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Active Noise Cancellation: The Unwanted Signal and the Hybrid Solution

The need to eliminate unwanted sounds is greater, as this is an expression of the limited tolerance that we have as individuals to the perception of sounds generated by industrial equipment, appliances and some general properties that are unpleasant for most people. Vibrations mechanical engines in operation, digging machinery and electricity generators, are just a few examples of processes or equipments that can produce signals nuisance to human ear. While mitigating these unwanted sounds already exists today, the methods used are based on passive elements that offer a poor response to low frequency sounds. This drawback happens (Kuo & Morgan, 1996), when the wavelength of the signal is long compared to the size of the muffler liabilities. The relevance in the treatment of low-frequency sounds is that they produce fatigue and loss of concentration, thus affecting the performance of people, machinery and equipment present. That is, low-frequency sounds produced very intense vibrations that can fracture structures in the team very long periods of exposure. ANC Systems must respond to changes in frequency of the primary noise with which they are working. In other words, the primary non-stationary noise vary, hence to get an acceptable cancellation we must use some kind of adaptive system, that carried out many operations at a high speed. To satisfy the requirements those ANC systems, it should be used digital signal processors (PDSs) compute based on adaptive algorithms and adaptive filters. The ability of an adaptive filter to operate and respond satisfactorily to an unknown environment, and variations that may be involved in signal reference, to make a powerful adaptive filter for signal processing and control applications. There are various types of adaptive filters but generally all share the characteristic of working with an input signal (input vector), and a desired response (output vector), these two signals are used to compute an estimate of error (error signal), which allows to control the coefficients of the adjustable filter. In this chapter, it’s proposed a new Hybrid active noise control (ANC) system. The main feature of the proposed system is the consideration of both, the online secondary path modeling and the acoustic feedback. The objective is to realize improved performance at a reasonable computational cost in a Hybrid ANC system that considers two of the more important troubles of the ANC. In the proposed system, two least mean square (LMS) adaptive filters are used in the noise control process, one for the Feedforward stage and the other for the Feedback stage, both of them use the same error signal as used in the