Chapter 2 Adaptive Noise Cancellation to Speech Enhancement

This chapter focuses on the theory and implementation of (Hayes in Statistical digital signal processing and modeling, Wiley, 1996; Hadei 2010) adaptive noise cancellation (ANC) method for dual-channel speech enhancement. Existing gradient-based approaches for adaptive filtering in speech enhancement are explained in detail. This chapter discusses about the limitations of the gradientbased algorithms for IIR filters. It also presents the advantages of the heuristic optimization technique. The organization of the chapter is as follows. Section 2.1 explains the concepts of ANC. Section 2.2 briefly reviews the different gradient-based algorithms that are proposed to speech enhancement. Section 2.3 discusses about how the heuristic optimization methods are advantageous over gradient-based approaches for ANC. 2.1 Concepts of Adaptive Noise Cancellation An adaptive noise canceller has been proposed by Widrow. It uses two or more microphones based on the availability of reference channel(s) which are characteristics of correlated samples or references of the contaminated noise. An adaptive filter utilizes the reference microphone output and produces an estimate of the noise. Its output is then subtracted from the primary microphone output (signal + noise). The overall output of the canceller is used to adjust the tap weights in the adaptive filter. Using an adaptation algorithm, ANC tends to minimize the mean square error value of the overall output. It gives the output which is the best estimate of the desired signal in the sense of minimum mean square error. Adaptive filters adjust their coefficients to minimize an error signal and can be realized as finite impulse response (FIR), infinite impulse response (IIR), lattice and transform domain filters. The most common of the adaptive algorithm is least mean square. The basic concept of adaptive noise cancellation (ANC) is based on two assumptions. These are as follows: © The Author(s) 2016 P. Kunche and K.V. V.S. Reddy, Metaheuristic Applications to Speech Enhancement, SpringerBriefs in Speech Technology, DOI 10.1007/978-3-319-31683-3_2 7 • The signal and noise at the output of the primary microphone are uncorrelated. • The noise at the output of the reference microphone is correlated with the noise component of the primary microphone output (Fig. 2.1). As shown in the figure, an adaptive noise canceller (ANC) has two inputs— primary and reference. The primary input receives a signal s from the signal source that is corrupted by the presence of noise n uncorrelated with the signal. The reference input receives a noise n0 uncorrelated with the signal but correlated in some way with the noise n. The noise n0 passes through a filter to produce an output nˆ that is a close estimate of primary input noise. This noise estimate is subtracted from the corrupted signal to produce an estimate of the signal at ŝ, the ANC system output. 2.1. 1 Adaptive Filters An adaptive filter is a computational device that attempts to model the relationship between two signals in real time in an iterative manner. An adaptive filter is defined by four aspects. 1. The signal being processed by the filter; 2. The structure that defines how the output signal of the filter is computed from its input signal; 3. The parameters within this structure that can be iteratively changed to alter the filters input–output relationship; 4. The adaptive algorithm that describes how the parameters are adjusted from one time instant to the next. signal source primary i/p s+n + o/p signal ŝ