Abstract:
A noise cancellation device (NCD) comprises a microphone, an analog-to-digital converter, a digital-to-analog converter, a rechargeable battery, and a processor. The NCD acquires an audio input, from an external device such as a stethoscope or a cell phone, and passes the analog data into an ADC (analog-to-digital converter) for signal conversion. The digitized signals are then passed to the processor for further processing. The processor contains all the processing functions such as preprocessing (divide the input data into frames and apply shaping function to each frame), short term Fourier transform (STFT), adaptive filtering, inverse STFT, and signal synthesis.

Description:
GOVERNMENT LICENSE RIGHTS 
       [0001]    This invention was made with government support under contract number 
         [0002]    N68335-11-C-0326 award by NAVAL AIR WARFARE CTR AIRCRAFT DIVISION. The government has certain rights in the invention. 
     
    
     BACKGROUND 
       [0003]    1. Field of the Invention 
         [0004]    The present invention generally relates to signal enhancement in noisy environments, and more particularly, relates to a compact plug-in noise cancellation device that can filter out background noises and retain useful signals. 
         [0005]    2. Description of the Prior Art 
         [0006]    Signal enhancement may be desired in many applications. For instance, it may be difficult for a doctor to perform auscultation in noisy environments such as ambulance, construction sites, and factories, as background noise will overwhelm a patient&#39;s internal body sounds such as heartbeat. It may be hard for a person to make a phone call in noisy environments such as restaurants, conference halls, and streets with a lot of traffic. Also, when soldiers call for close air support (CAS), they are normally very near the frontline where mortar explosions, machine guns, and other types of background noises are overwhelming. Communication quality is compromised in these noisy environments and thus effective signal enhancement is needed to improve the signal-to-noise ratio (SNR) so as to achieve satisfactory communication quality. 
         [0007]    Conventional signal enhancement techniques have reasonably good performance when the background noise is stationary, but if background noise is intermittent or non-stationary, then the noise suppression performance is not good. Moreover, conventional signal enhancement techniques cannot deal with intermittent and large amplitude noise where the SNR may be less than zero, that is, the amplitude of the useful signal is much less than that of the background noise. Therefore, it is desirable to have a compact, lightweight and low lost plug-in noise cancellation device that can effectively suppress various kinds of background noises and retain useful signals such as heartbeat in auscultation and speech in phone communications. 
       SUMMARY OF THE INVENTION 
       [0008]    The current invention is a noise cancellation device (NCD) that comprises of a microphone, an analog-to-digital converter (ADC), a digital-to-analog converter (DAC), a rechargeable battery, and a processor. The NCD acquires an audio input, from an external device such as a stethoscope or a cell phone, and passes the analog data into an ADC for signal conversion. The digitized signals are then passed to the processor for further processing. The processor contains all the processing functions such as preprocessing (divide the input data into frames and apply shaping function to each frame), short term Fourier transform (STFT), adaptive filtering, inverse STFT, and signal synthesis. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0009]    Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings, in which: 
           [0010]      FIG. 1  illustrates a block diagram of a plug-in noise cancellation device according to an embodiment of the present invention; 
           [0011]      FIG. 2  illustrates an internal configuration of a signal enhancement processor in the plug-in noise cancellation device as shown in  FIG. 1 , according to an embodiment of the present invention; 
           [0012]      FIG. 3  is a flow chart that illustrates the process of the signal enhancement processor of  FIG. 2  in accordance with a novel algorithm called Minimal Mean Squared Error in Frequency domain with N-coefficient and Signal Detection (FMSENSD), according to an embodiment of the present invention; 
           [0013]      FIG. 4  illustrates the performance of the signal enhancement processor of  FIG. 2  using the FMSENSD algorithm, as compared to the performance of a signal enhancement processor using a well-known algorithm called Recursive Least Square (RLS); 
           [0014]      FIG. 5  illustrates an exemplary mechanical dimension and appearance of a compact plug-in noise cancellation device according to an embodiment of the present invention; 
           [0015]      FIG. 6  schematically illustrates an application of a compact plug-in noise cancellation device for high performance auscultation according to an embodiment of the present invention; and 
           [0016]      FIG. 7  schematically illustrates an application of a compact plug-in noise cancellation device for bi-directional phone communication according to an embodiment of the present invention. 
       
