Patent Publication Number: US-10783899-B2

Title: Babble noise suppression

Description:
RELATED APPLICATIONS 
     This application is the U.S. National Stage of International Application No. PCT/US2016/062908, filed on Nov. 18, 2016, which designates the U.S., published in English, and claims the benefit of U.S. Provisional Application No. 62/291,791, filed on Feb. 5, 2016. The entire teachings of the above applications are incorporated herein by reference. 
    
    
     BACKGROUND 
     Speech-controlled applications and devices supporting human speech communication are becoming more and more popular. Human-computer interfaces based on speech recognition allow users to dictate text and control devices using spoken commands comprising speech utterances. A speech detector may be employed for speech recognition to determine a beginning and end of such speech utterances. 
     SUMMARY 
     According to one example embodiment, a system may be configured to perform noise suppression of an audio signal, the audio signal including foreground speech components and background noise. The system may comprise a soft speech detector configured to determine, dynamically, a speech detection result indicating a likelihood of a presence of the foreground speech components in the audio signal. The system may further comprise a noise suppressor. The noise suppressor may be communicatively coupled to the soft speech detector to receive the speech detection result determined and may be configured to compute, dynamically, spectral weighting coefficients based on the speech detection result determined. The noise suppressor may be further configured to apply the spectral weighting coefficients computed to the audio signal to suppress the background noise in a dynamic manner. 
     The noise suppressor may compute, dynamically, a dynamic noise overestimation factor based on the speech detection result determined. The spectral weighting coefficients may be computed based on the dynamic noise overestimation factor. The noise suppressor may determine periods of speech pauses and periods of speech activity in the audio signal as a function of the speech detection result determined. The noise suppressor may increase a value of the dynamic noise overestimation factor for the periods of speech pauses determined relative to the value of the dynamic noise overestimation factor for the periods of speech activity determined. Increasing the value of the dynamic noise overestimation factor may enable the spectral weighting coefficients computed to increase suppression of the background noise relative to an amount of suppression of the background noise for the periods of speech activity determined. 
     The system may further comprise a spectrum estimator configured to estimate a power spectrum of the audio signal based on a transformation of the audio signal from a time domain to a frequency domain. The soft speech detector may be further configured to determine the speech detection result as a function of a combination of feature values determined in the time domain, frequency domain, or a combination thereof. 
     The combination of feature values may include kurtosis and at least one other feature value. 
     The background noise may include stationary and non-stationary noise components. Changes in a power spectrum of the audio signal over a time interval may be less for the stationary noise components than for the non-stationary noise components. 
     The noise suppressor may be further configured to compute, dynamically, a dynamic noise floor, and selectively lower the dynamic noise floor based on frequencies corresponding to the non-stationary noise components. The spectral weighting coefficients may be computed further based on the dynamic noise floor computed and selectively lowered. 
     The noise suppressor may be further configured to identify one or more spectral weighting coefficients from the spectral weighting coefficients computed based on contextual information from neighboring spectral weighting coefficients. The noise suppressor may post-process the spectral weighting coefficients computed by setting first values computed for the one or more spectral weighting coefficients identified to second values. The second values may enable a stronger attenuation of the background noise than the first values. The applying may include applying the spectral weighting coefficients computed and post-processed. 
     The system may further comprise a pre-processing unit. The pre-processing unit may be configured to pre-process the audio signal to pre-emphasize spectral characteristics of the audio signal. The soft speech detector and the noise suppressor may be further configured to determine, dynamically, the speech detection result and compute, dynamically, the spectral weighting coefficients, respectively, for a given time interval of the pre-processed audio signal. The noise suppressor may apply the spectral weighting coefficients computed to the pre-processed audio signal in the given time interval. 
     The foreground speech components may correspond to speech from a user speaking into an audio receiving device. The background noise may include babble noise. The babble noise may include a composition of multiple background speech components from other speakers. 
     According to another example embodiment, a method may perform noise suppression of an audio signal. The audio signal may include foreground speech components and background noise. The method may determine, dynamically, a speech detection result indicating a likelihood of a presence of the foreground speech components in the audio signal. The method may compute, dynamically, spectral weighting coefficients based on the speech detection result determined and may apply the spectral weighting coefficients computed to the audio signal to suppress the background noise in a dynamic manner. 
     The method may compute, dynamically, a dynamic noise overestimation factor based on the speech detection result determined. The spectral weighting coefficients may be computed based on the dynamic noise overestimation factor. The method may determine periods of speech pauses and periods of speech activity in the audio signal as a function of the speech detection result determined. The method may increase a value of the dynamic noise overestimation factor for the periods of speech pauses determined relative to the value of the dynamic noise overestimation factor for the periods of speech activity determined. Increasing the value of the dynamic noise overestimation factor may enable the spectral weighting coefficients computed to increase suppression of the background noise relative to an amount of suppression of the background noise for the periods of speech activity determined. 
     The method may estimate a power spectrum of the audio signal based on a transformation of the audio signal from a time domain to a frequency domain. The speech detection result determined may be a function of a combination of feature values determined in the time domain, frequency domain, or a combination thereof. 
     The combination of feature values may include kurtosis and at least one other feature value. 
     The background noise may include stationary and non-stationary noise components. 
     Changes in a power spectrum of the audio signal over a time interval may be less for the stationary noise components than for the non-stationary noise components. 
     The method may compute, dynamically, a dynamic noise floor, and selectively lower the dynamic noise floor based on frequencies corresponding to the non-stationary noise components. Computing the spectral weighting coefficients may be further based on the dynamic noise floor computed and selectively lowered. 
     The method may identify one or more spectral weighting coefficients from the spectral weighting coefficients computed based on contextual information from neighboring spectral weighting coefficients. The method may post-process the spectral weighting coefficients computed by setting first values computed for the one or more spectral weighting coefficients identified to second values. The second values may enable a stronger attenuation of the background noise than the first values. The applying may include applying the spectral weighting coefficients computed and post-processed. 
     The method may pre-process the audio signal to pre-emphasize spectral characteristics of the audio signal. The speech detection result may indicate the likelihood of the presence of the foreground speech components in the pre-processed audio signal. The determining and the computing may be performed for a given time interval of the pre-processed audio signal. The applying may include applying the spectral weighting coefficients computed to the pre-processed audio signal in the given time interval. 
     The foreground speech components may correspond to speech from a user speaking into an audio receiving device. The background noise may include babble noise. The babble noise may include a composition of multiple background speech components from other speakers. 
     Yet another example embodiment may include a non-transitory computer-readable medium having stored thereon a sequence of instructions which, when loaded and executed by a processor, causes the processor to complete methods disclosed herein. 
     It should be understood that embodiments disclosed herein can be implemented in the form of a method, apparatus, system, or computer readable medium with program codes embodied thereon. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee. 
       The foregoing will be apparent from the following more particular description of example embodiments of the invention, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments of the present invention. 
         FIG. 1  is a block diagram of an example embodiment of a system configured to perform noise suppression of an audio signal. 
         FIG. 2  is a block diagram of another example embodiment of a system configured to perform noise suppression of an audio signal. 
         FIG. 3A  is a graph of an example embodiment of frequency components over time of a spectrum of an audio signal including a Texas Instruments/Massachusetts Institute of Technology (TIMIT) utterance. 
         FIG. 3B  is a graph of an example embodiment of simulated kurtosis feature values over time for the same TIMIT utterance used for generating the graph of  FIG. 3A . 
         FIG. 4  is a block diagram of an example embodiment of a graph with Receiver Operating Characteristic (ROC) curves. 
         FIG. 5A  is an example embodiment of a spectrogram for an unprocessed noisy input signal. 
         FIGS. 5B-D  are example embodiments of spectrograms showing improvements for babble noise suppression according to example embodiments disclosed herein. 
         FIG. 6A  is a graph including results of a subjective listening test. 
         FIG. 6B  is a graph with an objective measure. 
         FIG. 7  is a flow diagram of an example embodiment of a method of performing noise suppression of an audio signal. 
         FIG. 8  is a flow diagram of another example embodiment of a method of performing noise suppression of an audio signal. 
         FIG. 9  is a block diagram of an example internal structure of a computer optionally within an embodiment disclosed herein. 
     
