slugAcoustic Echo Cancellation by Adaptive Combination of Normalized Sub band Adaptive Filters by Using Stochastic Gradient Algorithm
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2013, Vol 1, Issue 3
Abstract
Acoustic echo is a common occurrence in today’s telecommunication systems. It occurs when an audio source and sink operate in full duplex mode; an example of this is a hands-free loudspeaker telephone. In this situation the received signal is output through the telephone loudspeaker (audio source), this audio signal is then reverberated through the physical environment and picked up by the systems microphone (audio sink). The effect is the return to the distant user of time delayed and attenuated images of their original speech signal. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. Adaptive filtering techniques are used to reduce this unwanted echo thus increasing communication quality. The input signal of the adaptive filter is highly correlated and the impulse response of the echo path is very long. These characteristics will slow down the convergence rate of the adaptive filter if the well-known normalized least mean-square (NLMS) algorithm is used. The normalized subband adaptive filter (NSAF) offers a good solution to this problem because of its decorrelating property, Which requires a tradeoff between fast convergence rate and small steady state mean-square error (MSE). The proposed combination is carried out in sub band domain and the mixing parameter that controls the combination is adapted by means of a stochastic gradient algorithm which employs the sum of squared sub band errors as the cost function. For the adaptation of the component filters, in addition to the conventional decoupling update method, we also propose a coupling one, which can further improve the performance of the adaptive combination scheme.
Authors and Affiliations
Sunita, Parveen Bajaj
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