Abhijit Karmakar

Design of Optimal Wavelet Packet Trees Based on Auditory Perception Criterion

A criterion is proposed to obtain an optimal wavelet packet (WP) tree based on the critical band structure of the human auditory system for time-frequency decomposition of speech and audio signals. The criterion minimizes a perceptual cost function based on Zwickers model of the critical band structure and allocates an optimal number of terminating nodes at different decomposition depths of the WP tree. The criterion is used to obtain the optimal WP tree and the corresponding critical band ordered wavelet packet basis for some typical sampling frequencies

A Multiresolution Model of Auditory Excitation Pattern and Its Application to Objective Evaluation of Perceived Speech Quality

This paper proposes a multiresolution model of auditory excitation pattern and applies it to the problem of objective evaluation of subjective wideband speech quality. The model uses wavelet packet transform for time-frequency decomposition of the input signal. The selection of the wavelet packet tree is based on an optimality criterion formulated to minimize a cost function based on the critical band structure. The models of the different auditory phenomena are reformulated for the multiresolution framework. This includes the proposition of duration dependent outer and middle ear weighting, multiresolution spectral spreading, and multiresolution temporal smearing. As an application, the excitation pattern is used to define an objective measure of auditory distortion of a distorted speech signal compared to the undistorted one. The performance of this objective measure is evaluated with a database of various kinds of NOISEX-92 degraded wideband speech signals in predicting the subjective mean opinion score (MOS) and is compared with the fast Fourier transform (FFT)-based ITU-T PESQ P.862.2 algorithm. The proposed measure is found to achieve comparable correlation between subjective MOS and objective MOS as PESQ P.862.2, with a trend suggesting better correlation for the nonstationary degradations compared to the stationary ones. Further refinement of the measure for distortion types other than additive noise is anticipated

The spectral subtractive-type algorithms for enhancing speech in noisy environments

The spectral subtraction is a traditional approach for enhancing the quality of speech degraded by environmental noise. This algorithm is based on the subtraction of the estimated noise spectrum from the noisy speech spectrum and combines it with the phase of the noisy speech. Besides suppressing the noise, this method introduces an unnatural and unpleasant remnant noise. Several variants of this algorithm have been adapted over the years to address its shortcomings, mainly the quality of the remnant noise and its trade-off with speech distortion. This paper presents a comparative performance analysis of different spectral subtractive-type single channel speech enhancement algorithms and experimental results show that the modified forms of spectral subtraction efficiently reduce the remnant noise and improve signal-to-noise ratio.

Design of an Optimal Two-Channel Orthogonal Filterbank Using Semidefinite Programming

A simple method for the design of an optimal two-channel finite impulse response (FIR) orthogonal filterbank that minimizes the stopband energy of the filters impulse response using semidefinite programming (SDP) is presented. The convex formulation is obtained by representing the optimality criterion and the orthogonality constraints in terms of the autocorrelation sequence of the filters impulse response and mapping them into the SDP framework. The resulting solution is equivalent to the solution obtained by the more complex analytical method.

Single channel speech enhancement utilizing iterative processing of multi-band spectral subtraction algorithm

The spectral subtraction method is a conventional approach for single channel speech enhancement. The basic principle of this method is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combines it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. This paper proposes a novel algorithm to reduce the remnant noise, and thus improving the overall quality of the enhanced speech. In this algorithm, the output of multi-band spectral subtraction (MBSS) method is used as the input signal again for next iteration process. After the MBSS method, the additive noise is changed to remnant noise. The remnant noise is re-estimated at each iteration. The new estimated noise, furthermore, is been used to process the next MBSS. This procedure is iterated a small number of times. The simulation results as well as informal subjective evaluations prove that the speech enhanced by proposed algorithm is more pleasant to listeners than the conventional MBSS algorithm. This reveals that the proposed algorithm reduces remnant noise satisfactorily and produces good speech quality with improved signal-to-noise ratio.

Synthesis of an Optimal Wavelet Based on Auditory Perception Criterion

A method is proposed for synthesizing an optimal wavelet based on auditory perception criterion for dyadic filter bank implementation. The design method of this perceptually optimized wavelet is based on the critical band (CB) structure and the temporal resolution of human auditory system (HAS). The construction of this compactly supported wavelet is done by designing the corresponding optimal FIR quadrature mirror filter (QMF). At first, the wavelet packet (WP) tree is obtained that matches optimally with the CB structure of HAS. The error in passband energy of the CB channel filters is minimized with respect to the ideal QMF. The optimization problem is formulated in the lattice QMF domain and solved using bounded value global optimization technique. The corresponding wavelet is obtained using the cascade algorithm with the support being decided by the temporal resolution of HAS. The synthesized wavelet is maximally frequency selective in the critical bands with temporal resolution closely matching with that of the human ear. The design procedure is illustrated with examples, and the performance of the synthesized wavelet is analyzed.