    
    
     DETAILED DESCRIPTION 
     Mobile speech applications employed by devices, such as smartphones, may be utilized in crowded surroundings. As a user speaks to the device, background noise (also referred to interchangeably herein as “noise” or “environmental noise”) may be present. The background noise may include speech from one or more interfering speakers that may be talking in the background while the user speaks to the device. The speech from the one or more interfering speakers may be referred to interchangeably herein as “babble,” “babble noise,” “babble speech,” or “interfering speech” and may be present in a crowded environment in which multiple persons are speaking. The crowded environment may be a public space, such as a restaurant or café, or any other suitable space in which multiple persons are speaking. 
     A speech application that includes a speech processing method may be employed on a device that may be used by a user speaking to the device in the crowded environment, and, thus, the speech application may experience a crowded environment noise condition. For example, the speech application may receive as input an electronic representation of the user&#39;s voice that may be superposed with interfering voices. In contrast to other noise conditions, such as an automotive noise condition that may include engine or wiper noise, the crowded environment noise condition may include babble noise that contains portions of interfering speech from the interfering voices. The crowded environment noise condition that includes the babble noise may be referred to herein as a babble noise scenario and poses a challenge for speech processing methods. Assumptions, such as stationarity of the noise or a good Signal to Noise Ratio (SNR), may not be valid for babble noise scenarios. According to embodiments disclosed herein, other distinctive properties may be considered for distinguishing the babble noise from the user&#39;s speech. 
     Since the background noise contains speech portions, it is a particularly challenging scenario for many speech processing methods (Nitish Krishnamurthy and John H. L. Hansen, “Babble Noise: Modeling, Analysis, and Applications,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 7, pp. 1394-1407, September 2009), such as speech detection methods or noise reduction methods. Speech detection methods or speech detectors that distinguish between desired foreground speech (also referred to interchangeably herein as “foreground speech components,” “user speech,” or “desired speech”) and the background noise, may be triggered, falsely, by the interfering speech, that is, the babble noise, that may be present in the background noise. Therefore, an elaborated speech detector that maintains reliable results under such an adverse condition, that is, the presence of babble noise, may be useful. 
     The background noise may include stationary and non-stationary noise components. Changes in a power spectrum of the audio signal over a time interval may be less for the stationary noise components than for the non-stationary noise components. Standard noise reduction approaches primarily target on reducing stationary background noise components in an audio signal. Non-stationary components of babble noise are, therefore, not sufficiently suppressed. As a result, the non-stationary components may remain as annoying artifacts in the processed audio signal. Embodiments disclosed herein are motivated by a desire to deal with babble noise while not affecting the desired foreground speech, that is, the speech from the user speaking to the device. 
     Characteristics of babble noise are disclosed herein and distinctive features summarized that enable distinguishing of the desired foreground speech from the babble noise. In particular, according to embodiments disclosed herein, kurtosis of a signal is identified as a good measure to use to detect the presence of the desired foreground speech even in the presence of babble noise. It should be understood that detection of the presence may be in the form of a hard result, such as a boolean true/false type indicator, or in the form of a soft result, that is, a probability value that indicates the probability of the presence of the desired foreground speech. 
     According to embodiments disclosed herein, a babble noise suppression system is introduced that distinguishes between the desired foreground speech and the babble noise. In an example embodiment, an aggressiveness (i.e., an attenuation amount, strength of attenuation, or attenuation level) of the noise suppression may be controlled by a kurtosis-based speech detector. Strong attenuation may be applied during absence of speech whereas the aggressiveness may be reduced, dynamically, in response to speech being detected. In addition, according to embodiments disclosed herein, annoying fluctuations in the background noise may be reduced to achieve a more stationary background in a processed audio signal. 
     According to embodiments disclosed herein, strong suppression of the babble noise is desired from the babble noise suppression system. In addition, any remaining noise after processing the audio signal should be perceived as pleasant by human listeners. To evaluate improvements achieved by an example embodiment of a babble noise suppression system disclosed herein, results from a subjective listening test are presented, as disclosed further below in the Results section. Further, an acceptability of the remaining background noise after processing is assessed, as disclosed in the Results section. 
       FIG. 1  is a block diagram  100  of an example embodiment of a system  102  configured to perform noise suppression of an input audio signal  101 . The input audio signal  101  may include foreground speech components  103  and background noise  105 . The system  102  may comprise an input interface  117 , that may be a hardware input interface or any other suitable interface, configured to transform the input audio signal  101  into an electronic representation of the input audio signal  101 ′ for input to a soft speech detector  104 . The soft speech detector  104  may be configured to determine, dynamically, a speech detection result  112 . The speech detection result  112  may indicate a likelihood (i.e., a probability value) of a presence of the foreground speech components  103  in the input audio signal  101 . The system  102  may further comprise a noise suppressor  114  communicatively coupled to the soft speech detector  104  to receive the speech detection result  112  determined and the electronic representation of the input audio signal  101 ′. The noise suppressor  114  may be configured to compute, dynamically, spectral weighting coefficients  109  based on the speech detection result  112  determined and may apply the spectral weighting coefficients  109  computed to the electronic representation of the input audio signal  101 ′ to suppress the background noise  105  in a dynamic manner. 
     The system  102  may comprise a memory  111  and the noise suppressor  114  may be configured to store the spectral weighting coefficients  109  computed in the memory  111 . The noise suppressor  114  may be configured to retrieve the spectral weighting coefficients  109  computed from the memory  111  to apply the spectral weighting coefficients  109  computed to the electronic representation of the input audio signal  101 ′. 
     The soft speech detector  104  may be referred to as a “soft” speech detector because the soft speech detector  104  may determine the speech detection result  112  that may represent a likelihood (i.e., probability value) of a presence of the foreground speech components  103  as opposed to a “hard” result that represents a definitive true/false boolean type of result for indicating whether the foreground speech components  103  are present. As such, a “soft” speech detector may be a speech detector that produces a “soft” speech detection result that represents a likelihood (i.e., probability) of a presence of speech components in an audio signal. 
     The foreground speech components  103  may correspond to speech from a user&#39;s voice, such as speech from a voice of a user  107   a  that may be speaking to the system  102 . The background noise  105  may include the babble noise  113 . The babble noise  113  may include a composition of multiple background speech components from one or more other speakers, such as the user  107   b  and the user  107   c . The system  102  may be referred to interchangeably herein as a babble noise suppression system  102 . 
     The soft speech detector  104  may be further configured to determine the speech detection result  112  as a function of a combination of feature values determined in the time domain, frequency domain, or a combination thereof. The combination of feature values may include kurtosis and at least one other feature value as disclosed below with reference to  FIG. 2 . It should be understood that in the example embodiment of  FIG. 2  the at least one other feature value is a cepstral maximum feature value, however, any other suitable feature value that reflects the presence of speech may be employed for combination with the kurtosis feature value. For example, power, signal-to-noise power ratio, harmonicity, or pitch-based features may be employed, or any other suitable feature that reflects the presence of speech. 
     According to embodiments disclosed herein, the system  102  may employ the soft speech detector  104  to control, dynamically, an aggressiveness (i.e., an attenuation strength) of noise suppression of the electronic representation of the input audio signal  101 ′ by the noise suppressor  114 . As such, the babble noise suppression system  102  may produce an electronic representation of an output audio signal  115 ′ with the background noise  105  suppressed. The electronic representation of the output audio signal  115 ′ may be output as the output audio signal  115  by an output interface  119 , that may be a hardware interface configured to produce the output audio signal  115  (also referred to interchangeably herein as a processed audio signal  115 ) in an audible form. As disclosed further below in the Results section, any remaining noise in the processed audio signal  115  may be perceived by human listeners as more pleasant than the background noise  105  of the audio signal  101 . Further embodiments of the babble noise suppression system  102  are disclosed below with regard to  FIG. 2 . 
       FIG. 2  is a block diagram  200  of another example embodiment of a system  202  configured to perform noise suppression of an electronic representation of an input audio signal  201 ′. According to one embodiment, the electronic representation of the input audio signal  201 ′, that is x(n), where n is the sample index, may be an electronic representation of a pre-emphasized input audio signal. Alternatively, x(n) may not be pre-emphasized. The system  202  may also be referred to interchangeably herein as a babble noise suppression system  202 . The babble noise suppression system  202  may be separated into two main parts, a soft speech detector  204  and a noise suppressor  214 . The noise suppressor  214  may determine spectral weighting coefficients in order to suppress the babble noise, as disclosed further below in the Noise Suppression section. 
     The soft speech detector  204  may determine a speech detection result  212  that indicates a likelihood of presence of desired speech in an input audio signal, such as the likelihood of the presence of the foreground speech components  103  of the input audio signal  101  of  FIG. 1 , disclosed above. The soft speech detector  204  may determine the speech detection result  212  as a function of a combination of feature values determined in the time domain, frequency domain, or a combination thereof. As such, the speech detection result  212  may be referred to interchangeably herein as a combined speech detection result  212 . The combination of feature values may include kurtosis and at least one other feature value. 
     According to one embodiment, the soft speech detector  204  may include a kurtosis feature module  208 , a cepstral maximum feature module  210 , and a combiner  206 . The combiner  206  may be configured to combine a kurtosis feature  232 , produced by the kurtosis feature module  208 , with a cepstral maximum feature  234 , produced by the cepstral maximum feature module  210 , to produce the speech detection result  212 , as disclosed further below with regard to Equations 2-8. The speech detection result  212  may be employed to control the noise suppressor  214 . 
     For example, the noise suppressor  214  may be designed to attenuate the background noise, such as the background noise  105  of  FIG. 1 , more aggressively during speech pauses (not shown) of the electronic representation of the input audio signal  201 ′, and such speech pauses may be identified as a function of the speech detection result  212  produced by the soft speech detector  204 . According to embodiments disclosed herein, spectral weighting coefficients, such as the Wiener filter spectral weighting coefficients  241 , modified spectral weighting coefficients  243 , and final spectral weighting coefficients  245 , may be determined by the noise suppressor  214  to enable the babble noise suppression system  202  to apply stronger attenuation during the speech pauses based on overestimating the noise. 
     The noise suppressor  214  may include a noise shaper  220  and post-processor  222  to achieve a more stationary electronic representation of the output audio signal  215 ′ by applying noise shaping and post-processing, respectively, as disclosed further below in the Noise Suppression section. Embodiments of the soft speech detectors  104  and  204  of  FIGS. 1 and 2 , respectively, are disclosed below in the Speech Detection section. Embodiments of the noise suppressors  114  and  214  of  FIGS. 1 and 2 , respectively, are disclosed further below in the Noise Suppression section. 
     Speech Detection 
     Detecting presence of desired speech in a noisy signal has been subject to research for several decades (Simon Graf, Tobias Herbig, Markus Buck, and Gerhard Schmidt, “Features for voice activity detection: a comparative analysis,” EURASIP Journal on Advances in Signal Processing, vol. 2015, no. 91, November 2015). The overlapping characteristics of the desired speech and the babble noise complicate detection of the desired speech. Embodiments disclosed herein include features that enable for a robust distinction between the babble noise and the desired speech. 
     Clean speech (i.e., speech in the absence of noise) sample values exhibit a sparse characteristic. Values close to zero are dominating which implies a peak of the probability density function (PDF) around zero. No or only little reverberation can be expected. To find distinctive properties of babble noise, two effects appear relevant (Nitish Krishnamurthy and John H. L. Hansen, “Babble Noise: Modeling, Analysis, and Applications,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 7, pp. 1394-1407, September 2009):
         independent speech components from multiple distant talkers superpose, and   reverberation smears, temporally, the speech component of each distant talker.
 
