Research Article - Biomedical Research (2016) Volume 27, Issue 4

## A prediction method using instantaneous mixing plus auto regressive approach in frequency domain for separating speech signals by short time fourier transform

**C Anna Palagan ^{1*} and K Parimala Geetha**

^{2}^{1}Department of ECE, Rajas Engineering College, Vadakkangulam, Tirunelveli, Tamilnadu, India

^{2}Department of ECE, Ponjesly College of Engineering, Nagercoil, Tamilnadu, India

**Accepted on **April 14, 2016

**Visit for more related articles at**Biomedical Research

### Abstract

The revealed works of separation of speech signals, the most disadvantages is that the incidences of distortion speech at intervals the signal that affects separated signal with loud musical noise. The thought for speech separation in normal Blind Source Separation (BSS) ways in which is solely one sound supply in an exceedingly single area. The projected methodology uses as a network that has the parameters of the Instantaneous Mixing Auto Regressive model (IMAR) for the separation matrices over the entire frequency vary. A trial has been created to estimate the simplest values of the Instantaneous Mixing Auto Regressive model (IMAR) model parameters using two matrices W and G by suggests that of the maximum-likelihood estimation methodology. Supported the values of those parameters, the supply spectral half vectors square measure calculable. The whole set of Texas Instruments Massachusetts Institute of Technology (TIMIT) corpus is utilized for speech materials in evolution results. The Signal to Interference quantitative relation (SIR) improves by a median of 5dB unit of measurement over a frequency domain BSS approach.

## Keywords

Blind source separation, Separation matrices, Instantaneous mixing plus auto regressive (IMAR) model, Maximum likelihood estimation.

## Introduction

The audio and speech signal process applications, the separation of speech signals is extremely vital done by exploitation Blind supply Separation (BSS) technique. The BSS has been employed in multi verbalize applications and acoustics signal process. Many adaptation step size strategies for BSS for automaton audition systems [1] are planned. The parameters aren't adjusted manually and there's no want of further preprocessing. For the moving sources [2] the positions and velocities of the supply is obtained from the 3D hunter supported Markov chain town particle filter which ends up in high rottenness. During this methodology it provides separation of the sources with none previous data of moving sources and its accustomed perform real time speech sweetening. In frequency domain, the permutation issues [3] a brand new technique that partitioned off the complete frequency bands into little regions by exploitation correlation of separate signal powers. The region wise permutation alignment is performed by region growing manner. Here the permutation alignment relies on lay to rest frequency dependence of separated power signal.

For multi-channel acoustic echo cancelation [4] the ICA is conjointly perform supply separation and multichannel acoustic cancellation through semi BSS while not double track detection. To cut back the result of non-singularity the matrix constraint is employed. For police investigation a time varied intermixture matrix the short time Fourier rework [5] is employed. In frequency domain [3,6] for reducing the process quality and increase the speed frequency domain BSS is employed. During this algorithmic rule the upper order frequency dependencies those using real room recordings. For extracting freelance from array signals the metallic element unvaried algorithmic rule [7] is employed. For finding the nonunitary joint resolution drawback in BSS a coinciding Biunvaried algorithmic rule is introduced. Within the multiple sources case, so as to seek out the correct estimation of propagation time delays [8] Generalized State Coherence rework (GSCT) that is a non-linear rework of the house drawn by the complete demixing matrices. In biconvex divergence ICA algorithmic rule, [9] the supply signals of the blind sources springs by the characteristics of Parzen window based mostly distribution. Supported the experimental results this algorithmic rule is that the quick algorithmic rule for the blind sources that involves speech and music signals. For cancelling the echo’s within the blind sources throughout continuous double track [10] so as to estimate the blind supply signals the most chance approach is employed.

