Increasing signal/acoustic interference ratio in telecommunications audio exchange by adaptive filtering methods

Abstract

The paper deals with the issues of increasing signal/noise ratio in telecommunication audio exchange systems. The study of characteristics of speech signals and acoustic noises, such as mathematical expectation, dispersion, relative intensity of acoustic speech signals and various types of acoustic noises and interference is carried out. It is shown that in the design of telecommunications systems, in particular loudspeaker systems operating under the influence of external acoustic noise of high intensity, it is necessary to solve the problem of developing algorithms to effectively suppress the above mentioned interference to ensure the necessary signal/noise ratio in communication systems. A mathematical model of the autocorrelation function of the speech signal by using the Lagrange interpolation polynomial of order 10, considered the creation of adaptive algorithms to suppress acoustic noise by linear filtering methods. Thus suppression of acoustic noises and hindrances is possible at the expense of operated change of area of a cutting in the interval from 0 Hz to 3001000 Hz, depending on a hindrance conditions.

1. Introduction

The issues of increasing signal/acoustic interference in telecommunications audio exchange are relevant and widely covered in the domestic [ 1-4 ] and foreign literature [ 5-8 ]. As the main criteria for the effectiveness of telecommunications systems, to ensure a reliable exchange of information between the subscribers of the system, we can cite the signal/acoustic noise ratio and syllable intelligibility [ 1 ].

Reliable exchange of audio information in communication and telecommunication systems is possible with the provision of signal/external acoustic noise ratio in Ps Pn ≥ 20 dB, which, accordingly, will provide the necessary syllabic intelligibility S ≥ 93% for the effective, complete exchange of audio data and operational-command information in loudspeaker systems at multifunctional facilities of various purposes. For this reason, it is important to continue research aimed at developing new approaches and algorithms to increase signal-to-acoustic interference ratio by methods of adaptive filtration with the use of controlled change of the resection area. 2. Investigation of the characteristics of speech signals and acoustic noise The known mathematical model of the function of probability density P(xi) of speech signals, developed in [ 9 ], allows to receive values of mathematical expectation xi and dispersion σ 2 of speech signals according to expressions

2n −1 2n −1 xi = ∑ xi ⋅ P( xi ) , σ 2 = ∑ xi2P( xi ) , (1)

i=−2n +1 i=−(2n −1) where xi is the speech signal samples quantized at 2n intervals and normalized relative to the maximum values in the form of ± xmax = 1 V, then we obtain the values xi ≈ 0 and σ 2 = 0,126 W/Ohm.

Accordingly, the level of sound pressure intensity I relative to the zero level of intensity I0=10-12 W/m2 is defined as

I0 S where k is the directivity coefficient of a loudspeaker device, in case of radiation in one direction is accepted k = 2; S = 4πR2 is a sphere area, R is a sphere radius in meters.

Then the relative intensity of the acoustic speech signal Iр calculated by means of expression (2) has the following value (2) I ( dB ) = 10lg kσ 2

⋅1012 , I0

I p ( dB ) = 103dB , in the case of σ 2 = 0,126 W, k = 2, R=1 m.

The relative intensities for different acoustic noises and acoustic disturbances are also calculated using the values of mathematical expectation and dispersions of different types of acoustic disturbances determined by formulas (1), in which the mathematical model of the function of probability density of acoustic disturbances is applied [ 10 ]. Values of relative intensities for different acoustic signals are presented in Table 1.

Type of signal Speech signal

Quantum noise Acoustic disturbances The noise of the sea

Wind noise The noise in the engine room spectrum of speech signals in the low-frequency region, which suggests that increasing the ratio Ps Pn can be applied linear filtering methods. Another factor allowing to apply linear filtration to suppress acoustic noise is the possibility of increasing the cutting area in the range from F=0 Hz to Fl=3001000 Hz, according to studies in [ 10 ]. From these studies it can be seen that with the increase of the lower boundary of the reproduced frequencies up to F=1000 Hz the value of S practically does not change, it takes the value not less than 94% which is acceptable.

