaluation filtered reference signal from the primary signal to produce an evaluation signal, filtering the at least one reference signal with an update filter to produce an update filtered reference signal, subtracting the update filtered reference signal from the primary signal to produce an update signal, modifying the update filter based on the reference signal and the update signal, modifying the evaluation filter based on the update filter, and modifying the output filter based on the output signal and the evaluation signal. 
         [0016]    The update filter may be modified only when the exclusive presence of a noise signal is declared, such as by using the techniques above. 
         [0017]    The details of one or more implementations are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0018]      FIG. 1  is a perspective drawing of a breathing mask. 
           [0019]      FIG. 2  is a block diagram of a signal acquisition system. 
           [0020]      FIG. 3  shows an example of a primary signal. 
           [0021]      FIG. 4  shows an example of a reference signal. 
           [0022]      FIG. 5  is a block diagram of an adaptive noise cancellation system. 
           [0023]      FIG. 6  shows an example of an energy signal for the reference signal of  FIG. 4 . 
           [0024]      FIG. 7  shows an example of a peak count for the energy signal of  FIG. 6 . 
           [0025]      FIG. 8  shows an example of a valley count for the energy signal of  FIG. 6 . 
           [0026]      FIG. 9  shows an example of a Low Air Alarm Only count for the energy signal of  FIG. 6 . 
           [0027]      FIG. 10  is a block diagram of a triple filter adaptive noise cancellation system. 
           [0028]      FIG. 11  is a flow chart a triple filter update system. 
           [0029]      FIG. 12  shows a second example of a primary signal. 
           [0030]      FIG. 13  shows an example of the output signal for the primary signal of  FIG. 12 . 
       
    
    
     DETAILED DESCRIPTION 
       [0031]      FIG. 1  shows a breathing mask  10  with a hose  11  which delivers pressurized breathing gas through a demand regulator  12 . A primary sensor  13  is held in position by support  14  which also serves to contain signal wires for the primary sensor. A reference sensor  15  is held in position by support  16  which also serves to contain signal wires for the reference sensor. Breath screen  17  shields the reference sensor from the flow of air emanating from the wearer&#39;s mouth. Cable  18  contains signal wires for the primary and reference sensors which may be connected to the signal acquisition system  20  shown in  FIG. 2 . Voice port  19  provides a passive means for acoustic signals to travel from the interior of the mask to the exterior while maintaining a barrier to the flow of gases. 
         [0032]    In some applications, such as retrofitting an existing breathing mask with sensors, it may be desirable to avoid penetration of the mask by cable  18 . One method of achieving this objective is to connect the sensors to a wireless transmitter mounted interior to the mask. The primary and reference signals are then transmitted to a wireless receiver external to the mask which is connected to a processor. 
         [0033]    Another method of avoiding mask penetration is to mount the sensors external to the mask. An exemplary location for the primary sensor  13  is near the external portion of voice port  19 . An exemplary location for the reference sensor  15  is near demand regulator  12 . 
         [0034]      FIG. 2  shows a signal acquisition system  20  for acquiring and sampling primary and reference acoustic signals. A primary sensor  21 , of which sensor  13  may be an example, senses the primary acoustic signal. A reference sensor  22  senses the reference acoustic signal. The primary and reference sensors are connected to signal conditioning blocks  23  which provide power for the sensors and amplify and bandpass filter the signals to prepare for sampling. Sampling blocks  24  sample the analog signals from the signal conditioning blocks to produce the undelayed primary digital signal and the reference digital signal x(n). For typical speech coding or recognition applications, the sampling rate ranges between 6 kHz and 16 kHz. Delay block  25  delays the undelayed primary digital signal by D samples to produce the primary digital signal y(n) where an exemplary value of D is 13. Delaying the primary signal allows future samples of the reference signal to be used when cancelling noise in the primary signal. 
         [0035]      FIGS. 3 and 4  show examples of primary signal y(n) and reference signal x(n) acquired using signal acquisition system  20  from primary and reference sensors mounted in breathing mask  10  as shown in  FIG. 1  operating at an exemplary sampling rate of 8 kHz. From 0 to about 4800 samples, only the low air alarm signal is present. From about 5000 samples to about 9600 samples, both speech and the low air alarm are present. 
         [0036]      FIG. 5  shows an adaptive noise cancellation system  50  which filters reference signal x(n) using filter  51 . The filter includes M filter coefficients with M having an exemplary value of 128. Each filter coefficient corresponds to a different time offset. 
         [0037]    The filtered reference signal produced by the filter  51  is then removed from the primary signal using subtraction unit  52  to produce output signal e(n). 
         [0000]    
       
