JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, VOLUME 3, ISSUE 4, APRIL 2013 1 Abstract— The quality of speech during the communication is generally effected by the surrounding noise and interference. To improve the quality of speech signal and to reduce the noise, speech enhancement is one of the most used branches of signal processing. For the reduction of noise from speech signals, one method is the AGE (Adaptive Gain Equalizer). This report presents a real time implementation of an AGE noise suppressor method using Uniform FFT (Fast Fourier Transform) modulated filter bank for speech communication system. Our result shows that this method offers low complexity, low delay and high flexibility makes this method suitable for wide range of implementations. Index Terms—Adaptive Gain Equalizer, FFT Filter Bank, Noise Suppression. 1. INTRODUCTION he objective of AGE is to divide the input signal into a number of frequency sub bands, that are individually and adaptively boosted according to a short term signal-to-noise ratio (SNR) estimate in each sub band at every time instant, that means it is focusing on enhancing the speech signal instead of suppression of the noise. A high sub band SNR estimate indicates that the sub band signal content is less corrupted by noise. Hence the sub band should be boosted. A low sub band SNR estimate indicates that the surrounding noise is dominant in the sub band at hand. Hence no boosting of the sub band speech should be performed. To achieve this speech boosting effect, a short term average per speech tracking and long term average for background noise floor level tracking are calculated simultaneously. Using the coefficients of these quantities, a gain function is achieved that weights the sub band signal directly according to a sub band signal SNR estimate at that particular time instant. If only noise is present in the signal, the noise floor level estimate and the short term average will be approximately same. Hence, the coefficients of these two measures will be unity and no alteration of the sub band signal will be performed. If speech is present, the short term average will increase but the noise floor level estimate will remain approximately unchanged. Hence, the coefficients will become larger than unity, amplifying the signal in the sub-band at hand. A general filter bank is a group of parallel low pass, band pass or high pass filters. It converts the normal representation of the signal nothing but time domain into time-frequency domain which is usually implemented in modern speech processing methods. Here, we are using a uniform FFT modulated filter bank which comprises of band pass filters which have very little mutual overlap in frequency which is shown in Fig.1. The notation “uniform” is because of the fact that the filters are uniformly distributed on the frequency axis during the modulation process. Fig.1. A bank of eight band pass filters hk[n], with Fourier transform Hk[f]=F{hk[n]} , comprise a filter bank. 2. PROBLEM STATEMENT AND MAIN CONTRIBUTION In a typical situation where a speech signal is distorted by noise i.e., the noise is acoustically added to the speech. The goal is to suppress the noise using some speech enhancement method resulting in an output signal with a higher SNR. Our main contribution is to design the adaptive gain equalizer noise suppressor for speech enhancement using MATLAB and then implement the method using CC studio on TMSC6713 processor and validate the results. 3. PROBLEM SOLUTION 3.1 Uniform FFT Modulated Filter bank: This filter bank consists of K band pass filters, for k=0, 1...K-1, with impulse response functions, each of length N taps. These filter banks are created by modulating (frequency-shifting) a low pass prototype Suppression of Noise in Speech using Adaptive Gain Equalizer 1 Anil Chokkarapu, 2 Sarath C Uppalapati and 3 Abhiram Chinthakuntla T ———————————————— Anil Chokkarapu is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden. Sarath C Uppalapati . is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden. Abhiram Chinthakuntla is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden
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Suppression of Noise in Speech using Adaptive Gain Equalizer
Journal of Information and Communication Technologies, ISSN 2047-3168, Volume 3, Issue 4, April 2013
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JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, VOLUME 3, ISSUE 4, APRIL 2013
1
Abstract— The quality of speech during the communication is generally effected by the surrounding noise and interference. To improve
the quality of speech signal and to reduce the noise, speech enhancement is one of the most used branches of signal processing. For the reduction of noise from speech signals, one method is the AGE (Adaptive Gain Equalizer). This report presents a real time implementation of an AGE noise suppressor method using Uniform FFT (Fast Fourier Transform) modulated filter bank for speech communication system. Our result shows that this method offers low complexity, low delay and high flexibility makes this method suitable for wide range of implementations.
Index Terms—Adaptive Gain Equalizer, FFT Filter Bank, Noise Suppression.
1. INTRODUCTION
he objective of AGE is to divide the input signal into
a number of frequency sub bands, that are
individually and adaptively boosted according to a short
term signal-to-noise ratio (SNR) estimate in each sub
band at every time instant, that means it is focusing on
enhancing the speech signal instead of suppression of the
noise. A high sub band SNR estimate indicates that the
sub band signal content is less corrupted by noise. Hence
the sub band should be boosted. A low sub band SNR
estimate indicates that the surrounding noise is dominant
in the sub band at hand. Hence no boosting of the sub
band speech should be performed.
To achieve this speech boosting effect, a short term
average per speech tracking and long term average for
background noise floor level tracking are calculated
simultaneously. Using the coefficients of these quantities,
a gain function is achieved that weights the sub band
signal directly according to a sub band signal SNR
estimate at that particular time instant. If only noise is
present in the signal, the noise floor level estimate and
the short term average will be approximately same.
Hence, the coefficients of these two measures will be
unity and no alteration of the sub band signal will be
performed. If speech is present, the short term average
will increase but the noise floor level estimate will remain
approximately unchanged. Hence, the coefficients will
become larger than unity, amplifying the signal in the
sub-band at hand.