Both effects result in a mixture of multiple samples of speech signals. The distribution of this mixture differs from the distribution of clean speech. Since multiple independent values are summed, the resulting distribution approaches a Gaussian probability distribution function (PDF). Embodiments disclosed herein exploit this property by evaluating the kurtosis.
       

     A. Kurtosis 
     The normalized kurtosis of a random variable χ 
                     kurt   ⁢     {   χ   }       =         E   ⁢     {     χ   4     }           (     E   ⁢     {     χ   2     }       )     2       -   3             (   1   )               
reflects the peakiness of the PDF (Guoping Li and Mark E. Lutman, “Sparseness and speech perception in noise,” in Proc. of Statistical and Perceptual Audition (SAPA), Pittsburgh Pa., USA, 2006). Here, zero-mean E{χ}=0 is assumed. Positive values of the kurtosis indicate a sharp peak of the distribution, whereas the kurtosis vanishes for Gaussian distributed variables. Clean speech is, therefore, characterized by high values of the kurtosis. The kurtosis decreases when multiple speech samples are mixed since the result approaches a Gaussian distributed random variable.
 
     This beneficial property of the kurtosis has been employed in different applications: The human speech recognition score in babble noise was predicted in (Guoping Li and Mark E. Lutman, “Sparseness and speech perception in noise,” in Proc. of Statistical and Perceptual Audition (SAPA), Pittsburgh Pa., USA, 2006). A high correlation between the value of kurtosis and the score was observed when increasing the number of talkers. Dereverberation of speech signals was performed in (Bradford W. Gillespie, Henrique S. Malvar, and Dinei A F Florêncio, “Speech dereverberation via maximum-kurtosis subband adaptive filtering,” in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Salt Lake City, Utah, USA, 2001) by maximizing the kurtosis value. In (Kohei Hayashida, Makoto Nakayama, Takanobu Nishiura, Yukihiko Yamashita, T. K. Horiuchi, and Toshihiko Kato, “Close/distant talker discrimination based on kurtosis of linear prediction residual signals,” in Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, 2014), the kurtosis was employed to distinguish between close and distant talkers. Kurtosis-based speech detection is discussed in multiple publications, such as (Elias Nemer, Rafik Goubran, and Samy Mahmoud, “Robust voice activity detection using higher-order statistics in the LPC residual domain,” IEEE Transactions on Speech and Audio Processing, vol. 9, no. 3, pp. 217-231, 2001), (David Cournapeau, Tatsuya Kawahara, Kenji Mase, and Tomoji Toriyama, “Voice activity detector based on enhanced cumulant of LPC residual and on-line EM algorithm,” in Proc. of INTERSPEECH, Pittsburgh, Pa., USA, 2006), and (David Cournapeau and Tatsuya Kawahara, “Evaluation of real-time voice activity detection based on high order statistics,” in Proc. of INTERSPEECH, Antwerp, Belgium, 2007). 
     Typically, the kurtosis is applied to the residual signal after linear predictive coding (LPC) analysis. In contrast, turning back to  FIG. 2 , embodiments disclosed herein, such as an embodiment of the kurtosis feature module  208 , apply the kurtosis feature directly to the electronic representation of the input audio signal  201 ′ x(n), that may be a pre-emphasized or non-pre-emphasized input audio signal, to detect presence of desired speech. 
     According to embodiments disclosed herein, a time-dependent estimate of the kurtosis feature may be determined, such as by the kurtosis feature module  208 , by: 
                       kurt   ⁡     (   ℓ   )       =           m   4     ⁡     (     ℓ   ·   R     )           (       m   2     ⁡     (     ℓ   ·   R     )       )     2       -   3       ,           (   2   )               
where downsampling by a factor R may be applied to align the feature with the l-th frame used for block processing. Moments of 2nd and 4th order, that is, m 2 (n) and m 4 (n), respectively, may be estimated by the kurtosis feature module  208  using recursive smoothing with a smoothing constant α k :
 
