scholarly journals Research of application of DFT-modulated filter bank in systems with significant spectral component amplification

Doklady BGUIR ◽  
2021 ◽  
Vol 19 (6) ◽  
pp. 14-22
Author(s):  
N. S. Sanko ◽  
M. I. Vashkevich
Keyword(s):  
Filter Bank ◽  
Amplification Factor ◽  
Spectral Component ◽  
Synthesis Method ◽  
Efficient Algorithms ◽  
Synthesis System ◽  
Synthesis Filter ◽  
Short Time ◽  
Speech Information ◽  
Factor Difference

The purpose of this article is to investigate the application of DFT-modulated filter bank in systems with significant spectral component amplification like hearing aid. There is a description of analysis / synthesis method based on short-time Fourier transform (STFT), which is used in most systems of speech information processing. It is shown that DFT-modulated filter bank is a generalization of STFT-method. In analysis / synthesis system based on DFT-modulated filter bank, the input signal is divided into subbands, passing through the analysis filter bank then each subband is amplified and the last step is to reconstruct the signal with synthesis filter bank. However, in digital systems with significant spectral component amplification, the resulting signal is distorted after reconstruction because of amplification factor difference in each subband. The article provides expressions for the distortion and the aliasing functions, allowing to estimate the distortion value, which appears in analysis / synthesis system of DFT-modulated filter bank. Efficient algorithms for calculating the distortion and the aliasing functions are also offered. In future it is planning to develop a procedure for optimizing the DFT-modulated filter bank based on the proposed efficient algorithms for calculating distortion and spectral aliasing in the filter bank.

scholarly journals Building a natural sounding Text-To-Speech system for the Nepali language: research and development challenges and solutions

Gipan ◽  
2019 ◽  
Vol 4 ◽  
pp. 106-116
Author(s):  
Roop Shree Ratna Bajracharya ◽  
Santosh Regmi ◽  
Bal Krishna Bal ◽  
Balaram Prasain
Keyword(s):  
Research And Development ◽  
Visually Impaired ◽  
Speech Synthesis ◽  
Selection Process ◽  
Synthesis Method ◽  
Text To Speech ◽  
Synthesis System ◽  
Unit Selection ◽  
Language Research ◽  
Shed Light

Text-to-Speech (TTS) synthesis has come far from its primitive synthetic monotone voices to more natural and intelligible sounding voices. One of the direct applications of a natural sounding TTS systems is the screen reader applications for the visually impaired and the blind community. The Festival Speech Synthesis System uses a concatenative speech synthesis method together with the unit selection process to generate a natural sounding voice. This work primarily gives an account of the efforts put towards developing a Natural sounding TTS system for Nepali using the Festival system. We also shed light on the issues faced and the solutions derived which can be quite overlapping across other similar under-resourced languages in the region.

Multispectral coherence

2018 ◽  
Vol 6 (1) ◽  
pp. T61-T69 ◽  
Author(s):  
Fangyu Li ◽  
Jie Qi ◽  
Bin Lyu ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Spectral Component ◽  
Single Image ◽  
Anadarko Basin ◽  
Seismic Amplitude ◽  
Spectral Components ◽  
Structural Boundary

Seismic coherence is a routine measure of seismic reflection similarity for interpreters seeking structural boundary and discontinuity features that may be not properly highlighted on original amplitude volumes. One mostly wishes to use the broadest band seismic data for interpretation. However, because of thickness tuning effects, spectral components of specific frequencies can highlight features of certain thicknesses with higher signal-to-noise ratio than others. Seismic stratigraphic features (e.g., channels) may be buried in the full-bandwidth data, but can be “lit up” at certain spectral components. For the same reason, coherence attributes computed from spectral voice components (equivalent to a filter bank) also often provide sharper images, with the “best” component being a function of the tuning thickness and the reflector alignment across faults. Although one can corender three coherence images using red-green-blue (RGB) blending, a display of the information contained in more than three volumes in a single image is difficult. We address this problem by combining covariance matrices for each spectral component, adding them together, resulting in a “multispectral” coherence algorithm. The multispectral coherence images provide better images of channel incisement, and they are less noisy than those computed from the full bandwidth data. In addition, multispectral coherence also provides a significant advantage over RGB blended volumes. The information content from unlimited spectral voices can be combined into one volume, which is useful for a posteriori/further processing, such as color corendering display with other related attributes, such as petrophysics parameters plotted against a polychromatic color bar. We develop the value of multispectral coherence by comparing it with the RGB blended volumes and coherence computed from spectrally balanced, full-bandwidth seismic amplitude volume from a megamerge survey acquired over the Red Fork Formation of the Anadarko Basin, Oklahoma.