    
    
     DESCRIPTION OF EMBODIMENTS 
       [0017]    Embodiments of the present invention will now be described in detail with reference to the drawings. In the detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be implemented without these specific details. 
         [0018]      FIG. 1  illustrates a block diagram of a plug-in noise cancellation device  100  according to an embodiment of the present invention. As shown in  FIG. 1 , the noise cancellation device  100  comprises a primary microphone  102 , a background microphone  104 , an analog-to-digital (A/D) converter  106 , a signal enhancement processor  108 , a digital-to-analog (D/A) converter  110  and a rechargeable power supply  112 . The noise cancellation device  100  acquires two inputs respectively by the primary microphone  102  and the background microphone  104 . Specifically, a first input is received by the primary microphone  102  from an external device (not shown) such as a stethoscope or a cell phone and includes a mixed sound signal containing a useful signal and a noise signal, while a second input is received by the background microphone  104  from the background environment and includes merely a background noise signal. The noise cancellation device  100  makes use of the dual microphone configuration to acquire the dynamically changing background noise signal as a reference signal for signal enhancement process. The analog mixed sound signal and background noise signal output from the primary microphone  102  and the background microphone  104  are converted into digitized signals in the A/D converter  106 . Then the digitized signals are passed to the signal enhancement processor  108 , in which the digitized mixed sound signal is subjected to adaptive filtering with the background noise signal as the reference signal so as to filter out the noise signal contained in the mixed sound signal and obtain the enhanced signal of high SNR. Finally, the enhanced signal output from the signal enhancement processor  108  is reconverted into analog signal by the D/A converter  110 . 
         [0019]    The signal enhancement processor  108  is the most critical component of the noise cancellation device  100 .  FIG. 2  illustrates the detailed internal configuration of the signal enhancement processor  108  that comprises two short-time Fourier transformers (STFTs)  202 ,  204 , an adaptive filter  206  and an Inverse STFT  208 . According to an embodiment of the present invention, the signal enhancement processor  108  performs the adaptive filtering on incoming signals in accordance with a novel algorithm called Minimal Mean Squared Error in Frequency domain with N-coefficient and Signal Detection (FMSENSD). Unlike conventional adaptive filtering algorithms such as Least Mean Square (LMS), Normalized Least Mean Square (NLMS), and Recursive Least Square (RLS) that usually work in time domain, the adaptive algorithm FMSENSD works in frequency domain and achieves excellent sound quality. The detailed FMSENSD algorithm is as follows: 
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                 Algorithm FMSENSD 
               
               
                 Input: x is the mixed sound signal; r is the reference noise signal 
               
               
                 (background noise only) 
               
               
                 Output: e is the filtered sound 
               
               
                 Parameters: nwin, noverlap, nfft, N (filter length) 
               
               
                 Algorithm: 
               
             
          
           
               
                  1. 
                 fx = spectrogram(x, nwin,noverlap,nfft);afx = abs(fx) //compute 
               
               
                   
                 spectral magnitudes of mixed sound 
               
               
                  2. 
                 fr = spectrogram(r,nwin,noverlap,nfft);afr = abs(fr) //compute 
               
               
                   
                 spectral magnitudes of reference noise 
               
               
                  3. 
                 for each band i  // estimate the channel response hh using all signal 
               
               
                   
                 components in the mixed sound 
               
             
          
           
               
                  4. 
                 AFR i  = Toeplitz(afr i ,N) 
               
               
                  5. 
                 hh = (AFR i   T AFR i ) −1 AFR i   T afx i , afy i  = afr i  * hh 
               
             
          
           
               
                  6. 
                 end 
               
               
                  7. 
                 afe = afx − afy  //compute residual 
               
               
                  8. 
                 nafe = smooth(afe.{circumflex over ( )}2) 
               
               
                  9. 
                 T = median(nafe) 
               
               
                 10. 
                 o = nafe &lt; T // extract silence regions 
               
               
                 11. 
                 for each band i  // estimate the channel response hh again using 
               
               
                   
                 noise only signal components 
               
             
          
           
               
                 12. 
                 AFR i  = Toeplitz(afr i (o),N) 
               
               
                 13. 
                 hh = (AFR i   T AFR i ) −1 AFR i   T afx i (o), afy i  = afr i  * hh 
               
