Patent Review:BLIND SIGNAL SEPARATION

Patent Name:BLIND SIGNAL SEPARATION
US Patent No:6,625,587

               This patent emphasizes on the implementation of Blind Signal Separation on DSP(Digital Signal Processor) hardware.The inventor uses control system block diagram approach to define operations involved in separating audio signals from various sources.Here, blind signal indicates that signal characteristics are unknown. Multiple mixtures are sampled using the multi-channel ADC(Analog-to-Digital Converter) and passed to the DSP. For faster data transfers, on-chip DMA(Direct Memory Access) is used.The DSP uses its multiplier block for forming matrices and solving them to finally obtain separated signals. The output is obtained using DAC(Digital-to-Analog Converter).

IEEE Paper Review:Blind Audio Source Separation Using Wiener Filtering Approach

            This IEEE paper discusses an interesting application called Blind Audio Source Separation. This is used to recover the source signals from the mixture of signals. In case of Blind audio separation,the characteristics of the filter are not known. The source signals can be considered as a mixture of information and interference. These signals may be coming from different sources. Considering this mixture as a linear combination of these signals, the authors have developed a method to isolate 2-3 signals using the Weiner filtering approach and Short-time Fourier Transform(STFT). The output is a complex signal,this approach removes the complexity of the signal. The areas of application are Speech Processing and Telecommunication.

IEEE Paper Link: http://ieeexplore.ieee.org/document/7359371/

Basic operations on DSP Processor

           In this Lab session some basic operations were performed on TI's C2000 DSP(Digital Signal Processor)TMS320F28335. The basic arithmetic and logical operations were performed using assembly instructions defined by the processor's architecture. The output of these instructions is seen using the JTAG debugger. These operations are similar to the ones in a regular processor but the DSP's hardware allows faster and more efficient execution of these instructions. Multiply and add instruction(MLA) has a single cycle execution due to a dedicated multiplier block inside the DSP. These fast operations differentiate DSPs from other processors. As multiplication and addition are commonly used in signal processing applications, the DSP hardware is used in such applications where faster computation is required. 

FIR FIlter Design using Windowing Method

             The second type of filters after IIR filters are FIR filters(Finite Impulse Response i.e. h[n] has finite length). The inherent advantage these filters possess is stability. After passing Ap,As,fp and fs in the code, it automatically calculates the filter order and the window type used. All window functions namely Rectangular,Bartlett,Hanning,Hamming and Blackman were analyzed. The window type is obtained from the attenuation in stopband(As). Here, inverse-DTFT of the ideal filter design is done to obtain desired h[n],after which  it is multiplied with the suitable window function to obtain the final output. For sharper frequency transitions from pass band to stop band, ripples are obtained in the frequency response. h[n] obtained is symmetric.

FIR Filter Design using Frequency Sampling Method(FSM)

                After analyzing, the windowing method Frequency sampling method was analyzed for filter design using Scilab. Here, DFT is used,as frequency response at discrete values of frequency is obtained. The output h[n] is obtained by performing inverse-DFT. As discrete values of the frequency response, it can be considered that the response is sampled,hence the name FSM. As DFT involves summation, it can be easily computed compared to integration in IDTFT in windowing method.  The filter order obtained using both methods is the same. h[n] obtained is symmetric.

Digital Chebyshev Filter design

        After Butterworth Filter design, the other type of IIR filter,Analog and then Digital Chebyshev filters were designed on Scilab. Here, Chebyshev-1 filter was designed with ripple in pass band and no ripple in stop band. On putting similar parameters as that of Butterworth filter for the same type, the order of Chebyshev filter is lower. There are ripples in pass band and the poles lie on an ellipse. The mapping of poles is one-to-one from s to z-plane. Here, the number of ripple peaks in the pass band is equal to the order of the filter. Both Butterworth and Chebyshev filters are Analog filters,hence their Digital filter frequency responses are similar to their analog ones. For IIR filters, stability is not guaranteed.

Digital Butterworth Filter Design

             After analyzing different algorithms for obtaining filter(system) output, it's time to design a filter. Firstly, IIR(Infinite Impulse Response i.e. on taking Inverse Z-Transform, h[n] has infinite length) filters were designed on Scilab. The first type are Butterworth filters with no ripple in passband and stopband. On substituting the input parameters As,Ap, Ws,Wp and sampling frequency, for both High Pass and Low Pass, Filters,a high order filter is designed.Here, first , an Analog and then a Digital Filter is designed. The poles of this filter lie on a circle inside the unit circle in the z-plane. As BLT method is used for design, there is one-to-one mapping of filter poles, but the frequency bands are compressed in case of digital filters.