Abstract:
A signal processing method for cochlear implant is performed by a speech processor and comprises a noise reduction stage and a signal compression stage. The noise reduction stage can efficiently reduce noise in a electrical speech signal of a normal speech. The signal compression stage can perform good signal compression to enhance signals to stimulate cochlear nerves of a patient with hearing loss. The patient who uses a cochlear implant performing the signal processing method of the present disclosure can accurately hear normal speech.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a divisional application of U.S. patent application Ser. No. 14/838,298 filed on Aug. 27, 2015. The entire disclosure of the prior application is considered to be part of the disclosure of the accompanying application and is hereby incorporated by reference. 
     
    
     BACKGROUND 
       [0002]    The present disclosure relates to a signal processing method, and more particularly to a signal processing method applied in cochlear implant. 
         [0003]    Cochlear implant is a surgically implanted electronic device that provides a sense of sound to patients with hearing loss. Progress of the cochlear implant technologies has enabled many such patients to enjoy high quality level of speech understanding. 
         [0004]    Noise reduction and signal compression are critical stages in the cochlear implant. For example, a conventional cochlear implant comprising multiple microphones can enhance the sensed speech volume. However, noise in the sensed speech is also amplified and compressed so as to affect the speech clarity. Besides, the multiple microphones increase hardware cost. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0005]    The present disclosure is illustrated by way of embodiments and accompanying drawings. 
           [0006]      FIG. 1  is a circuit block diagram of a cochlear implant of a prior art. 
           [0007]      FIG. 2  is a detailed circuit diagram showing a speech processor connected to a microphone and pulse generators of an exemplary embodiment of the present disclosure. 
           [0008]      FIG. 3  is a schematic view of a single-layered DAE-based NR structure. 
           [0009]      FIG. 4A  shows an amplitude envelope of a clean speech signal;  FIG. 4B  shows an amplitude envelope of a noisy speech signal;  FIG. 4C  shows an amplitude envelope detected by a conventional log-MMSE estimator;  FIG. 4D  shows an amplitude envelope detected by a conventional KLT estimator; and  FIG. 4E  shows an amplitude envelope detected by the exemplary embodiment of the present disclosure. 
           [0010]      FIG. 5  is a circuit block diagram of one channel of the speech processor of  FIG. 2 . 
           [0011]      FIG. 6  is a waveform diagram of an amplitude envelope detected by an envelope detection unit of the speech processor of  FIG. 2 . 
           [0012]      FIG. 7  is a waveform diagram of an output frame generated by the signal compressor of the speech processor of  FIG. 2 . 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    With reference to  FIG. 1 , a basic and conventional configuration of a circuit block diagram of a cochlear implant comprises a microphone  11 , a speech processor  12 , a transmitter  13 , a receiver  14 , a pulse generator  15 , and an electrode array  16 . The microphone  11  and the speech processor  12  are assembled to be mounted on a patient&#39;s ear. The transmitter  13  is adapted to be attached on skin of the patient&#39;s head. The receiver  14 , the pulse generator  15 , and the electrode array  16  are implanted under the skin on head of a patient. 
         [0014]    The microphone  11  is an acoustic-to-electric transducer that converts a normal speech sound into an electrical speech signal. The speech processor  12  receives the electrical speech signal and converts the electrical speech signal into multiple output sub-speech signals in different frequencies. The transmitter  13  receives the output sub-speech signals from the speech processor  12  and wirelessly sends the output sub-speech signals to the receiver  14 . The pulse generator  15  receives the output sub-speech signals from the receiver  14  and generates different electrical pulses based on the output sub-speech signals to the electrode array  16 . The electrode array  16  includes a plurality of electrodes  161  and each of the electrodes  161  electrically connected to different cochlear nerves of the patient&#39;s inner ear. The electrodes  161  output the electrical pulses to stimulate the cochlear nerves, such that the patient can hear something approximating to normal speech. 
         [0015]    The present disclosure provides a signal processing method for cochlear implant and the cochlear implant using the same. The signal processing method is performed by a speech processor of the cochlear implant. The signal processing method is configured to compress an input speech signal into a predetermined amplitude range, which includes a noise reduction stage and a signal compression stage. 
         [0016]    In more detail, with reference to  FIG. 2 , the speech processor  12  has multiple channels including a first channel, a second channel, . . . , an i-th channel, . . . , and a n-th channel, wherein i and n are positive integers. Each one of the channels has a band-pass filter  121 , an envelope detection unit  122 , and a signal compressor  123 . The envelope detection unit  122  is used to detect an amplitude envelope of a signal and can have a rectifier  124  and a low-pass filter  125 . In the present disclosure, a noise reduction unit  126  is added. The noise reduction unit  126  is connected between the microphone  11  and the band-pass filters  121  of each one of the channels. In time domain, when the noise reduction unit  126  receives the electrical speech signal from the microphone  11 , the noise reduction unit  126  segments the electrical speech signal into several continuous frames to reduce noise of the frames. For example, when a time length of the electrical speech signal is 3 seconds, the noise reduction unit  126  can segment the electrical speech signal into  300  continuous frames, wherein a time length of each one of the frames of the electrical speech signal is 10 milliseconds. 
         [0017]    Based on the above configuration, the band-pass filter  121  of each one of the channels sequentially receives the frames of the electrical speech signal from the noise reduction unit  126 . The band-pass filter  121  of each one of the channels can preserve elements of each one of the frames of the electrical speech signal within a specific frequency band and remove elements beyond the specific frequency band from such frame. The specific frequency bands of the band-pass filters  121  of the channels are different from each other. Afterwards, the amplitude envelopes of the frames of the electrical speech signal are detected by the envelope detection units  122  and are provided to the signal compressors  123 . 
         [0018]    The present disclosure relates to a noise reduction stage performed by the noise reduction unit  126  and a signal compression stage performed by the signal compressor  123 . The noise reduction stage and the signal compression stage are described below. 
         [0019]    1. Noise Reduction Stage 
         [0020]    The noise reduction unit  126  can be performed in a deep denoising autoencoder (DDAE)-based noise reduction (NR) structure. The DDAE-based NR structure is widely used in building a deep neural architecture for robust feature extraction and classification. In brief, with reference to  FIG. 3 , a single-layered denoising autoencoder (DAE)-based NR structure comprises an input layer  21 , a hidden layer  22 , and an output layer  23 . The DDAE-based NR structure is a multiple-layered DAE-based NR structure comprising the input layer  21 , the output layer  23 , and multiple hidden layers  22 . Because the parameter estimation and speech enhancement procedure of DDAE is the same as for that of single-layered DAE, only the parameter estimation and speech enhancement for the single-layered DAE is presented, for ease of explanation. The same parameter estimation and speech enhancement procedures can be followed for the DDAE. 
         [0021]    The input layer  21  receives an electrical speech signal y from the microphone  11  and segments the electrical speech signal y into a first noisy frame y 1 , a second noisy frame y 2 , . . . , a t-th noisy frame y t , . . . , and a T-th noisy frame y T , wherein T is a length of the current utterance. In other words, the present disclosure may segment an input speech signal, such as the electrical speech signal y, into a plurality of time-sequenced frames, such as the noisy frames y 1 , y 2 , . . . , and y T . For the elements in the t-th noisy frame y t , the noise reduction unit  126  reduces noise in the t-th noisy frame y t  to form a t-th clean frame x t . Afterwards, the output layer  23  sends the t-th clean frame x t  to the channels of the speech processor  12 . 
         [0022]    A relationship between the t-th noisy frame y t  and the t-th clean frame x t  can be represented as: 
         [0000]        x   t   =W   2   h ( y   t )+ b   2    (equation (1))
 
