Low Complexity Multiplier-less Modified FRM Filter Bank using MPGBP Algorithm

The design of a low complexity multiplier-less narrow transition band filter bank for the channelizer of multi-standard software-defined radio (SDR) is investigated in this paper. To accomplish this, the modal filter and complementary filter in the upper and lower branches of the conventional Frequency Response Masking (FRM) architecture are replaced with two power-complementary and linear phase filter banks. Secondly, a new masking strategy is proposed to fully exploit the potential of the numerous spectra replicas produced by the interpolation of the modal filter, which was previously ignored in the existing FRM design. In this scheme, the two masking filters are appropriately modulated and alternately masked over the spectra replicas from 0 to 2$\pi$, to generate even and odd channels. This Alternate Masking Scheme (AMS) increases the potency of the Modified FRM (ModFRM) architecture for the design of a computationally efficient narrow transition band uniform filter bank (termed as ModFRM-FB). Finally, by combining the adjoining ModFRM-FB channels, Non-Uniform ModFRM-FB (NUModFRM-FB) for extracting different communication standards in the SDR channelizer is created. To reduce the total power consumption of the architecture, the coefficients of the proposed system are made multiplier-less using the Matching Pursuits Generalized Bit-Planes (MPGBP) algorithm. In this method, filter coefficients are successively approximated using a dictionary of vectors to give a sum-of-power-of-two (SOPOT) representation. In comparison to all other general optimization techniques, such as genetic algorithms, the suggested design method stands out for its ease of implementation, requiring no sophisticated optimization or exhaustive search schemes. Another notable feature of the suggested approach is that, in comparison to existing methods, the design time for approximation has been greatly reduced. To further bring down the complexity, adders are reused in recurrent SOPOT terms using the Common Sub-expression Elimination (CSE) technique without compromising the filter performance

Speaker recognition using frequency filtered spectral energies

The spectral parameters that result from filtering the frequency sequence of log mel-scaled filter-bank energies with a simple first or second order FIR filter have proved to be an efficient speech representation in terms of both speech recognition rate and computational load. Recently, the authors have shown that this frequency filtering can approximately equalize the cepstrum variance enhancing the oscillations of the spectral envelope curve that are most effective for discrimination between speakers. Even better speaker identification results than using melcepstrum have been obtained on the TIMIT database, especially when white noise was added. On the other hand, the hybridization of both linear prediction and filter-bank spectral analysis using either cepstral transformation or the alternative frequency filtering has been explored for speaker verification. The combination of hybrid spectral analysis and frequency filtering, that had shown to be able to outperform the conventional techniques in clean and noisy word recognition, has yield good text-dependent speaker verification results on the new speaker-oriented telephone-line POLYCOST database.Peer ReviewedPostprint (published version

Convolutional Deblurring for Natural Imaging

In this paper, we propose a novel design of image deblurring in the form of one-shot convolution filtering that can directly convolve with naturally blurred images for restoration. The problem of optical blurring is a common disadvantage to many imaging applications that suffer from optical imperfections. Despite numerous deconvolution methods that blindly estimate blurring in either inclusive or exclusive forms, they are practically challenging due to high computational cost and low image reconstruction quality. Both conditions of high accuracy and high speed are prerequisites for high-throughput imaging platforms in digital archiving. In such platforms, deblurring is required after image acquisition before being stored, previewed, or processed for high-level interpretation. Therefore, on-the-fly correction of such images is important to avoid possible time delays, mitigate computational expenses, and increase image perception quality. We bridge this gap by synthesizing a deconvolution kernel as a linear combination of Finite Impulse Response (FIR) even-derivative filters that can be directly convolved with blurry input images to boost the frequency fall-off of the Point Spread Function (PSF) associated with the optical blur. We employ a Gaussian low-pass filter to decouple the image denoising problem for image edge deblurring. Furthermore, we propose a blind approach to estimate the PSF statistics for two Gaussian and Laplacian models that are common in many imaging pipelines. Thorough experiments are designed to test and validate the efficiency of the proposed method using 2054 naturally blurred images across six imaging applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin

Digital Filters and Signal Processing

Digital filters, together with signal processing, are being employed in the new technologies and information systems, and are implemented in different areas and applications. Digital filters and signal processing are used with no costs and they can be adapted to different cases with great flexibility and reliability. This book presents advanced developments in digital filters and signal process methods covering different cases studies. They present the main essence of the subject, with the principal approaches to the most recent mathematical models that are being employed worldwide

POWER REDUCTION BY DYNAMICALLY VARYING SAMPLING RATE

In modern digital audio applications, a continuous audio signal stream is sampled at a fixed sampling rate, which is always greater than twice the highest frequency of the input signal, to prevent aliasing. A more energy efficient approach is to dynamically change the sampling rate based on the input signal. In the dynamic sampling rate technique, fewer samples are processed when there is little frequency content in the samples. The perceived quality of the signal is unchanged in this technique. Processing fewer samples involves less computation work; therefore processor speed and voltage can be reduced. This reduction in processor speed and voltage has been shown to reduce power consumption by up to 40% less than if the audio stream had been run at a fixed sampling rate

Fractionally-addressed delay lines

While traditional implementations of variable-length digital delay lines are based on a circular buffer accessed by two pointers, we propose an implementation where a single fractional pointer is used both for read and write operations. On modern general-purpose architectures, the proposed method is nearly as efficient as the popularinterpolated circular buffer, and it behaves well for delay-length modulations commonly found in digital audio effects. The physical interpretation of the new implementation shows that it is suitable for simulating tension or density modulations in wave-propagating media.Comment: 11 pages, 19 figures, to be published in IEEE Transactions on Speech and Audio Processing Corrected ACM-clas

Speech enhancement using auditory filterbank.

This thesis presents a novel subband noise reduction technique for speech enhancement, termed as Adaptive Subband Wiener Filtering (ASWF), based on a critical-band gammatone filterbank. The ASWF is derived from a generalized Subband Wiener Filtering (SWF) equation and reduces noises according to the estimated signal-to-noise ratio (SNR) in each auditory channel and in each time frame. The design of a subband noise estimator, suitable for some real-life noise environments, is also presented. This denoising technique would be beneficial for some auditory-based speech and audio applications, e.g. to enhance the robustness of sound processing in cochlear implants. Comprehensive objective and subjective tests demonstrated the proposed technique is effective to improve the perceptual quality of enhanced speeches. This technique offers a time-domain noise reduction scheme using a linear filterbank structure and can be combined with other filterbank algorithms (such as for speech recognition and coding) as a front-end processing step immediately after the analysis filterbank, to increase the robustness of the respective application.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .G85. Source: Masters Abstracts International, Volume: 44-03, page: 1452. Thesis (M.A.Sc.)--University of Windsor (Canada), 2005