         
           
             
               
                 
                   
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         [0038]    Filter update unit  53  updates the filter coefficients h(n, m) based on the primary signal y(n), the reference signal x(n), and the output signal e(n). A simple normalized least mean squares (NLMS) filter update is given by 
         [0000]    
       
         
           
             
               
                 
                   
                     
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         [0000]    where μ is the step size with an exemplary value of 
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         [0000]    is an estimate of the variance of x(n). An estimate for σ x (n) is 
         [0000]      σ x ( n )=max(  σ   x ( n ),σ min )  (3) 
         [0000]    where the function max(a, b) returns the maximum of a or b, σ min  has an exemplary value of 0.01, and 
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         [0000]    where α has an exemplary value of 0.01 and β has an exemplary value of 0.0625. Estimating σ x (n) rather than σ x   2 (n) reduces the dynamic range of the estimated parameter and leads to reduced computation or better performance for a fixed word length implementation. 
         [0039]    In order to prevent maladaptation of the filter when speech is present, a detector is necessary for the condition where only noise is present. A Low Air Alarm Only (LAAO) detector operates by first computing the energy in the reference signal 
         [0000]    
       
         
           
             
               
                 
                   
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         [0000]    where an exemplary value for the block size L is 80 samples. An example of the energy γ(n) is shown in  FIG. 6  for the example reference signal shown in  FIG. 4 . 
         [0040]    The energy γ(n) is compared to a threshold T p  and a peak count N p (n) of the number of consecutive samples below threshold is maintained 
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         [0000]    where S 1  is the update interval with an exemplary value of 10 samples. The update interval S 1  may be larger than 1 without loss due to the rectangular low pass filter of length L applied to estimate the energy in Equation 5. The threshold T p  has an exemplary value of 2.0.  FIG. 7  shows an example of N p (n) for the energy γ(n) of  FIG. 6 . 
         [0041]    The energy γ(n) is compared to a threshold T v  and a valley count N v (n) of the number of consecutive samples above threshold is maintained 
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         [0000]    The threshold T v  has an exemplary value of 0.1.  FIG. 8  shows an example of N v (n) for the energy γ(n) of  FIG. 6 . The valley count N v (n) has been limited to a maximum of 500 in  FIG. 8  to reduce the dynamic range. 
         [0042]    The counts N p (n) and N v (n) are compared to threshold T n  to update LAAO count N a (n) 
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         [0000]    where the threshold T n  has an exemplary value of 500.  FIG. 9  shows an example of N a (n) for the counts N p (n) and N v (n) of  FIG. 7  and  FIG. 8 . When N a (n) exceeds a threshold T a  with an exemplary value of 5000, then a LAAO detection is declared, otherwise, no detection is declared. 
         [0043]    The convergence rate for the NLMS filter update depends on the eigenvalue spread of the covariance matrix of x(n). When x(n) is white noise, the eigenvalue spread is minimal and convergence is rapid. However, the internal reflections of the acoustic signals within the breathing mask produce resonances and antiresonances or poles and zeros in the frequency response which can produce a large spread in the eigenvalues and a consequent slow convergence rate. 
         [0044]    One method of improving the convergence rate is to transform the signals to the frequency domain using the Discrete Fourier Transform (DFT) before updating the filter. This allows normalization by the variance estimate at each DFT frequency which effectively reduces the eigenvalue spread and increases the convergence rate. The filter update is computed by 
         [0000]        h ( n+S,m )= h ( n,m )+μ 1   g ( n,m )  (9) 
         [0000]    where S is an update block size with an exemplary value of 80 samples, μ 1  is a step size with an exemplary value of 0.1, and g(n, m) is the inverse DFT of G(n, k) computed by 
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         [0000]    where K, the DFT length, has an exemplary value of 256. 
         [0045]    The frequency domain update G(n, k) is computed by 
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         [0000]    where X(n,k) is a Short Time Fourier Transform (STFT) of x(n) 
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         [0000]    and E*(n, k) is the complex conjugate of a STFT of e(n) 
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         [0000]    The variance σ x   2 (n, k) may be estimated as follows 
         [0000]          X   ( n,k )=max((| X   r ( n,k )|+| X   i ( n,k )|),σ min )  (14) 
         [0000]    
       