         [0000]    wherein h(y t ) is a function including W 1  and b 1  in time domain and W 1  and W 2  are default connection weights in time domain. b 1  and b 2  are default vectors of biases of the hidden layers  22  of the DDAE-based NR structure in time domain. 
         [0023]    In another embodiment, the relationship between the t-th noisy frame y t  and the t-th clean frame x t  can be represented as: 
         [0000]        x   t =Inv F{ ( W   2   ′h′ ( F{y   t })+ b   2 ′)}  (equation (2))
 
         [0000]    wherein F{} is a Fourier transform function to transfer the t-th noisy frame y t  from time domain to frequency domain and h′( ) is a function including W 1 ′ and b 1 ′; W 1 ′ and W 2 ′ are default connection weights in frequency domain. b 1 ′ and b 2 ′ are default vectors of biases of the hidden layers  22  of the DDAE-based NR structure in frequency domain and InvF { } is an inverse Fourier transform function to obtain the t-th clean frame x t . 
         [0024]    According to experiment results, the t-th clean frame x t  deduced from the Fourier transform and the inverse-Fourier transform as mentioned above has better performance than that without the Fourier transform and the inverse-Fourier transform. 
         [0025]    For the time domain based method as shown in equation (1), h(y t ) can be represented as: 
         [0000]    
       