 m   2 ( n )=(1−α k )· x   2 ( n )+α k   ·m   2 ( n− 1).  (3)
 
     Analogously, m 4 (n) may be computed by smoothing x 4 (n). 
     The kurtosis may be smoothed again by the kurtosis feature module  208  using moving averaging 
                       kurt   _     ⁡     (   ℓ   )       =       1       L   p     +     L   f     +   1       ⁢       ∑       ℓ   ~     =     -     L   p           L   f       ⁢     kurt   ⁡     (     ℓ   +     ℓ   ~       )                   (   4   )               
with a look-ahead L f  to capture speech onsets. As such, the kurtosis feature module  208  may be configured to produce the kurtosis feature  232 , according to Equations 2, 3, and 4, disclosed above.
 
       FIG. 3A  is a graph  350  of an example embodiment of frequency components over time of a speech spectrum of an audio signal including a Texas Instruments/Massachusetts Institute of Technology (TIMIT) utterance. The graph  350  includes frequency components f[kHz]  352  for the audio signal over time t[s]  354 . In the example embodiment, the TIMIT utterance is: “She had your dark suit in greasy wash water all year.” The TIMIT utterance is indicative of the desired speech, such as the foreground speech components  103  of  FIG. 1 , disclosed above. In the example embodiment of  FIG. 3A , a sampling rate of 16 kHz and a downsampling factor of R=256 were utilized to produce the frequency components, f[kHz]  352  of  FIG. 3A . 
       FIG. 3B  is a graph  360  of an example embodiment of simulated feature values  356  over time t[s]  358  for the same TIMIT utterance used for generating the graph  350  of FIG.  3 A, with smoothing constants of α k =0.9986≙−100 dB/s and L p =L f =10 frames≙160 ms. The simulated feature values  356  include simulated kurtosis features values  362  that are based on the kurtosis feature alone and resulted in most of the desired speech  361  of  FIG. 3A  being detected as shown in  FIG. 3B . However, in a few cases, low frequency vowels, such as “/u/,” were missed. As such, embodiments disclosed herein may employ an additional feature that may reflect, explicitly, the voiced excitation of human speech, such as the cepstral maximum feature  234  produced by the cepstral maximum feature module  210  of  FIG. 2 . The simulated feature values  356  include simulated cepstral maximum feature values  364  as well as simulated combination feature values  366  that are based on a combination of the kurtosis and cepstral maximum features. As shown in  FIG. 3B , the simulated combination feature values  366  yielded the highest amount of the desired speech  361  of  FIG. 3A . As such, according to embodiments disclosed herein, a speech detection feature based on kurtosis is capable of distinguishing between desired speech and babble noise and further improvement for the distinguishing may be achieved by smoothing the kurtosis and/or combining the kurtosis or smoothed kurtosis with a complementing feature, such as the cepstral maximum feature, disclosed in more detail below, or any other suitable feature that reflects the presence of speech. 
     B. Cepstral Maximum 
     As disclosed above, voiced speech components by multiple speakers overlap in the babble noise. Compared to clean speech, less distinct harmonic structures are, therefore, observable in a mixture of clean speech and babble noise. To complement the kurtosis, embodiments disclosed herein may exploit a cepstrum that reflects voiced components. According to embodiments disclosed herein, cepstral coefficients cepst(τ,l) may be determined, where τ denotes the cepstral coefficient index and denotes the l-th frame. The cepstral coefficients cepst(τ,l) may be determined by the cepstral maximum feature module  210  of  FIG. 2 , for example, by applying an Inverse Discrete Fourier Transform (IDFT) to log(Φxx(k,l)/{circumflex over (Φ)}bb(k,l)), where k addresses the frequency bin of the l-th frame of the noisy speech spectrum Φ(k,l) and the estimated noise spectrum {circumflex over (Φ)} bb (k,l) of the noisy speech spectrum Φ xx (k,l). The noisy speech spectrum Φ xx (k,l) may be referred to interchangeably herein as the power spectral density. According to embodiments disclosed herein, the noisy speech spectrum Φ xx (k,l) may be estimated by smoothing, temporally, the magnitude squared Short-Term Fourier Transform STFT coefficients |X(k,l)| 2 . Normalization with the estimated noise spectrum {circumflex over (Φ)} bb (k,l) may emphasize the harmonic structure. 
     For example, the system  202  of  FIG. 2  may further comprise an STFT module  224  and a spectrum estimator  228 . The STFT module  224  may be configured to transform blocks of the electronic representation of the input audio signal  201 ′ x(n) in a time domain into a representation in a frequency domain, that is, the STFT coefficients X(k,l)  226 . 
     The STFT coefficients X(k,l)  226  may be input to the spectrum estimator  228  that may estimate the power spectral density Φ xx (k,l) by smoothing, temporally, the magnitude squared STFT coefficients  226 . As such, the spectrum estimator  228  may be configured to estimate a power spectrum of the electronic representation of the input audio signal  201 ′ x(n) based on a transformation of the electronic representation of the input audio signal  201 ′ from the time domain to the frequency domain. The STFT coefficients X(k,l)  226  determined by the STFT module  224  may be further input to a noise shaper  220  and multiplicative element  253 , as disclosed further below. 
     The power spectral density Φ xx (k,l) may be input as the power spectral density Φ xx (k, l)  230   a  to the cepstral maximum feature module  210  for determining the cepstral coefficients cepst(τ,l) (not shown) disclosed above. The power spectral density Φ xx (k,l) may be further input as the power spectral density Φ xx (k,l)  230   b  to a noise estimator  236  that may be configured to estimate the power spectral density of the noise, that is, the estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238 , based on the estimated power spectral density Φ xx (k,l)  230   b  of the noisy input speech signal as estimated by the spectrum estimator  228 . The power spectral density Φ xx (k,l)  230   b  may be further input to a Wiener filter  221 , as disclosed further below in the Noise Suppression section. It should be understood that the power spectral density Φ xx (k,l)  230   a  and the power spectral density Φ xx (k,l)  230   b  are the same power spectral density Φ xx (k,l) determined by the spectrum estimator  228 . 
     In  FIG. 2 , the estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238  is not shown as an input to the cepstral maximum feature module  210  for simplicity; however, the estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238  may be used by the cepstral maximum feature module  210  for determining the cepstral coefficients cepst(τ,l). According to embodiments disclosed herein, the cepstral maximum feature module  210  may be configured to accumulate neighboring bins of the cepstrum by: 
                       cepst   _     ⁡     (     τ   ,   ℓ     )       =       1     3   ·   4       ⁢       ∑       τ   ~     =     -   1       1     ⁢       ∑       ℓ   ~     =     -   3       0     ⁢     cepst   ⁡     (       τ   +     τ   ~       ,     ℓ   +     ℓ   ~         )                     (   5   )               
before the maximum in the relevant region between 60 Hz and 300 Hz is searched
 
                     voicing   ⁡     (   ℓ   )       =         max   τ     ⁢     (       cepst   _     ⁡     (     τ   ,   ℓ     )       )       -       voicing   offset     ⁢           .               (   6   )               
An offset may be removed by the cepstral maximum feature module  210  by subtracting an offset parameter voicing offset , according to Equation 6, disclosed above. Finally, to produce the cepstral maximum feature  234 , the cepstral maximum feature module  210  may be configured to smooth the maximum value, temporally, by:
 
 voicing ( l )=(1−α v )·voicing( l )+α v · voicing ( l− 1).  (7)
 
