This overview article covers finite impulse response filters and filter banks. The use of these filters for hearing aids is considered. Ways to compensate for hearing loss and ways to increase loudness using broadband amplification are considered. A schematic diagram of a method for digital signal processing using a bank of filters, as well as a technique for synthesizing interpolation filters with low computational complexity, is presented. Also, the application of the MATLAB system for the synthesis of narrow-band non-recursive FIR filters, their design procedure, methodology and examples are considered. Finite Impulse Response (FIR) filters and filter banks have specific properties that guarantee stability. Therefore, they are popular in many applications such as communication systems, audio signal processing, biomedical instruments, and so on. Unfortunately, due to the longer wavelength, the cost of implementing an FIR filter is usually not higher than an infinite impulse response (IIR) filter that meets the same requirements. It is well known that the length of an FIR filter is inversely proportional to its transition bandwidth. Therefore, the disadvantage becomes acute when a given filter has a narrow transition band. The main goal is to consider computationally efficient methods for designing FIR filters and filter banks. The masking method (FRM) results in significant savings in the number of multipliers. Next, a 16-band, low group delay, non-equal-spacing digital FIR filter bank is considered. Overall latency is significantly reduced as a result of a new filter structure that reduces the interpolation factor for prototype filters. Masking filter may be an interpolated finite impulse response (IFIR) filter that helps reduce complexity.