         
           
             
               
                 
                   
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         [0026]    For the frequency domain based method shown in equation (2), h′(F{y t ) can be represented as: 
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         [0027]    Regarding the parameters including W 1 , W 2 , b 1  and b 2  in time domain or W 2 ′, b′ and b 2 ′ in frequency domain, they are preset in the speech processor  12 . 
         [0028]    For example, in time domain, the parameters including W 1 , W 2 , b 1  and b 2  in equations (1) and (3) are obtained from a training stage. Training data includes a clean speech sample u and a corresponding noisy speech sample v. Likewise, the clean speech sample u is segmented into several clean frames u 1 , u 2 , . . . , u T′ , and the noisy speech sample v is segmented into several noisy frames v 1 , v 2 , . . . , v T′ , wherein T′ is a length of a training utterance. 
         [0029]    The parameters including W 1 , W 2 , b 1  and b 2  of equation (1) and equation (3) are optimized based on the following objective function: 
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         [0030]    In equation (5), θ is a parameter set {W 1 , W 2 , b 1 , b 2 }, T′ is a total number of the clean frames u 1 , u 2 , . . . , u T′ , and η is a constant used to control the tradeoff between reconstruction accuracy and regularization on connection weights (for example, η can be set as 0.0002). The training data including the clean frames u 1 , u 2 , . . . , u T′ , and the training parameters of W 1-test , W 2-test , b 1-test  and b 2-test  can be substituted into the equation (1) and equation (3) to obtain a reference frame ū t . When the training parameters of W 1-test , W 2-test , b 1-test , and b 2-test  can make the reference frame ū t  be approximate to the clean frames u t , such training parameters of W 1-test , W 2-test , b 1-test , and b 2-test  are taken as the parameters of W 1 , W 2 , b 1  and b 2  of equation (1) and equation (3). When the noisy speech sample v approximates the electrical speech signal y, the training result of the parameters of W 1 , W 2 , b 1  and b 2  can be optimized. The optimization of equation (5) can be done by using any unconstrained optimization algorithm. For example, a Hessian-free algorithm can be applied in the present disclosure. 
         [0031]    After training, optimized parameters including W 1 , W 2 , b 1  and b 2  are obtained, to be applied to equation (1) and equation (3) for real noise reduction application. 
         [0032]    In frequency domain, the parameters including W 1 ′, W 2 ′, b 1 ′ and b 2 ′ of equation (2) and equation (4) are optimized based on the following objective function: 
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         [0033]    In equation (6), θ is a parameter set {W 1 ′, W 2 ′, b 1 ′, b 2 ′}, T′ is a total number of the clean frames u 1 , u 2 , . . . , u T′ , and η is a constant used to control the tradeoff between reconstruction accuracy and regularization on connection weights (for example, η can be set as 0.0002). The training data including the clean frames u 1 , u 2 , u T′  and the training parameters of W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ can be substituted into the equation (2) and equation (4) to obtain a reference frame ū t . When the training parameters of W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ can make the reference frame ū t  be approximate to the clean frames u t , such training parameters of W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ are taken as the parameters of W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ of equation (2) and equation (4). When the noisy speech sample v approximates the electrical speech signal y, the training result of the parameters of W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ can be optimized. The optimization of equation (6) can be done by using any unconstrained optimization algorithm. For example, a Hessian-free algorithm can be applied in the present disclosure. 
         [0034]    After training, optimized parameters including W 1-test ′, W 2-test ′, b 1-test ′ and b 2-test ′ are obtained, to be applied to equation (2) and equation (4) for real noise reduction application. 
         [0035]    With reference to  FIGS. 4A and 4B ,  FIG. 4A  an amplitude envelope of a clean speech signal is shown and  FIG. 4B  shows an amplitude envelope of a noisy speech signal.  FIG. 4C  shows an amplitude envelope detected by a conventional log-MMSE (minimum mean square error) estimator.  FIG. 4D  shows an amplitude envelope detected by a conventional KLT (Karhunen-Loeve transform) estimator.  FIG. 4E  shows an amplitude envelope detected by the present disclosure. Comparing  FIG. 4E  with  FIG. 4A , the result of detection is most closely approximate to the clean speech signal, which means the noise is removed. Comparing  FIG. 4B  with  FIGS. 4C and 4D , the results of detection as illustrated in  FIGS. 4C and 4D  are still noisy. 
         [0036]    According to experiment result as mentioned above, the signal performances of the conventional log-MMSE estimator and the KLT estimator are not as good as those obtained by the procedures of the present disclosure. The procedures of the present disclosure have better noise reducing efficiency. 
         [0037]    2. Signal Compression Stage 
         [0038]    With reference to  FIGS. 2 and 5 , for the i-th channel of the speech processor  12 , the signal compressor  123  receives an amplitude envelope of the t-th clean frame x t  within the specific frequency band from the noise reduction unit  126 , through the band-pass filter  121  and the envelope detection unit  122 . The amplitude envelope  30  of the t-th clean frame x t  is illustrated in  FIG. 6 . As shown in  FIG. 6 , the amplitude envelope  30  of t-th clean frame x t  is time-varying. 
         [0039]    The signal compressor  123  of the present disclosure comprises a compression unit  127 , a boundary calculation unit  128 , and a compression-factor-providing unit  129 . The compression unit  127  and the boundary calculation unit  128  are connected to the envelope detection unit  122  to receive the amplitude envelope  30  of the t-th clean frame x t  in real-time. With reference to  FIGS. 5 and 6 , the boundary calculation unit  128  can detect an upper boundary UB and a lower boundary LB in the amplitude envelope of the t-th clean frame x t . The results of calculations as to the upper boundary UB and the lower boundary LB are transmitted to the compression-factor-providing unit  129 . The upper boundary UB and the lower boundary LB can be calculated by: 
         [0000]      UB=   x     t +α 0 ×(max( x   t )−   x     t )   (equation (7))
 