In the last decade Blind Source Separation (BSS) methods
have been widely used in the field of biomedical engineering.
Noise reduction and useful signal extraction are among the
most significant applications of BSS [11]. More particularly,
assume that one realization of N-dimensional random vector
process {x[m]_{m∈N}} with values in the real field is available.
The simulated sources are denoted by EEGS, EOGRS, EOGLS
and ECGS. They represent brain activity, rapid eye
movements, slow eye movements and cardiac activity,
respectively. More precisely, the EEGS source is simulated
using the model of Jansen where parameters were selected to
derive a cerebral background activity. Note that the statistical
distribution of this signal is quasi-Gaussian. This is in
agreement with real background EEG data. The other sources
are derived from our sleep recordings database presented in.
More precisely, the EOGRS source is issued from a band-pass
filtering (between 1 Hz and 8 Hz]) of the derivation FPZ-CZ of
the standard 10-20 system. The EOGLS source corresponds to
a low-pass filtering of the classical derivation E1-E2 with the
cut-off frequency of 4HZ in order to reduce the effect of EEG
and EMG interference. Finally, the ECGS source corresponds
to a cardiac signal recorded on patients during their sleep. In
reference to the additive noise {v[m]_{m∈N}}, it is modeled as a
spatially correlated Gaussian noise with the spatial correlation
equal to 0.5. An example of 20 seconds portion of original
sources, observations and estimated sources.

*Generation of the mixing matrix*

To derive the mixing matrix associated with the brain and
ocular activities, a three concentric sphere head model is used.
Four dipoles located at four fronto-parietal positions and a
patch of two hundred dipoles (uniformly located in the cortex)
characterize the eye movements (rapid and slow) and the
background EEG sources, respectively. The EEG recording
system contains four electrodes plus one reference electrode:
two temporal sensors, in front of the higher part of the ears,
denoted by F7m and F8m (where m stands for modified), two
frontal sensors, above the eyes, denoted by FP1m and FP2m,
and the reference electrode CZ located at the top of the head.
Then the transfer formula describing the relationship between
current dipoles and surface observation is used to obtain the (4
× 3) mixing matrix A′. Since the heart contribution to the data
is assumed to be non-uniform on all the channels, we decide to
add to the (4 × 3) mixing matrix A′ a fourth column
vector a4 with different components. Note that the obtained
mixing matrix A=[A′_{a4}] is slightly ill-conditioned.

Blind Source Separation (BSS) ways aim to realize this goal
supported some previous data of the supply signal properties.
Following the physics of sound intermixture, allow us to take
into account N sources s_{m}(t), m=1,…,N to be convolutively
mixed. At M sensors, the recorded mixture signals a_{i}(t), i=1,
…,M is denoted by

(1)

Where L is that the delay length on the order of 10^{3}-10^{4} taps
(each regulator last 1/F_{s} second where ever F_{s} is that the
sampling frequency) in AN passing commonplace space gis(d)
is that the separate Green’s perform of the world, collectively
referred to as the world impulse response (RIR). The (severely
ill-posed) mathematical downside is to recover every G_{is}(d)
and s_{m}(t) from A_{i}(t). A significant branch of BSS is that the
therefore mentioned as freelance component analysis (ICA)
that assumes that the availability signals unit orthogonal to (or
freelance of) each other [12]. ICA could be an additional
general methodology than ill sound signals. The time domain
ICA [13,14] makes an attempt to estimate the g_{is} directly so as
to contend with a high dimensional non convex optimization
downside [12,15]. Frequency domain ICA [16-18] solves AN
fast (L=0) version of (1) in every frequency bin once applying
the Discrete Fourier Transform (DFT) to (2) frame by frame:

(2)

Where (A_{i}, G_{is} &a_{mp}; S_{m}) unit the T-point DFT of (a_{i}, g_{is} &a_{mp};
S_{m}) severally, and τ is that the frame selection. The larger T/l
unit, the upper the approximation. Attributable to the absence
of the regularity in d of g_{is} and s_{m}, DFT does not retread
convolution to native product exactly. The frequency domain
approach is restricted to use an extended DFT. additionally to
computations to delineated scaling and permutation
ambiguities once synthesizing multi-frequency estimation of
S_{m}(f,τ) back to a time domain output [12,15]. Imperfections
and errors in scaling and permutation within the frequency
domain might result in artifacts within the time domain signals
at the ultimate output.

