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Patent Review

SINGLE CHANNEL SOUND SEPARATION Patent No.:  US 7,243,060 B2  Inventors: Les Atlas, Seattle, WA (US); Jeffrey Thompson, Bothell, WA (US) Publication Date: Jul. 10, 2007 Filing Date  : Apr. 2, 2003 The speech of two or more simultaneous speakers (or other simultaneous sounds) conveyed in a single channel are distinguished. Joint acoustic/modulation frequency analysis and display tools are used to localize and separate sonorant(a sound produced with the vocal cords) portions of multiple-speakers’ speech into distinct regions using invertible transform functions. For example, the regions representing one of the speakers are set to zero, and the inverted modified display maintains only the speech of the other speaker. A combined audio signal is manipulated using a base acoustic transform, followed by a second modulation transform, which separates the combined signals into distinguishable components. The components corresponding to the undesired sp...
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Research Paper Review

Paper Title: Single-Channel Audio Sources Separation via Optimum Mask Filter Authors: Mahdi Fallah, Meysam Asgari In many single-channel audio separation techniques, expressing a given audio mixture in terms of its underlying signals is often introduced as a challenging topic. Opposed to commonly used GMM(Gaussian Mixture Model) based approaches, introducing a new mask filter for DFT mixtures, in this paper a VQ(vector quantization)-based single channel audio separation approach is proposed. Opposed to commonly used Mixture Maximization (MIXMAX) mask filter based approximation, which was extensively used in single-channel audio separation algorithm, it was demonstrated that the proposed approach outperformed MIXMAX mask filter in terms of SNR results as well as MOS results.
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Basic operations using DSPP

The DSP Processor on which this was implemented was TMS320F28375. Various instructions like 1. Arithmetic: ADDB, SUBB  2. Bitwise operations - AND, NOT 3. Shifting operation – LSL,LSR,ROR,ROL were learnt. Register values before and after execution of the program were noted.
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Digital FIR Filter using Frequency Sampling Method

This is the second method of FIR Filter design. The steps of finding the frequency response of the input is same as that of the windowing method. Then, DFT of the frequency response is calculated. The values are adjusted depending on the type of filter i.e. low pass filter or high pass filter. Inverse of the frequency sampled signal gives the desired filter design. The designing and plotting of magnitude response was done on Scilab.
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Digital FIR Filter Design using Windowing Method

A linear phase finite impulse response(FIR) filter was designed using Scilab. Filters like stop band and pass band attenuation and stop band and pass band frequency are taken as input from the user.There are different window functions that can be used like Hamming Window, Bartlet Window, Hanning Window, Blackman Window,etc. The window function used in this experiment is Hanning window.the formula is input in the Scilab code and then the program is executed.
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Digital Chebyshev Filter Design

We designed the Digital Chebyshev Filter using Scilab. Both Chebyshev LPF and HPF have been designed. We have plotted magnitude spectrum just like in case of Butterworth filter design. Ap, As, fp, fs and F have been entered and order of the filter and its transfer function were calculated. But in this case unlike the Butterworth filter plot, ripples were observed in the passband while the stop band was monotonic. The number of transitions in ripples signifies the order of the filter. It was also observed that the order of the filter was less in Chebyshev as compared to Butterworth for the same inputs. Hence Chebyshev filter requires less hardware. Originally posted at: https://chaitanya33.wordpress.com/2017/04/12/digital-chebyshev-filter-design/
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Digital Butterworth Filter Design

We designed the Digital Butterworth Filter using Scilab. Both Butterworth LPF and HPF have been designed. The Passband Attenuation(Ap), Stopband Attenuation(As), Analog Passband frequency(fp), Analog Stopband frequency(fs) and Sampling frequency(F) were entered as inputs. The order and transfer function of the filter were calculated. The magnitude spectrum and pole zero plot were also observed. There were no ripples seen in the passband or in stopband. Butterworth filter has all its poles inside the unit circle. Hence the filter is stable. Originally posted at: https://chaitanya33.wordpress.com/2017/04/12/digital-butterworth-filter-design/
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