         [0000]      LB=   x     t +α 0 ×(min( x   t )−   x     t )   (equation (8))
 
         [0000]    wherein α 0  is an initial value. 
         [0040]    The compression unit  127  receives the amplitude envelope  30  of the t-th clean frame x t  and outputs a t-th output frame z t . Inputs of the compression-factor-providing unit  129  are connected to an input of the compression unit  127 , an output of the compression unit  127 , and an output of the boundary calculation unit  128 . Results of calculating the upper boundary UB, the lower boundary LB, and the t-th output frame z t  are received from unit  128 . An output of the compression-factor-providing unit  129  is connected to the input of the compression unit  127 , such that the compression-factor-providing unit  129  provides a compression factor α t  to the compression unit  127 . The compression factor α t  is determined according to a previous compression factor α t-1 , the upper boundary UB, the lower boundary LB, and the t-th output frame z t . In brief, the procedures herein may determine the compression factor α t  for a frame based on the frame&#39;s amplitude upper boundary UB and lower boundary LB. When the t-th output frame z t  is in a monitoring range between the upper boundary UB and the lower boundary LB, the compression factor α t  can be expressed as: 
         [0000]      α t =α t-1 +Δα 1    (equation (9))
 
         [0000]    where Δα 1  is a positive value (i.e., Δα 1 =1). 
         [0041]    In contrast, when the t-th output frame z t  is beyond the monitoring range, the compression factor α t  can be expressed as: 
         [0000]      α t =α t-1 +Δα 2    (equation (10))
 
         [0000]    where Δα 2  is a negative value (i.e., Δα 2 =−0.1). 
         [0042]    The t-th output frame z t  can be expressed as: 
         [0000]        z   t =α t ×( x   t   − x     t )+   x     t    (equation (11))
 
         [0000]    where  x   t  is a mean of the amplitude envelope of the t-th clean frame x t . 
         [0043]    According to equations (9) and (10), a present compression factor α t  is obtained by a previous compression factor α t-1 . It can be understood that the compression factor α t  for the next frame can be modified based on the next frame&#39;s amplitude upper boundary UB and lower boundary LB. According to equation (11), the t-th output frame z t  is repeatedly adjusted by the t-th clean frame x t  and the results of calculating UB, LB, and α t . According to experiment result, the signal compression capability is good. As illustrated in  FIG. 7 , speech components A in the t-th output frame z t  are amplified. The speech components A even reach the upper boundary UB. In contrast, noise components B are not significantly amplified. Therefore, the t-th output frame z t  is enhanced to stimulate the cochlear nerves and the patient can accurately hear a spoken conversation.