## Existing Methods and New Idea

BSS refers to the drawback of the signals from many discovered linear mixtures. Up to currently, finding the BSS drawback in associate underdetermined case has principally consisted in assumptive that the speech signals was sufficiently thin [19-22]. However, due to surprising discontinuous zeropadding, such separated signals have sizable distortion, and thus a loud musical noise is detected. In [21], associate calculable admixture matrix was used for finding the determined BSS drawback. Our suggestion for eliminating the distortion matter is to mix scantiness with admixture matrix estimation. So we are able to acquire additional info regarding the signals to be separated and to cut back the zero-artefact result, from that the musical noise originates. Whereas Vielva et al., Rickard associated Yilmaz worked on an undetermined fast case using scantiness [19-21], and Deville on a determined fast case utilizing a admixture matrix estimation [21], here, we tend to area unit managing undetermined BSS in a very convolutively case.

*Blind source separation of speech and music signals*

Blind Source Separation (BSS) may well be a technology for separating mixtures of multiple speech signals. This technology has been studied extensively and important progresses square measure transformed the last decade. However, typical BSS ways in which performs very poor once the reverberation time is huge. Several researchers have selfaddressed this drawback, but it's still associate open question. Our approach to overcoming this limitation is to unify BSS and performing arts BSS can notice BSS even underneath extremely live environments.

**Figure 1** is an example for speech separation of an interior area
downside. For these applications, the moment combination
model might even be applicable as a result of the propagation
delays is negligible. However, in real environments substantial
time-delays might occur Associate in an exceedingly style and
formula is needed to account for the mixing of time-delayed
sources and convolved sources. It focuses on the
implementation of the educational rule and on problems that
arise once separating speakers in space recordings. It used
associate informal approach throughout a feed forward
network enforced among the frequency domain pattern the
polynomial filter pure mathematics technique. Below
minimum-phase combine conditions this preprocessing step
was adequate for the separation of signals. These methods
successfully separated a recorded voice with music among the
background (indoor area problem).

## Proposed Method

*Imar model to generate microphone signals*

In the planned methodology the BSS is recovered of supply signals by LTI filter and permutation. The time domain Blind source Separation approach is employed here. During this planned methodology the estimation of blind source signal is within the variety of source signal vector baccalaureate (n) by applying Associate in Nursing IM input signals and IS output separation filter price to discovered signal vector O (n). Within the time domain BSS approach for separating sound mixtures the order of the separation filter is about a price that exceeds the space reverberation time. The order of the separation filter becomes terribly massive for the reverberation time is long. That the convergence rate is poor and therefore the price for computation is extremely high. The estimation of supply spectral part vector within the frequency domain BSS approach is completed by applying a separation matrix to the discovered spectral part vector.

During this planned methodology the assessment may be a
multiple sound supply case, wherever I_{M}=2. During this
thought the frequency domain BSS approach as shown in **Figure 2**. A pair of is by exploitation WPE methodology as a
preprocessor that illustrate the case of I_{S}=I_{M}=2. As a result of
within the start we have a tendency to use prediction error with
1st electro-acoustic transducer for BSS method. **Figure 3** shows the structure for commixture of speech signals.

For any microphone as the prediction target as

(3)

Where {h_{n,s,v}} Li ≤ L_{i}+M_{i}-1 denoted the prediction filter for
the I^{th} electro-acoustic transducer spectral part and P_{n,u,v} is that
the corresponding prediction error. The various spectral part
outputs P_{1,u,1}…..P_{Im,u,1} is obtained. The fast mixtures of the
supply spectra elements were thought of for these elements.
supported the speculation of Multichannel linear prediction the
values of P_{1,u,1,}……P_{Im,u,1} become nearly fast mixtures by
mistreatment applicable prediction filters though such
prediction filters might not be able to obtained with the WPE
technique. For the m^{th} electro-acoustic transducer the
prediction filter values area unit D_{n,s,v}.

It's assumed that the bin indices is one for all frequencies from
the set of exists values of X_{1} and L_{1} ≤ L_{1}+M_{1}-1 that is equalize
the output spectral part vector B_{u,1} supported these
assumptions we have a tendency to known Z_{u,1} with B_{u,1} is

(4)

The assumption taken within the Equation (4) won't utterly hold in real time therefore any experimental detail is desired. Therefore in IMAR model it performs high separation of speech signal supported the potential assumption is a minimum of part show the sensible validity of this assumption.

