Abstract:
A communications transmitter which operates as a mobile telephone incorporates a noise suppressor ( 100,  FIG.  1 ) which reduces the background noise in the transmitted voice signal. An external voice activity detector ( 150 ), which operates in conjunction with a noise suppressor ( 100 ) estimates the signal power of the incoming voice signal and compares this to an estimated noise floor. As a result of this comparison, a voice activity factor is applied to an updated noise floor estimate to create a voice activity threshold estimate. The voice activity threshold estimate is then used to decide whether or not to the force noise suppressor ( 100 ) to perform an update of a noise content estimate of the incoming voice signal.

Description:
FIELD OF THE INVENTION  
         [0001]    The invention relates to communication systems and, more particularly, to noise suppression of transmitted voice signals.  
         BACKGROUND OF THE INVENTION  
         [0002]    In a communications system, a transmitting station may employ a noise suppression mechanism in order to reduce the noise content of a transmitted voice signal. This can be particularly useful when the transmitting station is a mobile handset or hands-free telephone operating in the presence of background noise. In these environments, a sudden increase in background noise can cause a far-end listener to hear an undesirable level of noise. This problem is particularly apparent when the transmitter station is operating as a mobile station and the transmitter station includes noise suppression technology. While current noise suppression techniques are effective in reducing background noise in a static or slowly changing noise environment, noise suppression performance can be significantly degraded when the transmitting station is operated in the presence of a rapidly changing noise environment.  
           [0003]    In mobile environments, large changes in background noise can be brought about when the user of the mobile transmitter activates a fan, lowers a window while the mobile station is in motion, or is otherwise subjected to significant and sudden changes in the background noise within the mobile station. The background noise within the mobile unit can also be affected by numerous other changes within the mobile station.  
           [0004]    In typical mobile transmitters which use voice activity detection internal to a noise suppression algorithm, an increase in background noise can be interpreted by the noise suppression algorithm as a voice signal from the user of the mobile transmitter. This condition is brought about due to the inter-dependency between the voice activity detection and the noise floor estimate computed by the noise suppression algorithm. One noise suppression technique, such as a stationary spectral check, has been used with some success in order to mitigate be effects of sudden increases in background noise. However, in practice, this solution has been shown to be inadequate in many cases due to the time required for the noise suppression algorithm to reduce the background noise to an acceptable level. In some cases, this time period can be 10-20 seconds in duration. In other cases, the system can experience a locked fault condition in which noise floor updates cease to occur. This results in the transmitter being placed in a condition where the listener is subjected to an unacceptable amount of noise for an extended period of time.  
           [0005]    Therefore, it is highly desirable for the noise suppression method and system to adapt to sudden increases in background noise through the use of a voice activity detector with reduced inter-dependency between voice activity detection and noise floor estimates. Such a system would provide a capability for lower noise transmissions while a mobile station is operating in the presence of widely varying background noise. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0006]    The invention is pointed out with particularity in the appended claims. However, a more complete understanding of the present invention may be derived by referring to the detailed description and claims when considered in connection with the figures, wherein like reference numbers refer to similar items throughout the figures, and:  
         [0007]    [0007]FIG. 1 is a block diagram of a transmitter which employs voice activity detection using and external voice activity detector in accordance with a preferred embodiment of the invention;  
         [0008]    [0008]FIG. 2 is a flowchart of a method for noise suppression using an external voice activity detector in accordance with a preferred embodiment of the invention; and  
         [0009]    [0009]FIG. 3 is a flowchart of a method used by an external voice activity detector to control the updating noise content estimate performed by a noise suppression algorithm in accordance with a preferred embodiment of the invention.  
     
    
     DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0010]    A method and system for improved noise suppression using an external voice activity detector provides a capability to conduct voice communications in the presence of widely varying background noise. The method and system correct a shortcoming in many noise suppression techniques by providing faster noise updates which minimizes the noise heard by the listening station. Additionally, the locked fault condition where noise updates cease to occur is avoided. These result in a hands-free communications system which does not subject a far-end listener to a noise burst when an increase in background noise occurs.  
         [0011]    [0011]FIG. 1 is a block diagram of a transmitter which employs voice activity detection using and external voice activity detector in accordance with a preferred embodiment of the invention. In FIG. 1, microphone  50  receives acoustic energy and converts this energy to an electrical signal. Microphone  50  can be any type of the microphone or other transducer which converts mechanical or acoustic vibrations into electrical signals. Microphone  50  is coupled to analog to digital converter  75  which converts the incoming analog electrical signal to a digital representation. Analog to digital converter  75  can be any general purpose type of converter which preferably possesses sufficient sampling rate and dynamic range in order to produce accurate digital representations of the incoming analog voice signals from microphone  50 .  