     An example embodiment of the cepstral maximum feature  234  of  FIG. 2  is shown as the simulated cepstral maximum feature values  364  of  FIG. 3B , disclosed above. In the example embodiment of  FIG. 3B , the STFT module  224  was employed with a Hann window of length 512 samples to determine the STFT coefficients X(k,l)  226  of  FIG. 2 . The estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238  was computed by smoothing Φ xx (k,l)  230   a=Φ   xx (k, l)  230   b  when the soft speech detector  204  produced a speech detection result  212  indicating a high likelihood of absence of speech, that is, a high probability that speech is not present. In the presence of speech, that is, when the soft speech detector  204  produced the speech detection result  212  indicating a high probability of the presence of speech, the noise estimate {circumflex over (Φ)} bb (k,l)  238  was not updated. The offset parameter, voicing offset , disclosed above, was set=⅙ to achieve positive values only for distinct harmonic structures. Smoothing was performed with α v =0.9≙−30 dB/s. 
     C. Combination and Detection Results 
     In the example embodiment of  FIG. 2 , the kurtosis feature  232  and the cepstral maximum feature  234  are combined by a combiner  206  to produce the speech detection result  212 . According to embodiments disclosed herein, the combiner  206  may combine the kurtosis feature  232  and the cepstral maximum feature  234  using a weighted sum:
 
comb( l )= w   k ·max(0, kurt ( l ))+ w   v ·max(0, voicing ( l ))  (8)
 
with w k =1 and w v =96. According to one embodiment, only positive values of the kurtosis feature  232  and cepstral maximum feature  234  may be considered to prevent from negative contributions.
 
Turning again to  FIG. 3B , the combination of both features is shown as the simulated combination feature values  366  that may represent the speech detection result  212  of  FIG. 2 .
 
     Analyses with artificially mixed data from TIMIT (John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallet, and Nancy L. Dahlgren, “DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus CD-ROM,” 1993) and NOISEX-92 (Andrew Varga and Herman J. M. Steeneken, “Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems,” Speech Communication, vol. 12, no. 3, pp. 247-251, 1993) database were performed. Signal-to-noise power ratios (SNRs) in a range between 0 and 10 dB were chosen. A good detection performance for the kurtosis feature is observable from the kurtosis feature Receiver Operating Characteristic (ROC) curve  404  shown in  FIG. 4 , disclosed below. For example, high detection rates of the detection rates P d    406  can be achieved with relatively low false-alarm rates of the false-alarm rates P fa    408  as shown. 
       FIG. 4  is a block diagram  400  of an example embodiment of a graph  402  with Receiver Operating Characteristic (ROC) curves. The cepstral maximum feature ROC curve  410  that may be computed according to Equation 7, disclosed above, shows a lower performance than the kurtosis feature ROC curve  404  that may be computed according to Equation 2, disclosed above, since the cepstrum only detects voiced speech portions. Furthermore, harmonic components in the babble noise sometimes falsely trigger the cepstral maximum feature. 
     According to embodiments disclosed herein, performance improvements can be achieved by combining both features, as shown by the combined feature ROC curve  416  and performance can be further improved by combining both features and applying a smoothing to the kurtosis, as shown by the combination feature with kurtosis smoothing ROC curve  420 . For example, smoothing the kurtosis, according to Equation 4, disclosed above, yields the smoothed kurtosis feature ROC curve  418 . Combining both features and applying a smoothing to the kurtosis according to Equation 8, disclosed above, yields the combination feature with kurtosis smoothing ROC curve  420  that results in the highest detection rates of the detection rates P d    406  with the lowest false-alarm rates of the false-alarm rates P fa    408 . For comparison, the operating point  414  of the established speech detector ETSI-AFE (ETSI, “ETSI standard 202 050 v1.1.5: Advanced front-end feature extraction algorithm,” 2007) is plotted. With the database, this detector is almost never triggered resulting in both P d ≈P fa ≈0 as shown by the data point  414 . This observation underlines the challenge of speech detection in babble noise. 
     Noise Suppression 
     Turning back to  FIG. 2 , according to embodiments disclosed herein, the noise suppressor  214  of  FIG. 2  may perform noise suppression and the noise suppression may include a strong attenuation that is applied during speech pauses by overestimating the noise, dynamically. The noise suppressor  214  may include an overestimator  250  that is configured to produce an overestimation factor β oe (l)  216  to control the aggressiveness of noise suppression. As such, the noise suppressor  214  may be configured to compute, dynamically, the dynamic noise overestimation factor β oe (l)  216  based on the speech detection result  212  determined. 
     For example, the noise suppressor  214  may be further configured to determine periods of speech pauses and periods of speech activity in the electronic representation of the input audio signal  201 ′ as a function of the speech detection result  212  determined. The overestimation factor β oe (l)  216  may be computed by the overestimator  250  based on the combined speech detection result  212  according to Equation 11, disclosed further below. 
     As disclosed above, the noise suppressor  214  may include the wiener filter  221 . The Wiener filter  221  may be configured to produce spectral weighting coefficients H wf (k,l)  241  according to: 
                         H   WF     ⁡     (     k   ,   ℓ     )       =     1   -           β   oe     ⁡     (   ℓ   )       ·         Φ   ^     bb     ⁡     (     k   ,   ℓ     )             Φ   xx     ⁡     (     k   ,   ℓ     )             ,           (   9   )               
where the power spectral density Φ xx (k,l)  230   b  from the spectrum estimator  228  is represented in the denominator. The noise suppressor  214  may include a first multiplicative element  251  configured to apply the overestimation factor β oe (l)  216 , that may be a scalar, to the estimated power spectral density (PSD) of the noise, that is the estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238 , to produce the numerator of Equation 9, disclosed above, that is, the overestimated power spectral density of the noise  239  of  FIG. 2 . As such, the overestimation factor β oe (l)  216  may be computed by the overestimator  250  based on the combined speech detection result  212  and applied to the estimated noise power spectral density {circumflex over (Φ)} bb (k,l)  238  by employing the first multiplicative element  251 . In Equation 9, disclosed above, all variables may be scalars; however, the overestimation factor β oe (l) is not frequency-dependent as it depends only on T. In contrast, the power spectral density (PSD) Φ xx (k,l) depends on both frequency (k) and frame (l). As such, for a frame l, the same overestimation factor β oe (l) is, therefore, applied to all frequency bins (k) of the PSD Φ xx (k,l).
 