The set of Equations (3) and (4) represents the generation of
IMAR model for the determined spectral element vector O_{u,1}.
In would like of this mode could also be interpreted as follows.
The individual sound signals and therefore the spectral element
of the sound supply was given in Equation (4) square measure
in a flash mixed at the side of admixture X_{1}-1 to make element
P_{u,1}. Within the Equation (3) the blending of the remaining
components gift in element P_{u,1} with the multichannel AR
system with regression or prediction matrices {H_{s,1}}= L_{1} ≤ s ≤
L_{1+s1}-1 to come up with the determined spectra element vector
O_{u,1}.

**Figure 3** represents the Instantaneous Mixing plus Auto
Regressive (IMAR) model. The latent spectral part vector is
unperceivable by element P_{u,v}. The separation matrices and
prediction matrices parameters of IMAR model over the whole
frequency vary is denoted by Φ_{W} and Φ_{G}. The subsequent
expressions represent the set of separation matrices and
prediction matrices as

(5)

(6)

By mistreatment the most probability estimation the perfect
values of IMAR model parameters Φ_{W} and Φ_{G}. The parameter
values of the supply spectral part vectors area unit calculable
from Equations (3) and (4). The matrix type is described for
the IMAR model within the MIMO filter which supplies the
link between the IMAR model and also the frequency domain.
The expression for matrix type of X_{s,v} as

(7)

Where 0 is a zero matrix. Then (3) and (4) may be summarized in one equation as

(8)

## Experimental Results and Discussions

In the projected technique 2 sources and 2 microphones are used for testing the speech signals. The whole take a look at used a group of TIMIT corpus which has thirty eight male speakers, eight feminine speakers and a hundred forty five utterances. The sampled frequency used for the testing of acoustic signals of those utterances is fourteen kc and therefore the information measure is restricted between seventy rate to four kc frequency vary. The data’s from twenty six male speakers are taken and it will be wont to from male-male utterances pairs and therefore the data’s from five feminine speakers are taken and it will be used for feminine-feminine utterances pairs. The remaining data’s from twelve male speakers and three feminine speakers are taken and it will be paired to make the remaining utterances. Thus in total fifty five male-male, twenty feminine-feminine and seventy malefeminine utterances pairs were generated. We have a tendency to take every utterances combine and it will be mixed with the acoustic signals of the 2 utterances with the area impulse responses measured in an exceedingly varechoic chamber to simulate signals which may be discovered from the microphones.

**Figure 4** shows an experimental setup developed for the
present work. The Signal to Interference Ratio (SIR) and the
Direct to Reverberation Ratio (DRR) should be evaluated from
each trial taken by the experimental setup. The component of
I_{M} ^{th} microphone signal is given by O_{Im} ^{Is}(n) which is
originating from the I_{S} ^{th} source. Then the value of O_{Im} ^{Is}(n) is
given by the following equations as

(9)

The room impulse response is given by {b_{Is,Im} (s)} with s ≥ 0
from the Isth source speech signal to the I_{m} ^{th} microphone. The
index value of the microphone is obtained from I_{s} where the
source speech signal Is appears most prominently as

(10)

By using the source signal I_{s} ^{th} the input SIR and DRR value is
computed as

(11)

(12)

The direct reverberation components from the experimental
values of O_{Im} ^{IsR} (n) are found out from the values of O_{Im} ^{IsD} and O_{Im} ^{IsR} respectively. So from the above experimental values
the direct to dereverberent components are described as

(13)

(14)

From the experimental output every signalling is rotten in keeping with the sources so as to search out the values of signalling to interference quantitative relation.

Allow us to think about W_{Io}(n) denote the signalling I_{O} ^{th} and
WI_{o}I_{s}(n) denote the I_{s} ^{th} supply element. For these calculations
of process mike signals O_{1}I_{s}(n),….., OI_{m}I_{s}(n) with the
calculable prediction and separation matrices for conniving the
direct to reverberation quantitative relation we'd like the
impulse responses from the sources to the outputs. For
estimating the impulse responses it uses the smallest amount
mean squares matching. The impulse response is calculable
from the supply speech signal I_{S} to the output speech signal I_{O}.
The matching errors square measure taken by the assumptions of -20 dB. This experimental result indicates the IMAR model
is effective for BSS. **Figure 5** shows the input samples of
speech signals from two sources. The corresponding short time
Fourier transform was illustrated in **Figure 6**. The
instantaneous mixing of the samples based on the IMAR model
was shown in **Figure 7** the separated speech signals of the blind
sources are illustrated in different forms for various separable
speech signals are given in **Figure 8**. From that figure we can
clearly mentioned the IMAR model gives better outperformed
in the frequency domain blind source separation for both
reverberation conditions in terms of average SIR.