         [0012]    The output of analog to digital converter  75  is input to noise suppressor  100  which includes preprocessor  110 , voice activity detector  120 , noise content estimator  130 , and channel gain calculation element  140 . An output of analog to digital converter  75  is additionally coupled to external voice activity detector  150 . In a preferred embodiment, noise suppressor  100  is illustrative of a variety of noise suppressors suitable for use in conjunction with the present invention. Additionally, the functions of noise suppressor  100  may be performed entirely as one or more software processing elements, or may be performed in hardware where individual functions are performed by discrete and dedicated processing elements.  
         [0013]    In FIG. 1, preprocessor  110  receives the digital representations of voice signals from analog to digital converter  75 . In a preferred embodiment, preprocessor  110  performs any required spectral conditioning functions in which certain spectral bands, preferably those which contain primarily voice, are emphasized, while other spectral bands, such as those which contain primarily noise, are de-emphasized. Additionally, preprocessor  110  may also perform conversion from a time domain signal to a frequency domain signal in order to allow the remaining portions of noise suppressor  100  to perform additional manipulations on the digital representations of the voice signals.  
         [0014]    The output of preprocessor  110  is coupled to voice activity detector  120 , and noise content estimator  130 . In a preferred embodiment, voice activity detector  120  performs voice detection based on the noise floor and channel energy statistics of the digital representations of the voice signals from preprocessor  110 . Noise content estimator  130  measures the background noise present in the digital representations of the voice signals from preprocessor  110 .  
         [0015]    The output of voice activity detector  120  and noise content estimator  130  are then coupled to channel gain calculation element  140 . In a preferred embodiment, channel gain calculation element  140  segments the digital representations of the voice signals into a group of frequency bins. By way of the segmentation of voice signals into frequency bins, channel and gain calculations can be performed on specific frequency bands which primarily contain voice information. Additionally, those frequency bands which primarily contain noise information can be attenuated.  
         [0016]    As shown in FIG. 1, noise content estimator  130  and voice activity detector  120  are coupled in order to perform a voice activity decision which is based on the noise content of the digital representations of the voice signal from preprocessor  110 . Thus, voice activity detector  120  determines voice activity by way of receiving an input from noise content estimator  130 .  
         [0017]    In FIG. 1, external voice activity detector  150  performs a separate voice activity determination in order to assist noise content estimator  130  in determining the noise content of the digital representation of the voice signals from preprocessor  110 . In a preferred embodiment, external voice activity detector determines voice activity without an input from noise content estimator  130 . Importantly, the external noise floor estimate is not tied Through removing the dependency of noise floor determination on voice activity detection decisions, a more reliable voice activity detection mechanism can be provided for use in environments where background noise changes rapidly.  
         [0018]    External voice activity detector  150 , accepts inputs of digital representations of voice signals from analog to digital converter  75 . These inputs are coupled to signal power estimator  154 , and noise floor estimator  156 . Signal power estimator  154  performs computations in order to determine the signal power present in the input signal. Noise floor estimator  156  performs calculations on the input signal in order to ascertain the noise floor of the signal input.  
         [0019]    Outputs from signal power estimator  154  and noise floor estimator  156  are coupled to voice activity processor  158  which compares the levels of signal power and noise floor in order to determine whether an update of noise content estimator  130 , should be performed. The method used by signal power estimator  154 , noise of floor estimator  156 , voice activity processor  158  is discussed further in reference to FIG. 3. The output of voice activity  158  is coupled to noise suppressor  100 . In a preferred embodiment, this output consists of an indicator which can force noise content estimator  130  to perform a noise estimate of the digital representations of the voice signal from preprocessor  110 .  
         [0020]    [0020]FIG. 2 is a flow chart of a method performed by an external voice activity detector in accordance with a preferred embodiment of the invention. External voice activity detector  150  of FIG. 1 is suitable for performing the method. The method of FIG. 2 begins with the voice activity detector computing a background noise floor estimate. By way of example, and not by way of limitation, this estimate is based upon a slow rise/fast-fall technique designed to track changes in the noise floor of a particular signal. Preferably, the technique does not require an assumption as to whether the incoming digital representation of a voice signal is either voice or noise. As each sample, denoted by y(n) is processed , an estimate of the current signal power is desirably updated in step  220  by way of an integration function such as the leaky integrator shown in the equation below.  