     The spectral weighting coefficients H wf (k,l)  241  may be computed according to the Wiener filter (Equation 9) based on the estimated power spectral density of the noisy speech signal Φ xx (k,l)  230   b  and the overestimated power spectral density of the noise  239 . According to embodiments disclosed herein, increasing the value of the dynamic noise overestimation factor β oe (l)  216  enables the spectral weighting coefficients computed, that is H wf (k,l)  241 , to increase suppression of the background noise relative to an amount of suppression of the background noise for the periods of speech activity determined. Without overestimation, (β oe (l)=1), the Wiener filter  221  corresponds to a classical Wiener characteristic. According to embodiments disclosed herein, the spectral weighting coefficients H wf (k,l)  241  may be input to the noise shaper  220 , disclosed above. The noise shaper  220  may apply a dynamic floor to the spectral weighting coefficients H wf (k,l)  241  that reduces the non-stationary noise in the noisy speech signal. 
     A dynamic floor shapes the residual noise (Vasudev Kandade Rajan, Christin Baasch, Mohamed Krini, and Gerhard Schmidt, “Improvement in Listener Comfort Through Noise Shaping Using a Modified Wiener Filter Approach,” in  Proc. of  11 . ITG  Symposium on Speech Communication, Erlangen, Germany, 2014)
 
 H   NS ( k,l )=max( H   floor ( k,l ), H   WF ( k,l ))  (10)
 
to achieve a more stationary output signal. Classical noise suppression methods employ a fixed floor H floor (k,l)=H floor,fixed  instead.
 
     According to embodiments disclosed herein, the overestimation factor β oe (l)  216  may be computed based on the speech detection result  212 , that is, the combined speech detection feature comb(l), according to: 
                       β   oe     ⁡     (   ℓ   )       =     min   ⁡     (       β   max     ,       1       comb   ⁡     (   ℓ   )       +   ϵ       +   1       )               (   11   )               
and applied to the estimated noise spectrum {circumflex over (Φ)} bb (k,l)  238  according to Equation 9, disclosed above. During speech pauses indicated by the combined speech detection result  212  (e.g., comb (l)≈0), high noise overestimation may be applied with a maximal factor β max =21. High values of the feature that indicates presence of speech results in a reduced overestimation factor β oe (l)≈1. This kurtosis-based control prevents speech distortions caused by too aggressive attenuation during presence of speech.
 
     As such, according to Equations 9 and 11, disclosed above, speech detection features may be employed to control aggressiveness of noise suppression and protect the desired speech by reducing, dynamically, the aggressiveness with more aggressive attenuation applied to the non-stationary noise components. Further, according to Equations 10 and 12, disclosed above, combination with reduction of non-stationary components by selectively lowering the maximal attenuation H floor (k,l) yields a more stationary output in addition to the more aggressive attenuation. 
       FIG. 5A  is an example embodiment of a spectrogram  502   a  for an unprocessed noisy input signal.  FIG. 5B ,  FIG. 5C , and  FIG. 5D  are example embodiments of spectrograms  502   b ,  502   c , and  502   d , respectively, showing improvements for babble noise suppression of the unprocessed noisy input signal according to example embodiments disclosed herein. 
       FIG. 5B  is an example embodiment of a spectrogram  502   b  showing improvement to the babble noise suppression of  FIG. 5A  by processing the unprocessed noisy input signal of  FIG. 5A  with a Wiener filter without overestimation and with a fixed floor. The spectrogram  502   b  may show that stationary noise is reduced; however, the spectrogram  502   b  shows strong non-stationary artifacts, that is, non-stationary components remain. These artifacts are reduced by the dynamic overestimation as shown in the spectrogram  502   c  of  FIG. 5C , disclosed below. 
       FIG. 5C  is an example embodiment of a spectrogram  502   c  showing further improvement of the babble noise suppression that is achieved by processing the unprocessed noisy input signal of  FIG. 5A  with a Wiener filter as in  FIG. 5B  and with dynamic noise overestimation, as disclosed above.  FIG. 5D  shows further improvement to the quality of the unprocessed noisy input signal of  FIG. 5A , as disclosed below. 
     Remaining non-stationary components can be further reduced by selectively lowering the noise floor 
                       H   floor     ⁡     (     k   ,   ℓ     )       =       H     floor   ,   fixed       ·     min   ⁡     (     1   ,       (         X   desired     ⁡     (     k   ,   ℓ     )              X   ⁡     (     k   ,   ℓ     )              )     c       )                 (   12   )               
for the corresponding frequencies (Vasudev Kandade Rajan, Christin Baasch, Mohamed Krini, and Gerhard Schmidt, “Improvement in Listener Comfort Through Noise Shaping Using a Modified Wiener Filter Approach,” in  Proc. of  11 . ITG  Symposium on Speech Communication, Erlangen, Germany, 2014). For this noise shaping, the ratio between a desired magnitude X desired (k,l) and the current magnitudes of the noisy STFT bins |X(k,l)| is determined. Attenuating by this factor results in a more stationary background noise. The stationarity of the result can be controlled by adjusting the exponential term c. For c=0, no noise shaping is applied, whereas for c=1 the non-stationary characteristic is completely removed. As a trade-off, embodiments disclosed herein may choose c=0.5 to reduce the non-stationary components but preserve a naturally sounding background noise. According to embodiments disclosed herein, the desired shape may be computed based on the estimated noise spectrum averaged over time:
 
 X   desired ( k,l )=√{square root over ({circumflex over (Φ)} bb,average ( k,l ) + )}
 
     According to embodiments disclosed herein, the noise suppressor  214  may be further configured to compute, dynamically, a dynamic noise floor, and selectively lower the dynamic noise floor based on frequencies corresponding to the non-stationary noise components. The noise shaper  220  may apply the dynamic noise floor to the spectral weighting coefficients H wf (k,l)  241  that may reduce the non-stationary noise. The spectral weighting coefficients may be computed further based on the dynamic noise floor computed and selectively lowered to produce the modified spectral weighting coefficients after noise shaping  243  that are input to the post-processor  222 . 
     Alternatively, residual non-stationary noise components may be identified relying on contextual information from neighboring coefficients. Spectral weighting coefficients corresponding to the residual non-stationary noise components may be set to a lower value, such as a fixed noise floor or a dynamic noise floor. 
     The post-processor  222  may modify the spectral weighting coefficients after noise shaping  243  according to Equation 13, disclosed below. According to embodiments disclosed herein, sporadically occurring musical tones may be finally removed by the post-processor  222 . According to Equation 13, disclosed below, the post-processor  222  may modify the spectral weighting coefficients after noise shaping  243  to produce the final spectral weighting coefficients H(k,l)  245 . Spectral weighting coefficients H(k,l)  245  that exceed the fixed noise floor may be set to the dynamic floor 
                     H   ⁡     (     k   ,   ℓ     )       =     {             H   floor     ⁡     (     k   ,   ℓ     )                     if   ⁢           ⁢       H   NS     ⁡     (     k   ,   ℓ     )         &gt;       H     floor   ,   fixed       ⁢   Λ   ⁢           ⁢       n   -     ⁡     (     k   ,   ℓ     )         &gt;               1.5   ·       n   +     ⁡     (     k   ,   ℓ     )                           H   NS     ⁡     (     k   ,   ℓ     )           else                   (   13   )               
in an event a majority of coefficients in neighboring frequencies attenuates the spectrum to the dynamic floor. For example, a majority of neighboring coefficients has to attenuate stronger than the fixed floor to overrule the original weighting coefficient, that is, a given one of the modified spectral weighting coefficients  243 . For this, a first number n − (k,l) of coefficients next to k that attenuate stronger than the fixed floor may be compared to a second number of coefficients n + (k,l) that exceed the fixed floor. As such, the noise suppressor  214  may be further configured to identify one or more spectral weighting coefficients from the spectral weighting coefficients computed based on contextual information from neighboring spectral weighting coefficients. Neighboring spectral coefficients may be some spectral coefficients in frequency bins k or frames l that are close to a current spectral coefficient, such as in a given range of bins or frames from an associated bin or frame of the current spectral coefficient. The noise suppressor  214  may be further configured to post-process the spectral weighting coefficients computed by setting first values computed for the one or more spectral weighting coefficients identified to second values, the second values enabling a stronger attenuation of the background noise than the first values.
 
