10-10-2014, 03:20 PM
ECHO CANCELLATION USING THE LMS ALGORITHM
ECHO CANCELLATION.pdf (Size: 248.51 KB / Downloads: 51)
Introduction
The paper presents a solution to noise / echo cancellation and a hardware
real-time implementation of the LMS algorithm.
Although other researches have been made in this field ([2], [3]), the
results obtained from this implementation are optimistic and with future
optimizations regarding the used algorithm, the output can be improved. Also
hardware and real-time processing constraints had to be taken into consideration.
The following sub-sections will present a brief description of numerical
and adaptive filters focused on the cancellation of noise and echo. The second
section if focused on the LMS algorithm and its simulation in Matlab. The third
section presents the simulation results obtained from the adaptive cancellation of
noise. A brief description of the DSP and hardware implementation of the
algorithm is discussed in the fourth section.
Numerical Filters
The analog filter represents a functional bloc with selective proprieties in
the area of frequencies. If the signal x(t) has a given area of frequencies, then,
because of the filter, in the area of the signal y(t) only a part of the original signal
x(t) with the same amplitudes will be found, and the rest will not be taken into
consideration. In this case, the functional bloc has selective proprieties of some
frequencies from the area of the entrance signal.
Starting from the continuous signal, a system with the same functionality
as the analog filter, but within the area of discrete signals is used ([1]).
In this case, the functional block represents a computing algorithm, which
generates a sequence of data as outputs starting from a given sequence of data as
input.
Conclusions
The adaptive cancellation of the noise has many applications, because
interferences are common for many environments. Besides the LMS algorithm,
there are other algorithms like RLS and Kalman, more powerful and with a higher
convergence speed. These algorithms ([11] and [12]) or other approaches ([10])
will be taken into consideration in further developments. Another configuration is
the linear adaptive intensification, but the required order of filters is too high and
better hardware is needed in terms of computational power. Other promising paths
of research would be to use this algorithm combined with the compression of
CPU Memory
Program
Memory CPU Data
Memory
Address Bus
Data Bus
Address
Bus
Data
Bus
Address
Bus
Data
Bus174 Cristina Gabriela Sărăcin, Marin Sărăcin, Mihai Dascălu, Ana-Maria Lepar
useful information similar with [8] or with wavelet function representations in a
new methodology from [13].
All configurations need a reference noise which is related to the noise that
distorted the original signal. In order to generate the noise, only one microphone
was used. This proved to be difficult, because the noise’s wide frequency
spectrum.
The overall performance of the designed adaptive filter compared with
other implemented systems using the same filter is good, and, with further
improvements the results will improve. The LMS algorithm provides good
numerical stability and its hardware requirements are low, therefore being the best
choice on the available hardware platform. A disadvantage of this algorithm is its
weak convergence, but based on obtained results this didn’t prove to become a
major issue. On the other hand, the NLMS algorithm is one of the most
implemented adaptive algorithms in actual telecom/industrial applications.
The echo cancellation algorithm is recommended to be implemented in
hardware in case of Internet conversations (VoIP or voice chat) where high
latencies and echo can induce perturbations.