**Figure 8** shows the average SIRs for each reverberation
condition. We infer that the methodological difference between
the ICA, CSD and IMAR models leads to the difference in SIR
improvement. We infer that the methodological difference
between the ICA, CSD and IMAR models leads to the
difference in SIR improvement. For an experimental study the
0.3 sec and 0.5 sec reverberation time are considered. The
effects of male voices and female voices for the separation of
speech signals are estimated. **Table 1** represent the average
changes in SIR and DRR for male female and female male pair
by taking speaker 1 is male then speaker 2 is female and
another one is reverse of this.

Gender | Speaker 1 | Male | Female |
---|---|---|---|

Speaker 2 | Female | Male | |

Speaker 3 | Male | Male | |

SIR | Speaker 1 | 5.21 | 4.72 |

Speaker 2 | 5.26 | 4.88 | |

Speaker 3 | 5.3 | 4.92 | |

DRR | Speaker 1 | 4.32 | 4.12 |

Speaker 2 | 4.51 | 4.42 | |

Speaker 3 | 4.68 | 4.58 |

**Table 1.** Average sir and DRR increases in decibel.

*Application of blind source separation in the field of
biomedical engineering*

**Spectrographic displays:** A spectrogram is a display of the
magnitude of the short-time Fourier transform of a signal as a
function of both time and frequency. For sound signals,
restriction to the magnitude of the STFT is usually justified
because the ear is not very sensitive to the phase of the shorttime
spectrum. Spectrograms can be displayed either by
encoding energy on a gray scale, or as perspective
representations. Gray-scale displays are particularly
convenient, and were produced by analog spectrographic
instruments in the 1940’s, long before digital spectrograms
became available. Analog spectrograms were generated by
passing the signal through a bank of analog band pass filters,
then computing the short-time energy at the output of each
filter by rectifying and low pass filtering the band pass filter
outputs. Modern, digital spectrograms are obtained by computing fast Fourier transforms of successive signal
segments. Two bandwidths are widely used in spectrographic
analyses of speech: Broadband spectrograms, which have a
frequency resolution of 300 Hz, and narrowband spectrograms
which have a resolution of 50 Hz.

The frequency resolution of the narrowband spectrogram is sufficient to resolve individual harmonics of the fundamental frequency of voice (˜100 Hz). These harmonics appear as horizontal bands during voiced portions of speech. On the other hand, the broadband spectrogram has sucient time resolution to resolve individual opening and closing of the vocal cords, which appear as vertical striations during voiced segments. Thus, the periodic vibration of the vocal cords appears as vertical striations in broadband spectrograms, and as horizontal bands in narrowband spectrograms. The broadband spectrogram also reveals the short noise bursts of stop consonants and rapid changes in formant frequencies.

## Conclusion

The present work is disbursed to style the effectively separate of speech signal from the blind source Separation by victimization technique of fast admixture motor vehicle Regressive method and also the most probability perform. The key options given within the fast admixture motor vehicle Regressive methodology is that optimized separation of speech signals and thereby facultative U.S. to perform a blind supply separation method in thought. Within the gift methodology the signal to interference rate improves over half dozen decibel. By victimization fast admixture motor vehicle Regressive methodology it earned sensible signal to interference quantitative relation and direct to reverberation quantitative relation even once a reverberation time was 0.3 s. it's complete that the fast admixture motor vehicle Regressive methodology provides a robust tool for mike array signal process during a reverberative area impulse response. This stage theoretically needs the perfect knowledge of the noise covariance. Now, if compare the performances provided by each method. To conclude, the selection of a BSS method should be driven by hypotheses and considerations issued from application objectives such some statistical/physiological prior information on the sources and the additive noise.

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