Jagadish

S Jakati Dr.Shridhar.K

Department

of ECE

Department

of ECE

S.G.Balekundri

Institute of Techonology

Basaveshwar

Engineering Collage

Belagavi,Karanatak,India Bagalkot,

Karanatak,India

[email protected] [email protected]

Abstract

Speech enhancement aims to improve speech quality by using various

algorithms. The objective of enhancement is

improvement in intelligibility or overall perceptual quality of degraded speech signal using audio signal

processing techniques. Speech

enhancement is necessary for many applications in which clean speech signal is

important for further processing. The speech enhancement techniques mainly

focus on removal of noise from speech signal. The various types of noise and

techniques for removal of those noises are presented in speech signal. Most

widely used speech enhancement technique namely, FIR,IIR and Multirate method is reviewed in this paper with its

state-of-art for better noise cancellation.

Key words: Speech enhancement, FIR,IIR and Multirate

1.

INTRODUCTION

Speech enhancement is an area of

speech processing where the goal is to improve the intelligibility or

pleasantness of a speech signal. The most common approach in speech enhancement

is noise removal, where we, by estimation of noise characteristics, can cancel

noise components and to get the clean speech signal. The basic problem with

this approach is that if we remove those parts of the signal that resemble

noise. In other words, speech enhancement algorithms, Current speech processing

algorithms can roughly be divided into three domains, spectral subtraction,

sub-space analysis and filtering algorithms:

• Spectral subtraction algorithms operate in the

spectral domain by removing, from each spectral band, that amount of energy

which corresponds to the noise contribution. While spectral subtraction is

effective in estimating the spectral magnitude of the speech signal, the phase

of the original signal is not retained, which produces a clearly audible

distortion known as “ringing”.

• Sub-space analysis operates in the autocorrelation

domain, where the speech and noise components can be assumed to be orthogonal,

whereby their contributions can be readily separated. Unfortunately, finding

the orthogonal components is computationally expensive. Moreover, the orthogonality

assumption is difficult to motivate.

• Finally, filtering algorithms are time-domain

methods that attempt to either remove the noise component or estimate the noise

and speech components by a filtering approach (IIR,FIR filtering and multirate

system).

2. IIR FILTER

IIR filters are

digital filters with infinite impulse response. They have the feedback (a

recursive part of a filter) and are known as recursive digital filters because

fraction of the output signal is fed

back to the input.IIR filters have much better frequency response than FIR

filters of the same order. Unlike FIR filters, their phase characteristic is

not linear which can cause a problem to the systems which need phase linearity.

For this reason, it is not preferable to use IIR filters in digital signal

processing when the phase is essence.

Fig.IIR

Filter

There is one problem

known as a potential instability that is typical of IIR filters only. FIR

filters do not have such a problem as they do not have the feedback. For this

reason, it is always necessary to check after the design process whether the

resulting IIR filter is stable or not. The input X(n) and the output

Y(n)of a causal IIR filter satisfy the

Nth order

linear constant-coefficients difference equation of the form.

Where

k=1,2,3….N

Advantages and Disadvantages

Advantages

Ø

The main advantage digital IIR filters have over FIR

filters is their efficiency in implementation, in order to meet a specification

in terms of passband, stopband, ripple, and/or roll-off. Such a set of

specifications can be accomplished with a lower order IIR filter than would be

required for an FIR filter meeting the same requirements.

Disadvantages

Ø

FIR filters can be easily made to be linear phase, a property that is not easily met

using IIR filters and then only as an approximation.

Ø

Digital IIR filters are the potential for limit cycle behavior when idle, due to

the feedback system in conjunction with quantization.

3.FIIR FILTER

Finite impulse

response (FIR) filter is a filter whose impulse response (or response to any finite

length input) is of finite duration, because it settles to zero in

finite time. An FIR filter

is usually implemented by using a series of delays, multipliers, and adders to

create the filter’s output.

Fig.FIR

Filter

Figure 2 shows the basic block diagram for an

FIR filter of length N. The delays result in operating on prior input samples.

The h0 values

are the coefficients used for multiplication, so that the output at time n is

the summation of all the delayed samples multiplied by the appropriate

coefficients. A causal FIR filter

has the following difference equation

Where M

is the order. The result y(n) is the discrete convolution of x(n) with the

(finite) impulse response:

h(n)=

Advantages and Disadvantages

Advantages

Ø There is no feedback loop in the structure of

an FIR filter. Due to not having a feedback loop, an FIR filter is inherently

stable. Meanwhile, for an IIR filter, we need to check the stability.

Ø FIR filter can provide a linear-phase response.

As a matter of fact, a linear-phase response is the main advantage of an

FIR filter over an IIR design otherwise, for the same filtering specifications,

an IIR filter will lead to a lower order

Disadvantages

Ø The main disadvantage of FIR filters is that

considerably more computation power in a general purpose processor is required

compared to an IIR filter with similar sharpness or selectivity especially

when low frequency (relative to the sample rate) cutoffs are needed.

Ø Digital signal processors provide specialized

hardware features to make FIR filters approximately as efficient as IIR for

many applications.

3.MULTIRATE SYSTEM.

Two

channel version, popularly called the Quadrature mirror Filter Bank. The input

signal X(n) is first filtered by two

filters H0(z) and H1(z),typically lowpass and high pass

filter. Each signal Xk(n) sub band signal with band limited to width

of ?. The subband signal are decimated by factor of 2 to produce Vk(n).Each

decimated signal Vk(n) is then coded in special properties of the

sub band are exploited. At the receiver end the received signal are decoded to

produce the signal V0(n) and V1(n) which are passed

through two-fold expanders. The output signal Y0(n) and Y1(n)

are then passed rough the filters F0(z) and F1(z) to

produce the output signal X^(n)

Fig.Multirate

Filter

Ø H0(z) and H1(z)-Analysis

filters

Ø V0(n) and V1(n)-

Decimated signal

Ø F0(z) and F1(z) –

Synthesis filters

Ø Y0(n) and Y1(n)-Expanded

signal

Ø X^(n)-Reconstructed signal

Compared to IIR filters, FIR

filters and Multirate Systems.

S.N

Parameter

FIR

Filter

IIR

Filter

Multirate

System

01

02

03

04

05

05