             
          
           
               
                 14. 
                 end 
               
               
                 15. 
                 afe = max(0,afx − afy)  // compute residual 
               
               
                 16. 
                 e = overlap _add(afe,angle(fx),nwin,noverlap,nfft) //reconstruct 
               
             
          
           
               
                 time domain signals 
               
               
                   
               
             
          
         
       
     
         [0020]    Hereinafter, the FMSENSD algorithm will be described in detail with reference to the flow chart in  FIG. 3  that illustrates the process of the signal enhancement processor of  FIG. 2  in accordance with the FMSENSD algorithm, according to an embodiment of the present invention. In the embodiment, the adaptive filtering is carried out in the frequency domain, so the mixed sound signal x and the reference noise signal r need to be processed by the STFTs  202 ,  204  respectively to compute the spectral magnitudes of these signals (step  302 ). Then, at step  304 , the channel response hh in frequency domain can be estimated using all signal components in the mixed sound signal x that include useful signal components and noise signal components based on linear Minimum Mean Square Error (MMSE) estimation. Also, with the estimated channel response hh, the noise signal y contained in the mixed sound signal can be estimated by calculating the convolution of the estimated channel response hh and the reference noise signal r (step  304 ). Next, at step  306 , the silence regions in which the mixed sound signal is estimated to merely have noise signal components can be extracted, and then at step  308 , the channel response hh can be estimated again only using noise signal components in the mixed sound signal x and the noise signal y contained in the mixed sound signal x can be more accurately estimated by calculating the convolution of the estimated channel response hh and the reference noise signal r. After having the noise signal y estimated, the useful signal contained in the mixed sound signal x can be obtained by filtering out the noise signal y (step  310 ). Finally, at step  312 , the useful signal in the mixed sound signal x processed in frequency domain can be subjected to the inverse Fourier transform in the Inverse STFT  208  so as to get the reconstructed time-domain useful signal to be output from the signal enhancement processor  108 . 
         [0021]    The FMSENSD algorithm is a high performance algorithm that converges fast and works very well under noisy conditions and reverberant environments. In particular, the filter length N is a design parameter, which can be adjusted by user to deal with different reverberant conditions. This is a key innovation of our algorithm. Moreover, FMSENSD requires much less computations as compared to RLS and hence is very suitable for real-time applications. Extensive experiments have been done for different applications such as auscultation enhancement in 80 dBA noisy environment, speech enhancement in 120 dBA noisy environment, and speech enhancement using cell phones (e.g. iPhone 4). For example, the FMSENSD algorithm can be applied to enhance auscultation in noisy environments such as International Space Station (ISS) where the noisy level can be more than 79 dBA. Conventional stethoscopes are designed for quiet environments (45 dBA) and hence they are not suitable for noisy environments.  FIG. 4  demonstrates the performance of the signal enhancement processor using the FMSENSD algorithm under various environments, as compared to the performance of a signal enhancement processor using a well-known algorithm called Recursive Least Square (RLS). It can be seen that the FMSENSD algorithm performs significantly better than the RLS algorithm. Moreover, the computational complexity of the FMSENSD algorithm is much simpler than RLS. 
         [0022]    The noise cancellation device of the present invention has the advantages of compact model and low cost.  FIG. 5  illustrates an exemplary mechanical dimension and appearance of the compact plug-in noise cancellation device according to an embodiment of the present invention. With the advent of fast processors, the size of the noise cancellation device may be similar to a USB portable drive (2.5″ (length)×2″ (width)×0.5″ (thickness)) and the cost may be less than 20 dollars in five years. In addition, the noise cancellation device of the present invention is a portable, light weight and plug-in device. It is very convenient to connect/disconnect the noise cancellation device to/from a system in a noisy environment based on actual needs. 
         [0023]    With the forgoing described advantages, the noise cancellation device of the present invention is applicable to many applications such as biometrics (voiceprint), speech enhancement for construction workers, factory works, law enforcement agents, soldiers, doctors and cell phone users. 