         
           
             
               
                 
                   
                     
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                                   , 
                                   k 
                                 
                                 ) 
                               
                             
                           
                         
                       
                       
                         
                           
                             
                               
                                 α 
                                  
                                 
                                     
                                 
                                  
                                 
                                   
                                     X 
                                     _ 
                                   
                                    
                                   
                                     ( 
                                     
                                       n 
                                       , 
                                       k 
                                     
                                     ) 
                                   
                                 
                               
                               + 
                               
                                 
                                   ( 
                                   
                                     1 
                                     - 
                                     α 
                                   
                                   ) 
                                 
                                  
                                 
                                   
                                     σ 
                                     x 
                                   
                                    
                                   
                                     ( 
                                     
                                       
                                         n 
                                         - 
                                         S 
                                       
                                       , 
                                       k 
                                     
                                     ) 
                                   
                                 
                               
                             
                             , 
                           
                         
                         
                           
                             otherwise 
                             . 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Estimating σ x (n, k) rather than σ x   2 (k, n) reduces the dynamic range of the estimated parameter and leads to reduced computation or better performance for a fixed word length implementation. 
         [0046]    When low amplitude speech is present, such as at the start of a phrase, the LAAO detector may not properly indicate that filter adaptation should be disabled. This can lead to small maladaptations of the filter which reduces noise cancellation performance.  FIG. 10  shows a method of improving performance using triple filter adaptive noise cancellation  100 . The output filter  101  filters the reference signal x(n) and the resultant signal is removed from the primary signal y(n) using subtraction unit  104  to produce the output signal e 0 (n). The evaluation filter  102  filters the reference signal x(n) and the resultant signal is removed from the primary signal y(n) using subtraction unit  105  to produce the signal e 1 (n). The update filter  103  filters the reference signal x(n) and the resultant signal is removed from the primary signal y(n) using subtraction unit  106  to produce the signal e 2 (n). These functions are summarized in Equation 16: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         e 
                         p 
                       
                        
                       
                         ( 
                         n 
                         ) 
                       
                     
                     = 
                     
                       
                         y 
                          
                         
                           ( 
                           n 
                           ) 
                         
                       
                       - 
                       
                         
                           ∑ 
                           
                             m 
                             = 
                             0 
                           
                           
                             M 
                             - 
                             1 
                           
                         
                          
                         
                           
                             
                               h 
                               p 
                             
                              
                             
                               ( 
                               
                                 n 
                                 , 
                                 m 
                               
                               ) 
                             
                           
                            
                           
                             x 
                              
                             
                               ( 
                               
                                 n 
                                 - 
                                 m 
                               
                               ) 
                             
                           
                         
                       
                     
                   
                   , 
                   
                     p 
                     = 
                     
                       0 
                       , 
                       1 
                       , 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   16 
                   ) 
                 
               
             
           
         
       
     
         [0047]    Filter update unit  107  monitors signals e 0 (n), e 1 (n), e 2 (n), x(n), and y(n) to decide how to update filters h 0 (n, k), h 1 (n, k), and h 2 (n, k). First, the estimated standard deviations σ e     0   (n), σ e     1   (n), and σ e     2   (n) are updated according to Equation 17 at an interval of S samples. 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         σ 
                         