A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments

In this paper, we propose a speech enhancement method where the front-end decomposition of the input speech is performed by temporally processing using a filterbank. The proposed method incorporates a perceptually motivated stationary wavelet packet filterbank (PM-SWPFB) and an improved spectral over-subtraction (I-SOS) algorithm for the enhancement of speech in various noise environments. The stationary wavelet packet transform (SWPT) is a shift invariant transform. The PM-SWPFB is obtained by selecting the stationary wavelet packet tree in such a manner that it matches closely the non-linear resolution of the critical band structure of the psychoacoustic model. After the decomposition of the input speech, the I-SOS algorithm is applied in each subband, separately for the estimation of speech. The I-SOS uses a continuous noise estimation approach and estimate noise power from each subband without the need of explicit speech silence detection. The subband noise power is estimated and updated by adaptively smoothing the noisy signal power. The smoothing parameter in each subband is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement method is tested on speech signals degraded by various real-world noises. Using objective speech quality measures (SNR, segmental SNR (SegSNR), perceptual evaluation of speech quality (PESQ) score), and spectrograms with informal listening tests, we show that the proposed speech enhancement method outperforms than the spectral subtractive-type algorithms and improves quality and intelligibility of the enhanced speech.

Efficient integration of coprocessor in LEON3 processor pipeline for System-on-Chip design

Abstract In this paper, we have proposed an efficient method for integrating longer pipeline coprocessors with SPARCv8 compliant processor implementations that requires minimum changes in the existing processor pipeline. The proposed integration method is independent of the length of the coprocessor pipeline. We have used COordinate Rotation DIgital Computer (CORDIC) core as the coprocessor that has been integrated with SPARCv8 based LEON3 processor. Only a subset of the coprocessor instructions defined in the Instruction Set Architecture (ISA) are required in our proposed method. The required synchronisation of data and control signals between the coprocessor and LEON3 pipeline has been presented in detail. The performance of the resulting closely-coupled design is compared with bus-based integration in terms of speed, power and area in the System-on-Chip (SoC) design, and both FPGA and ASIC results are reported. Our proposed integration method shows significant improvements over bus-based method for applications that require consecutive coprocessor operations in terms of CPI metric along with substantial reduction in number of cycles. Similar strategy can be employed for integration with coprocessors having different pipeline lengths.

Implementation of an improved connected component labeling algorithm using FPGA-based platform

Labeling of connected components is one of the most fundamental operations in the area of image and video processing. This paper presents a field-programmable gate array (FPGA) platform based approach for implementing an efficient and improved two-scan equivalence-based connected component labeling algorithm. The implementation utilizes standard intellectual-property (IP) elements, FPGA off-the-shelf components, peripherals available on the Xilinx ML-507 FPGA platform and runs on an embedded PowerPC 440 processor available in the Xilinx Virtex-5 xc5vfx70t FPGA device. In this work, the equivalence handling mechanism of Stefano-Bulgarelli (SB) algorithm is improved to achieve complete merger for all the possible cases. The improved algorithm is tested using binary test patterns and standard images. The results demonstrate that the improved algorithm handles equivalences efficiently and gives accurate count of connected components. The proposed FPGA-based system arrangement can be efficiently utilized in many practical image and video processing applications, which uses connected component labeling algorithm.

A Perceptually Motivated Multi-Band Spectral Subtraction Algorithm for Enhancement of Degraded Speech

The spectral subtraction method is a classical approach for enhancement of degraded speech. The basic principle of this technique is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combine it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. The other drawback of this method is that it can work only for white Gaussian noise which has a flat spectrum and is distributed uniformly over the frequency spectrum. But real-world noise is mostly colored and has a non-uniform spectrum. To take care of this kind of noises, spectral subtraction algorithm has been extended to a multi-band case with uniformly spaced frequency bands. In this paper, a perceptually motivated multi-band spectral subtraction algorithm is proposed to enhance the speech signal degraded by colored noise. In the proposed scheme, the whole speech spectrum is divided in different non-uniform bands (N = 6) in accordance to the critical-band rate scale and spectral subtraction is executed independently in each band. The simulation results as well as informal subjective evaluations show that the proposed algorithm reduces remnant noise efficiently and the enhanced speech contains minimal speech distortions with improved signal-to-noise ratio.