         [0024]      FIG. 6  schematically illustrates an application of a compact plug-in noise cancellation device  600  for high performance auscultation according to an embodiment of the present invention. When a doctor performs auscultation on a patient in a noisy environment and realizes that the background noise will significantly affect the auscultation, he/she can bring out the noise cancellation device  600  of the present invention and connect the stethoscope output to the primary microphone of the noise cancellation device  600 . When the connected noise cancellation device  600  is turned on, both the stethoscope output sound and the background noise will be simultaneously acquired by the noise cancellation device  600 . Then, as above described, the noise cancellation device  600  will perform the adaptive filtering based on the foregoing FMSENSD algorithm on the stethoscope output sound to generate filtered and enhanced output. In the embodiment, the noise cancellation device  600  provides two outputs: unfiltered output (original signal) and filtered output (enhanced signal). During the auscultation process, the doctor can choose to listen to either the unfiltered or filtered output from the noise cancellation device  600  and makes a judicious diagnosis. Also, the outputs of the noise cancellation device  600  can be connected to a PC or other devices for storage. 
         [0025]    The noise cancellation device of the present invention can also be employed for speech enhancement in battlefield environments. When soldiers call for close air support (CAS), they are normally very near the frontline where mortar explosions, machine guns, and other types of background noises are overwhelming. Communication quality may be compromised. Conventional speech enhancement techniques can only deal with stationary noise, but not intermittent and unpredictable noise such as explosions. Moreover, conventional techniques cannot deal with intermittent and large amplitude noise where the SNR may be less than zero, that is, the amplitude of the useful signal is much less than that of the background noise. Similar to the above auscultation enhancement application, the noise cancellation device of the present invention can be utilized to achieve speech enhancement in battlefield environments. Similar to that illustrated in  FIG. 6 , the primary microphone of the noise cancellation device receives mixed sound signal including voice signal from a soldier and noise signal in the noisy environment, and synchronously the background microphone of the noise cancellation device receives background noise signal. Then the mixed sound signal can be processed with the background noise signal as reference noise signal to obtain the enhanced voice signal. In an experiment, the SNR in the primary microphone is −20.1837 dB and the SNR in the background microphone is −22.3265 dB. Both SNRs are very low. The speech is not intelligible in such a battlefield environment. However, after speech enhancement by the noise cancellation device of the present invention, the speech is recovered quite clearly (the SNR becomes 22.2848 dB). 
         [0026]    In the foregoing described auscultation enhancement application and speech enhancement application in battlefield environments, the communication is only in one direction. In cell phone communications, bi-directional communications are required. Therefore, a variant of the noise cancellation of the present invention is needed so as to be applied for signal enhancement in cell phone communications.  FIG. 7  schematically illustrates an application of a plug-in noise cancellation device  700  for bi-directional cell phone communication according to an embodiment of the present invention. As can be seen from  FIG. 7 , the noise cancellation device  700  has a built-in background microphone for acquiring background noise signals and a DSP board for realizing signal enhancement with the FMSENSD algorithm, which are the same as those of the noise cancellation device  600  shown in  FIG. 6 . However, the noise cancellation device  700  may not include a built-in primary microphone for receiving the mixed sound signal. Instead, two 4-pole sockets are provided in the noise cancellation device  700 . In the setup of the embodiment, one 4-pole socket is connected to a cell phone 4-pole headset for acquiring microphone signal from the cell phone 4-pole headset and transmitting the stereo signal from a cell phone to the cell phone 4-pole headset, while the other 4-pole socket is connected to the cell phone for transmitting real-time enhanced speech signal to the cell phone and receiving stereo signal from the cell phone. Also, as illustrated in  FIG. 7 , a transmission path is provided between the two 4-pole sockets for transmitting stereo signal from the cell phone to the cell phone 4-pole headset. With such a configuration, the noise cancellation device  700  can be connected between a cell phone and a cell phone 4-pole headset to generate and transmit the real-time enhanced speech signal to the cell phone and also receive and convey the stereo signal from the cell phone to the cell phone 4-pole headset. In this way, a bi-direction communication is accomplished. 
         [0027]    While the process described above includes a number of operations that appear to occur in a specific order, it should be apparent that these processes may include more or fewer operations, which may be executed serially or in parallel. Furthermore, the foregoing description provides a disclosure of the invention concerning its implementation, which is not limited by the detailed description but only by the scope of the appended claims. All those other aspects of the invention that will become apparent to a person of skill in the art, who has read the foregoing, are within the scope of the invention and of the following claims.