Thus, the above factors allow to consider the possibility of designing adaptive algorithms of speech signal processing and suppression of acoustic interference due to controlled change of the cutting area in the range from 0 Hz to (300÷1000) Hz, depending on the interference situation. 3. Development of an adaptive filtering algorithm with control of the resection area Consider as a model of adaptive filtering a bandpass filter of rectangular shape with a floating lowfrequency cut Fl in the AFC channel. Varying Fl within 0≤Fl≤Fh, where Fh is a fixed high-frequency section of the AFC filter. The AFC of the bandpass filter K(f) is specified as where 0≤Fl≤Fh, Fh=6000 Hz.

With the known spectral function of the speech signal G( f ) , Ps at the bandpass filter output is defined as

K( f ) = 1, at Fl ≤ f ≤ Fh , 0, at other values f ;

Fh Fh Ps = ∫ K 2 ( f )G( f )df = ∫ G( f )df ,

Fl Fl where the second part of the Ps calculation expression is rewritten according to the K 2 ( f ) = 1 integration limits of Fl to Fh .

Correspondingly changing Fl within the range of 0≤ Fl ≤ Fh , we obtain the dependence of Ps on the value of low-frequency Fl AFC of the channel. Write down the spectral function of the energy spectrum of the speech signal in the form

N −1 k−1 G( ∆f ⋅ g ) = ∑ ∑ R( kT )e− j2π∆f ⋅g⋅kT ,

g=0 k=0 where ∆f = Fd is the sampling interval of the spectral function by frequency, N is number of speech

N signal samples at the final interval, g is the number of the frequency discrete component of the spectral function reference is within 0 ≤ g ≤ N-1, R(kT) is the voice signal ACF is represented as a grid function in Table 2 for one of the voice signal implementations.

Fh Pn = ∫ GП ( f )df .

Fl The spectral function of acoustic noise GП ( ∆f ⋅ g ) looks like

N −1 K −1 GП ( ∆f ⋅ g ) = ∑ ∑ RП ( kT )l − j2π∆f ⋅g⋅kT ,

g=0 K =0 where Rп(kТ) is ACF interference is also represented by its approximation by an interpolation Lagrangian polynomial.

The process of filtration of the spectral functions of the speech signal G(f) and interference Gп(f) and, accordingly, the change in the ratio Ps Pn depending on the change in Fl within 300 Hz ≤ Fl ≤ 1000 Hz is experimentally simulated and the results are illustrated in Figure 1. Figure 1 clearly shows that the area of the integrative function of Gп(f) corresponding to the Pn decreases faster with changes in Fl from 300 Hz to 1000 Hz than the area of the integrative function of G(f). Figure 1 also demonstrates that the basic energy of the spectral function G(f) remains in the range from 0 Hz to Fl, outside the bandwidth of the rectangular filter.

The obtained results of studies of the effect of change in Fl on Ps Pn a fixed Fh=6000 Hz are shown in Figure 2. In researches cases for three various realizations of speech signals concerning various external noise hindrances are considered.

a) Wind noise

b) The noise in the engine room

4. Conclusion

From the diagrams in Figure 2 it can be seen that external acoustic interference of the "wind noise" type, if set to Fl=500 Hz, is suppressed by -(17÷23) dB. At the influence of acoustic noise of the type "noise in the engine room" and at Fl =700÷1000 Hz, such acoustic noise is suppressed up to -12 dB. At the influence of acoustic interference of "sea noise" type at Fl = 800÷1000 Hz, this interference is suppressed by (11÷15) dB [ 10 ]. The obtained results of acoustic noise suppression studies show that the linear filtration method can provide the necessary ratio of Ps Pn ≥20 dB and, accordingly, the necessary syllabic legibility of S ≥ 93% in the telecommunications system of voice information exchange.

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