scholarly journals Robustness of Auditory Teager Energy Cepstrum Coefficients for Classification of Pathological and Normal Voices in Noisy Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Lotfi Salhi ◽  
Adnane Cherif
Keyword(s):  
Auditory Processing ◽  
Filter Bank ◽  
Energy Operator ◽  
Time Spectrum ◽  
Neural System ◽  
Noisy Environments ◽  
Robust Feature Extraction ◽  
Short Time ◽  
Teager Energy

This paper focuses on a robust feature extraction algorithm for automatic classification of pathological and normal voices in noisy environments. The proposed algorithm is based on human auditory processing and the nonlinear Teager-Kaiser energy operator. The robust features which labeled Teager Energy Cepstrum Coefficients (TECCs) are computed in three steps. Firstly, each speech signal frame is passed through a Gammatone or Mel scale triangular filter bank. Then, the absolute value of the Teager energy operator of the short-time spectrum is calculated. Finally, the discrete cosine transform of the log-filtered Teager Energy spectrum is applied. This feature is proposed to identify the pathological voices using a developed neural system of multilayer perceptron (MLP). We evaluate the developed method using mixed voice database composed of recorded voice samples from normophonic or dysphonic speakers. In order to show the robustness of the proposed feature in detection of pathological voices at different White Gaussian noise levels, we compare its performance with results for clean environments. The experimental results show that TECCs computed from Gammatone filter bank are more robust in noisy environments than other extracted features, while their performance is practically similar to clean environments.

scholarly journals Solution Phase Synthesis of Chemical Libraries for Lead Discovery

1996 ◽  
Vol 1 (4) ◽  
pp. 179-186 ◽  
Author(s):  
Cheryl D. Garr ◽  
John R. Peterson ◽  
Lauri Schultz ◽  
Amy R. Oliver ◽  
Ted L. Underiner ◽  
...  
Keyword(s):  
Analytical Data ◽  
Synthesis Method ◽  
Computational Design ◽  
Significant Degree ◽  
Reaction Products ◽  
Lead Discovery ◽  
Drug Candidates ◽  
Design And Synthesis ◽  
The Individual ◽  
Short Time

By integrating advanced computational design and synthesis, a series of structurally diverse reaction products based on three core scaffolds were prepared by a propietary high throughput synthesis method. Incorporation of auto-mated work stations and sample handling techniques allowed for the production of more than 20,000 compounds in a relatively short time. A method to efficiently obtain quantitative and qualitative analytical data on these compounds was developed. Structural comparison of the individual members of this library with a database of clinical drug candidates revealed a significant degree of similarity based on Tanimoto coefficients.

scholarly journals Improvement of LMS adaptive noise canceller using uniform Poly-phase digital filter bank

2020 ◽  
Vol 17 (3) ◽  
pp. 1258
Author(s):  
Alaa Hadi Mohammad ◽  
Azura Che Soh ◽  
Noor Faezah Ismail ◽  
Ribhan Zafira Abdul Rahman ◽  
Mohd Amran Mohd Radzi
Keyword(s):  
Digital Filter ◽  
Filter Bank ◽  
Signal To Noise Ratio ◽  
Process Analysis ◽  
Lms Algorithm ◽  
Least Mean Square ◽  
Full Band ◽  
Synthesis Filter ◽  
Adaptive Noise Canceller ◽  
Digital Filter Bank

<span>This paper presents the Least Mean Square (LMS) noise canceller using uniform poly-phase digital filter bank to improve the noise can-cellation process. Analysis filter bank is used to decompose the full-band distorted input signal into sub-band signals. Decomposition the full-band input distorted signal into sub-band signals based on the fact that the signal to noise ratio (S/N) is inversely proportional to the signal bandwidth. Each sub-band signal is fed to individual LMS algorithm to produce the optimal sub-band output. Synthesis filter bank is used to compose the optimal sub-band outputs to produce the final optimal full-band output. In this paper, m-band uniform Discrete Fourier Transform (DFT) digital filter bank has been used because its computational complexity is much smaller than the direct implementation of digital filter bank. The simulation results show that the proposed method provides the efficient performance with less and smooth error signal as compared to conventional LMS noise canceller.</span>

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