REAL TIME AUDIO UPSAMPLING AND FILTERING

Abstract : An analog signal is any continuous signal for which the time varying feature of the signal is a representation of some other time varying quantity. In audio signal, noise can refer to the unwanted residual electronic noise signal that gives rise to acoustic noise heard as a hiss. Noise reduction, the recovery of the original signal from the noise- corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory namely the Nyquist-Shannon Sampling Theorem. In Digital Signal Processing, Upsampling is the process of inserting zero-valued sampled between original samples to increase the sampling rate. This is called Zero Stuffing. The Primary reason to interpolate is simply to increase the sampling rate at the output of one system so another system operating at a higher sampling rate can input the signal.
 EXISTING SYSTEM :
 This paper develops a complete theory for the analysis arbitrary combinations of up samplers, down samplers and filters in multiple dimensions. Although some of the simpler results are well known, the more difficult results concerning swapping up samplers and down samplers and variations thereof are new.
 DISADVANTAGE :
 In multirate digital signal processing and wavelet theory the topic of perfect reconstruction filter banks in the 1-D case is fairly well understood. However, not much in the form of a well - developed theory is known in the multi-dimensional cases of uniform band (or integer matrix sampling rate) filter banks. Moreover, in the multidimensional case, there has not been any coherent attempt to develop a complete set of tools for the analysis of arbitrary combinations of up samplers, down samplers and filters.
 PROPOSED SYSTEM :
 This paper overcomes this difficulty and gives extensions of all the results known in the 1-D case to the multidimensional case. We described the algebraic operations of upsampling and downsampling in multiple dimensions and using the theory of integer matrices, the Aryabhatta/Bezout identity in particular, we develop complete set tools for the analysis of multidimensional filter banks.
 ADVANTAGE :
 In digital communication system, digital information can be sent on a carrier through changes in its fundamental characteristics such as phase, frequency and amplitude. The use of a filter plays an important part in a communication channel because it is effective at eliminating spectral leakage, reducing channel width, and eliminating interference from adjacent symbols (Inter Symbol Interference) ISI.

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