     As such, post-processing of the modified spectral weighting coefficients  243  may employ contextual information from neighboring frequency bins to produce the final spectral weighting coefficients H(k,l)  245  that may be considered as corrected spectral weighting coefficients. The final spectral weighting coefficients H(k,l)  245  may include spectral weighting coefficients associated with a particular kth bin and fth frame that may be set to the maximal attenuation when the majority of neighboring bins are set to the maximal attenuation. 
     The system  202  may further include a second multiplicative element  253  that applies the final spectral weighting coefficients H(k,l)  245  after noise shaping by the noise shaper  220  and post-processing by the post-processor  222  to the noisy STFT coefficients X(k,l)  226  and generates the enhanced STFT coefficients Y(k,l)  247 . The second multiplicative element  253  may perform an element-wise multiplication of two vectors that represent the final spectral weighting coefficients H(k,l)  245  and the noisy STFT coefficients X(k,l)  226  to generate the enhanced STFT coefficients Y(k,l)  247 . The system  202  may further include the overlap add module  249  that may be configured to convert (i.e., transform) the enhanced STFT coefficients Y(k,l)  247  into the time-domain electronic representation of the output audio signal  215 ′. 
       FIG. 5D  is an example embodiment of a spectrogram  502   d  showing further improvement of the babble noise suppression that is achieved by processing the unprocessed noisy input signal of  FIG. 5A  with a Wiener filter with dynamic noise overestimation as in  FIG. 5C , and by applying the noise shaping and post-processing disclosed above. 
     The spectrogram  502   d  includes all of the processing features of a babble noise suppression system according to embodiments disclosed herein. According to embodiments disclosed herein, in the presence of the desired speech (i.e., 1.5 s-4 s in the example embodiment), the babble noise suppression acts less aggressively to prevent from speech distortions. A more stationary background is achieved which is expected to be more comfortable for human listeners. In order to evaluate the improvements that are achieved by the babble noise suppression system according to embodiments disclosed herein, a subjective listening test was conducted. In the following, the test results are summarized. 
     Results 
     To evaluate the methods disclosed herein, a subjective listening test and objective measure were employed. 
     To evaluate the quality of the remaining noise as perceived by human listeners, a subjective listening test was conducted. In a multi-stimulus test, similar to MUSHRA (ITU, “Recommendation ITU-R B S.1534-3: Method for the subjective assessment of intermediate quality level of audio systems,” 2015), 21 test subjects rated an acceptability of background noise for four processing variants of a signal. The subjects were asked to decide whether they perceived the noise in the variants as more or less pleasant than the noise in a reference. 
       FIG. 6A  is a graph  602  including results of the subjective listening test. In the subjective listening test, a noisy speech signal was presented as the reference that was repeated as a hidden reference shown in (a)  606 . In  FIG. 6A , results for variants of the signal that were presented are shown. The variants of the signal included: (i) the signal processed with a standard Wiener filter (b)  608 , (ii) the signal processed a Wiener filter with dynamic noise overestimation (c)  610 , and (iii) the signal processed with the Wiener filter with dynamic noise overestimation, noise shaping, and post-processing (d)  613 . The test was repeated for 10 different signals, including artificially mixed signals, as well as real recordings. The acceptability of the remaining background noise was rated on a scale from −10 (less pleasant) to 10 (more pleasant) as shown by the rating  614  that is a rating relative to the reference (a)  606 . 
     The median, as well as the 25% and 75% quantiles over all answers are depicted in the boxplots  609 ,  611 , and  613  for each signal variant (b)  608 , (c)  610 , and (d)  612 , respectively. Almost all subjects correctly identified the hidden reference  606  ( a ) and rated the signal as “equal” (zero). Wiener filtering  608  ( b ) already increased the acceptability of the background noise for most subjects; however, some subjects preferred the unprocessed signals. Significant improvements were achieved by dynamic noise overestimation  610  ( c ). Noise shaping and post-processing  612  ( d ) slightly increased the acceptability. 
     According to embodiments disclosed herein, the noise suppression may be designed to act less aggressively in presence of speech. However, small speech distortions may be unavoidable when the signal is processed. In order to evaluate the speech distortions that are introduced by the system, an objective measure may be employed. For artificially mixed signals, the distortions between the clean speech signal and the processed speech component may be determined. 
       FIG. 6B  is a graph  604  with such an objective measure. The graph  604  shows an example embodiment of distortion-to-speech power ratios  616  for different signal processing variants (b)  618 , (c)  620 , and (d)  622 , that correspond to the signal processing variants (b)  608 , (c)  610 , and (d)  612  of the graph  602  of  FIG. 6A , respectively. Distortion-to-speech power ratio results  626 ,  628 , and  630  are shown for each signal variant (b)  618 , (c)  620 , and (d)  622 , respectively. A worst case reference (e)  624  is also shown that depicts the distortion-to-speech power ratio results  632  for a fixed noise overestimation β oe (l)=β max . 
     As shown in the graph  604  of  FIG. 6B , the Wiener filter without noise overestimation  618  introduces small distortions in the range of 23 dB. Only 2 dB of additional speech distortions are introduced by applying the dynamic noise overestimation with noise shaping and post-processing according to embodiments disclosed herein. These processing features, however, significantly improved the acceptability of the background noise, as confirmed by the subjective listening tests, disclosed above. For comparison, a fixed overestimation by β max =21 introduces high speech distortions in the range of −5 dB. 
     According to embodiments disclosed herein, a babble noise suppression system is introduced that may include a soft speech detector that may be employed to distinguish between babble noise and desired speech. Simulation results disclosed herein show that a kurtosis measure achieves good detection results and that further improvements may be achieved by smoothing the kurtosis and combining the smoothed kurtosis with at least one other feature. 
     According to embodiments disclosed herein, noise suppression may be controlled in a system based on a combined speech detection result. Noise may be suppressed more aggressively when no speech is detected. Noise shaping may be applied to achieve a more stationary background in the output signal. In addition, remaining musical tones may be reduced by modifying the spectral weighting coefficients. 
     Subjective listening tests confirmed that a system, according to embodiments disclosed herein, reduces the babble noise, effectively. The background noise in the processed signals was mostly perceived as more pleasant compared to an unprocessed reference. Further, the system introduces only little speech distortions as verified by an objective measure, as disclosed above. 
     Turning back to  FIGS. 1 and 2 , the systems  102  and  202  may further comprise a pre-processing unit (not shown). The pre-processing unit may be configured to pre-process the electronic representation of the input audio signal  101 ′ or  201 ′ to pre-emphasize spectral characteristics of the electronic representation of the input audio signal  101 ′ or  201 ′. The soft speech detector  104  or  204  and the noise suppressor  114  or  214 , respectively, may be further configured to determine and compute, respectively, for a given time interval of the pre-processed electronic representation of the input audio signal  101 ′ or  201 ′. The noise suppressor  114  or  214  may be further configured to apply the spectral weighting coefficients computed to the pre-processed audio signal in the given time interval. 
       FIG. 7  is a flow diagram  700  of an example embodiment of a method of performing noise suppression of an audio signal. The audio signal may include foreground speech components and background noise, such as the foreground speech components  103  and background noise  105  of  FIG. 1 , disclosed above. The method may begin ( 702 ) and determine, dynamically, a speech detection result indicating a likelihood of a presence of the foreground speech components in the audio signal ( 704 ). The method may compute, dynamically, spectral weighting coefficients based on the speech detection result determined ( 706 ) and apply the spectral weighting coefficients computed to the audio signal to suppress the background noise in a dynamic manner ( 708 ). The method thereafter ends ( 710 ), in the example embodiment. 
       FIG. 8  is a flow diagram  800  of another example embodiment of a method of performing noise suppression of an audio signal. The method may begin ( 802 ) and pre-process the input signal ( 804 ). The method may estimate the spectrum ( 806 ) and compute speech detection features ( 808 ). According to embodiments disclosed herein, speech detection features may be employed to control aggressiveness, that is, a strength of attenuation, of noise suppression. Protection of desired speech may be achieved by reducing the aggressiveness. More aggressive attenuation of the non-stationary noise components may be applied via the overestimation factor of Equation 11 as applied in Equation 9, as disclosed above. 
     According to embodiments disclosed herein, a speech detection feature based on kurtosis may be used to distinguish between desired speech and babble noise. Further, embodiments disclosed herein may provide further improvement by smoothing and/or combining the kurtosis feature with a complementing feature, such as, the cepstral maximum feature, or any other suitable feature. The method may compute a dynamic noise overestimation factor ( 810 ) and determine spectral weighting coefficients ( 812 ). The method may determine a dynamic maximum attenuation and apply post-processing ( 814 ). 
     Combination with reduction of non-stationary components by selectively lowering the maximal attenuation may achieve a more stationary output in addition to the more aggressive attenuation as disclosed above, with reference to Equations 10 and 12. Post-processing of spectral weighting coefficients may employ contextual information from neighboring frequency bins to correct spectral weighting coefficients, as disclosed above. Embodiments disclosed herein may set a frequency bin to the maximal attenuation in an event the majority of neighboring bins is set to the maximal attenuation. The method may apply the spectral weighting coefficients ( 816 ) and the method thereafter ends ( 818 ) in the example embodiment. 
     As disclosed above, babble noise may be a severe problem in speech enhancement applications. This type of noise may include a composition of multiple background speech components and exhibit properties similar to the desired foreground speech. Embodiments disclosed herein improve suppression of non-stationary noise components, such as babble noise, whereas other noise reduction approaches primarily suppress the stationary background noise. 
     According to embodiments disclosed herein, a noise suppression system is introduced for a more aggressive attenuation of babble noise. The noise suppression system disclosed herein includes a speech detector for foreground speech which is robust against background speech. Embodiments disclosed herein distinguish between desired speech and interfering babble noise and introduce a babble noise suppression system that provides effective suppression of babble noise by employing speech detection information to control the noise suppression. 
     Since mobile devices, such as smartphones, are employed even in crowded environments, a strong need may be present for embodiments disclosed herein that may reduce babble noise in a cost effective manner. Automatic speech recognition can benefit from the improved speech enhancement or from meta-information on the presence of speech, such as the combined speech detection result. 
     According to some embodiments, the babble noise suppression described herein may comprise four stages:
         Feature extraction and combination to distinguish between desired speech and interfering babble noise. As disclosed above, in order to detect desired foreground speech in the presence of babble noise, two features may be evaluated. Kurtosis reflects the sparseness of foreground speech by considering the distribution of sample values. The distribution of foreground speech is sparser than the distribution of babble noise in the background. Higher values of kurtosis, therefore, indicate the presence of desired speech. As disclosed above, the kurtosis feature may be applied directly to a pre-emphasized noisy input signal. In addition to kurtosis, another feature, such as the cepstral maximum, may be evaluated, for example, to capture harmonic speech components. According to embodiments disclosed herein, the combination of both features allows for a more accurate detection of desired speech compared to the single features.   Noise overestimation for stronger attenuation of noise in speech pauses. According to embodiments disclosed herein, using the combined value from both features, noise overestimation may be controlled. For example, during speech pauses, the noise spectrum may be overestimated resulting in a stronger attenuation of noise. The maximum attenuation, however, may be limited by a floor.   Noise floor modifications for more aggressive attenuation of non-stationary noise components. A fixed floor may result in the same attenuation for stationary and non-stationary noise components. As such, the non-stationary components still stick out of the stationary background in this case. According to embodiments disclosed herein, stationarization of non-stationary components may be achieved by applying a more aggressive attenuation to non-stationary components. For this, the noise floor may be selectively lowered for frequency bins that contain non-stationary interferences, as disclosed above.   Post processing of the spectral weighting coefficients to reduce the amount of noise in the result. According to embodiments disclosed herein, post processing may be applied to the spectral weighting coefficients that were determined with noise overestimation and noise floor modification. Open bins that are neighbored by attenuating bins may be set to the noise floor to reduce remaining musical noise components. The decision as to which bins are affected by this post processing may rely on a local majority vote, as disclosed above.       