A general filter bank is a group of parallel low pass,
band pass or high pass filters. It converts the normal
representation of the signal nothing but time domain into
time-frequency domain which is usually implemented in
modern speech processing methods. Here, we are using a
uniform FFT modulated filter bank which comprises of
band pass filters which have very little mutual overlap in
frequency which is shown in Fig.1. The notation
“uniform” is because of the fact that the filters are
uniformly distributed on the frequency axis during the
modulation process.
Fig.1. A bank of eight band pass filters hk[n], with Fourier transform
Hk[f]=F{hk[n]} , comprise a filter bank.
2. PROBLEM STATEMENT AND MAIN
CONTRIBUTION
In a typical situation where a speech signal is distorted
by noise i.e., the noise is acoustically added to the speech.
The goal is to suppress the noise using some speech
enhancement method resulting in an output signal with a
higher SNR.
Our main contribution is to design the adaptive gain
equalizer noise suppressor for speech enhancement using
MATLAB and then implement the method using CC
studio on TMSC6713 processor and validate the results.
3. PROBLEM SOLUTION
3.1 Uniform FFT Modulated Filter bank:
This filter bank consists of K band pass filters, 𝐻𝑘 𝑧 for
k=0, 1...K-1, with impulse response functions𝑘 𝑛 , each
of length N taps. These filter banks are created by
modulating (frequency-shifting) a low pass prototype
Suppression of Noise in Speech using Adaptive Gain Equalizer
1Anil Chokkarapu, 2Sarath C Uppalapati and3Abhiram Chinthakuntla
T
————————————————
Anil Chokkarapu is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden.
Sarath C Uppalapati . is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden.
Abhiram Chinthakuntla is with School of Engineering, Blekinge Tekniska Högskola, Karlskrona, Sweden
JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES, VOLUME 3, ISSUE 4, APRIL 2013
2
filter (which is equivalent to the first band pass filter at
DC frequency, i.e. 0 𝑛 ) , according to
𝑘 𝑛 = 𝑊𝑘−𝑘𝑛0 𝑛 for n=0, 1, . . .N-1 (1)
Therefore, the Z-transform of each modulated band
passfilter is given by:
𝐻𝑘 𝑧 = 𝐻0 𝑊𝐾𝑘𝑧 (2)
We assume that the input signal filtered by each
modulated band pass filter, are subject to decimation a
factor D, where D=K/O and O denotes the over-sampling
ratio . A filter with the decimator is implemented with
the polyphase implementation. It is achieved by dividing
the prototype filter 𝐻𝑘 𝑧 into O number of groups
containing D number of polyphase components. Now we
apply IFFT to the O number of group of polyphase
components individually and since the sub band indices
are not in order, we need to arrange them in increasing
order. Since, we have a symmetric, real valued input
speech signal and we have only the first half of the
frequency, then the other half can be generated by just
taking the complex conjugate values of the first half. This
implementation is known as analysis filter bank and it is
shown in Fig. 2.
Fig.2. Analysis filter bank
Designing the synthesis filter bank is similar to that of
analysis filter bank except for the polyphase components
of the synthesis filter bank are obtained by flipping and
applying conjugate to the analysis polyphase components
and instead of IFFT in the analysis filter bank, we use FFT
in the synthesis filter bank. The model of the synthesis
filter bank can be shown in Fig. 3.
3.1 Adaptive gain equalizer:
Suppose we have an acoustic noise denoted w[n] and a
speech signal denoted s[n]. The noise corrupted speech
signal x[n] can be written as 𝑥 𝑛 = 𝑠 𝑛 + 𝑤 𝑛 . By
filtering the input signal by using an analysis filter bank,
the signal is divided into K sub bands each denoted by
Fig.3. Synthesis filter bank
𝑥𝑘 𝑛 where k is the sub band index, we get𝑥𝑘 𝑛 =
𝑥 𝑛 ∗ 𝑘 𝑛 where * indicates convolution operator.The
input signal can be described as
𝑥 𝑛 = 𝑥𝑘 𝑛 = 𝑠𝑘 𝑛 + 𝑤𝑘 𝑛 𝐾−1𝑘=0
𝐾−1𝑘=0 (4)
where𝑠𝑘 𝑛 is the speech part sub-band k and 𝑤𝑘 𝑛 is the
noise part sub-band k. The output y[n] is formed by
𝑦 𝑛 = 𝐺𝑘 𝑛 𝑥𝑘 𝑛 𝐾−1𝑘=0 (5)
Where Gk[n] is a gain function (AGE weighting function)
which introduces a gain to each sub band and it amplifies
the signal when speech is active. Fig. 4 shows the simple
block diagram of the AGE.
Fig.4. Block diagram of Adaptive gain equalizer
Two terms used for the calculation of the gain function
are; a long term (slow) average𝐴𝑠,𝑡(𝑡)and the short term
(fast) average𝐴𝑓 ,𝑡(𝑡). The short term average for sub-band
k, 𝐴𝑓 ,𝑘(𝑛) is calculated as,
𝐴𝑓 ,𝑘 𝑛 = 1 − 𝛼𝑘 𝐴𝑓 ,𝑘 𝑛 − 1 + 𝛼𝑘 |𝑥𝑘 𝑛 | (6)
Where 𝛼𝑘 is small positive constant, given by
𝛼𝑘 =1
𝑇𝑠,𝑘∗𝐹𝑠 (7)
where 𝐹𝑠 is the sampling frequency in Hz and 𝑇𝑠,𝑘 is a time