A Novel Algorithm for the Reduction of Irregular Noise in Corrupted Speech Signals

One of the main problems associated with the use of adaptive filtering for noise cancellation is the nature of the noise signals. This problem imposes the use of high complexity algorithms to reduce the noise in useful signals. This can be impractical for many real time applications, where computational power is a critical issue. Most of existing literature approaches is based on a single and usually complex adaptation algorithm to do the job. In this paper, a new mechanism is devised to eliminate background noise from speech communications. The procedure is based on a two-sensor adaptive noise canceller that able to assign a suitable algorithm according to properties of the noise. The criterion used here is based on calculating the eigenvalue spread of the autocorrelation of the input noise. The new smart noise canceller (SNC) applies a suitable adaptive algorithm according to the eigenvalue spread. This approach showed its capability in executing noise cancellation under different types of environmental noise. Fast convergence rates, improvement in signal-to-noise ratio and substantial reduction in computational power are obtained using this SNC technique. Experiments are conducted using real life signals to demonstrate the success of the method.