       FIG. 9  is a block diagram of an example of the internal structure of a computer  900  in which various embodiments of the present disclosure may be implemented. The computer  900  contains a system bus  902 , where a bus is a set of hardware lines used for data transfer among the components of a computer or processing system. The system bus  902  is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input/output ports, network ports, etc.) that enables the transfer of information between the elements. Coupled to the system bus  902  is an I/O device interface  904  for connecting various input and output devices (e.g., keyboard, mouse, displays, printers, speakers, etc.) to the computer  900 . A network interface  906  allows the computer  900  to connect to various other devices attached to a network. Memory  908  provides volatile storage for computer software instructions  910  and data  912  that may be used to implement embodiments of the present disclosure. Disk storage  914  provides non-volatile storage for computer software instructions  910  and data  912  that may be used to implement embodiments of the present disclosure. A central processor unit  918  is also coupled to the system bus  902  and provides for the execution of computer instructions. 
     Further example embodiments disclosed herein may be configured using a computer program product; for example, controls may be programmed in software for implementing example embodiments. Further example embodiments may include a non-transitory computer-readable medium containing instructions that may be executed by a processor, and, when loaded and executed, cause the processor to complete methods described herein. It should be understood that elements of the block and flow diagrams may be implemented in software or hardware, such as via one or more arrangements of circuitry of  FIG. 9 , disclosed above, or equivalents thereof, firmware, a combination thereof, or other similar implementation determined in the future. For example, the soft speech detector  104  or  204  of  FIGS. 1 and 2 , respectively, and the as well as the noise suppressor  114  and  214  of  FIGS. 1 and 2 , respectively, and elements thereof, may be implemented in software or hardware, such as via one or more arrangements of circuitry of  FIG. 9 , disclosed above, or equivalents thereof, firmware, a combination thereof, or other similar implementation determined in the future. In addition, the elements of the block and flow diagrams described herein may be combined or divided in any manner in software, hardware, or firmware. If implemented in software, the software may be written in any language that can support the example embodiments disclosed herein. The software may be stored in any form of non-transitory computer readable medium, such as random access memory (RAM), read only memory (ROM), compact disk read-only memory (CD-ROM), and so forth. In operation, a general purpose or application-specific processor or processing core loads and executes software in a manner well understood in the art. It should be understood further that the block and flow diagrams may include more or fewer elements, be arranged or oriented differently, or be represented differently. It should be understood that implementation may dictate the block, flow, and/or network diagrams and the number of block and flow diagrams illustrating the execution of embodiments disclosed herein. 
     The teachings of all patents, published applications and references cited herein are incorporated by reference in their entirety. 
     While this invention has been particularly shown and described with references to example embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.