                           e 
                           p 
                         
                       
                        
                       
                         ( 
                         n 
                         ) 
                       
                     
                     = 
                     
                       
                         
                           ( 
                           
                             1 
                             - 
                             
                               α 
                               1 
                             
                           
                           ) 
                         
                          
                         
                           
                             σ 
                             
                               e 
                               p 
                             
                           
                            
                           
                             ( 
                             
                               n 
                               - 
                               S 
                             
                             ) 
                           
                         
                       
                       + 
                       
                         
                           
                             α 
                             1 
                           
                           S 
                         
                          
                         
                           
                             ∑ 
                             
                               m 
                               = 
                               0 
                             
                             
                               S 
                               - 
                               1 
                             
                           
                            
                           
                              
                             
                               
                                 e 
                                 p 
                               
                                
                               
                                 ( 
                                 
                                   n 
                                   - 
                                   m 
                                 
                                 ) 
                               
                             
                              
                           
                         
                       
                     
                   
                   , 
                   
                     p 
                     = 
                     
                       0 
                       , 
                       1 
                       , 
                       2 
                     
                   
                 
               
               
                 
                   ( 
                   17 
                   ) 
                 
               
             
           
         
       
     
         [0000]    Then, filter update unit  107  updates h 2 (n, m) in a manner similar to the single filter ANC discussed above with reference to Equation 9: 
         [0000]        h   2 ( n+S,m )= h   2 ( n,m )+μ 1   g ( n,m )  (18) 
         [0000]    The other filters are updated based on the estimated standard deviations σ e     p   (n),p=0, 1, 2 according to the triple filter update flow chart of  FIG. 11 . 
         [0048]    The filter update unit  107  starts the triple filter update at step  111  and executes the triple filter update at an interval of T samples, where T has an exemplary value of 2000. It should be noted that if a filter update is not explicitly encountered in the flow chart, then the new value h p (n, m) should be set to the previous value h p (n−T, m). At step  112 , the unit  107  compares the LAAO count N a (n) to the threshold T a . If the LAAO count is greater than the threshold, the unit  107  executes step  113 . Otherwise, the unit  107  proceeds to step  117 . 
         [0049]    At step  113 , the unit  107  compares the estimated standard deviations σ e     1   (n) and σ e     0   (n). If σ e     i   (n) is less than σ e     0   (n), the unit  107  proceeds to step  114 . Otherwise, the unit  107  proceeds to step  115 . 
         [0050]    At step  114 , the unit  107  sets the coefficients of the output filter h 0 (n, m) to the coefficients of the previous version of the evaluation filter h 1 (n−T, m) since h 1 (n−T, m) produces a lower estimated standard deviation. At step  114 , the unit  107  also sets σ e     0   (n)=σ e     1   (n) since the filter coefficients were updated. 
         [0051]    At step  115 , the unit  107  sets the coefficients of the evaluation filter h 1 (n, m) to the coefficients of the update filter h 2 (n, m) so that the most recent filter update may be evaluated. Step  116  signifies the end of this update. At step  117 , the unit  107  sets all of the filters to the previous value of the output filter h 0 (n−T, m) to prevent maladaptations in h 1 (n, m) and h 2 (n, m) from reaching the output filter h 0 (n, m). The unit  107  also updates the estimated standard deviations appropriately. 
         [0052]      FIG. 12  shows a second example of a primary signal with only a low air alarm signal before sample  35000 . From sample  36000  to sample  44000 , both a low air alarm and inhalation noise are present. From sample  52000  to sample  72000  both a low air alarm and speech are present.  FIG. 13  shows an example of the output signal e 0 (n) of the triple filter adaptive noise cancellation system for the primary signal of  FIG. 12 . The filters adapt to reduce the level of the low air alarm signal from sample  8000  to approximately 15000 samples. After that, the reduced level of the low air alarm is maintained at about 9 dB below its level in the primary signal. There is little effect on the level of speech and inhalation noise. 
         [0053]    Other implementations are within the scope of the following claims.