           P   y ( n )=(1−) y   2 ( n )+ P   y ( n− 1), where 0.9875  
         [0021]    In step  230 , the current signal power estimate is compared to the noise floor estimate. If the signal power estimate exceeds the noise floor estimate, which can indicate a decrease in the noise level of the incoming voice signal, the updated noise floor is set equal to the signal power estimate in step  245 . This produces the desired “fast fall” in the noise floor. If the signal power estimate exceeds the noise floor estimates, symbolizing a increase in noise level, a slope factor is applied to the noise floor estimate (in step  240 ) to cause a slow rise rambling of the current noise floor estimates at a rate of decibels per second. The algorithm for steps  230 ,  240  and  245  can be expressed as:  
         If ( P   y ( n )&lt; NF   y ( n− 1)) then  NF   y ( n )= P   y ( n )  
         else  
           NF   y ( n )=( NF   y ( n− 1)) where β≈2 to 8 dB per second endif.  
         [0022]    In step  250 , a voice activity factor, ,is applied to the updated noise floor estimates to create a voice activity threshold estimate, ( (NF y (n)). The method then continues in step  260  where the signal power estimate is compared with the voice activity threshold estimates from step  250 . Step  260  is the primary decision as to whether or not to force the noise suppression technique to update the noise content estimate of the digital representations of the voice signal, although typical implementation would preferably also employ well-known techniques such as hangover periods and hysteresis.  
         [0023]    If the signal power estimate exceeds the voice activity threshold estimate, then the external voice activity detector allows the noise suppression technique to update the noise content estimate, as in step  270 . In the event that the signal power estimate does not exceed the voice activity threshold estimate, step  262  is executed in which a determination is made as to whether an upper limit of a silence counter has been reached. If the upper limit of the silence counter has not been reached, step  263  is executed in which the counter is incremented, and the method returns to step  260 . A complete description of the purpose and preferred numerical values of the silence counter is described with reference to FIG. 3.  
         [0024]    If the decision of step  262  indicates that the upper limit of the silence counter has been reached, step  265  is executed in which the external voice activity sensor forces the noise suppression technique to update the noise content estimate. Step  280  is then executed where the silence counter is rest. After executing steps  265  through  280 , the method returns to step  210 , where the next frame of digital representations of voice signals is evaluated. The algorithm for steps  250 , through  280  can be expressed as:  
         If  P   y ( n )&gt;(( NF   y ( n ))  
         [0025]    then do not force update else force update, increment silence counter, and check threshold endif.  
         [0026]    [0026] 
         [0027]    [0027]FIG. 3 is a flow chart of a method used by an external voice activity detector to control the updating of a noise content estimate performed by a noise suppression algorithm in accordance with a preferred embodiment of the invention. The method begins in step  310  where an external voice activity detector, such as external voice activity detector  150  of FIG. 1, determines if voice activity is present. Step  310  represents the outcome of voice activity detection, such as that described in reference to FIG. 2, in which a noise content estimate is forced if the appropriate conditions are present. If step  310  determines that voice activity is not present, step  320  is executed where a counter is incremented. In step  330 , a check is performed to determine if the current value of the counter has reached an upper limit. In a preferred embodiment, the upper limit for the counter is set to equal  20 .  
         [0028]    If the upper limit of the counter has been reached, the external voice activity detector forces an update of the noise content of the incoming digital representations of a voice signal and the method returns to step  310 . If, however, step  330  determines that the upper limit has not been reached, the method executes step  350  where the external voice activity detector allows the noise suppression algorithm to determine if an update in the noise content of an incoming digital representation of a voice signal is required. The method then returns to step  310 . If the external voice activity detector determines that a voice signal is present, as in step  310 , a counter is reset in step  315  and the method returns to step  310 .  
         [0029]    Steps  320  through  340  allow a noise update only after a relatively long “hangover” period has occurred. The use of a hangover period restricts the noise suppression algorithm to performing a noise content estimate only after a hands-free subscriber has stopped talking. Thus, noise content estimates are not performed during the voice the pauses which occur during normal speech. Additionally, the use of a counter to limit the time between forced updates of the noise content of the voice signal limits the length of the hangover period. By limiting the length of the hangover period, the locked fault condition in which the noise suppression algorithm ceases to update the noise content estimate can be avoided. Thus preventing the far-end listener from be subjected to high levels of noise.  
         [0030]    A method and system for improved noise suppression using an external voice activity detector provides a capability to conduct voice communications in the presence of widely varying background noise. The method and system correct a shortcoming present in many noise suppression techniques by forcing the noise suppression technique to perform noise content estimates on incoming digital representations of voice signals under certain conditions. This, in turn, minimizes the noise heard by the listening station. Additionally, the locked fault condition where noise updates cease to occur, is avoided. The method and system result in a hands-free communications system which does not subject a far-end listener to a noise burst when an increase in background noise occurs.  
         [0031]    Accordingly, it is intended by the appended claims to cover all modifications of the invention that fall within the true spirit and scope of the invention.