Frame bound computation of two-dimensional filter bank frames

2013 ◽  
Author(s):  
Yu Pan ◽  
Li Chai ◽  
Yuxia Sheng
Keyword(s):  
Filter Bank ◽  
Two Dimensional ◽  
Frame Bound

scholarly journals Flexible Filter Bank Multi-Carriers PON Based on Two-Dimensional Multiple Probabilistic Shaping Distribution

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 1793-1799 ◽  
Author(s):  
Lei Jiang ◽  
Bo Liu ◽  
Yaya Mao ◽  
Jianxin Ren ◽  
Xing Xu ◽  
...  
Keyword(s):  
Filter Bank ◽  
Two Dimensional

Theory and design of two-dimensional DFT modulated filter bank with arbitrary modulation and decimation matrices

2015 ◽  
Vol 44 ◽  
pp. 123-130 ◽  
Author(s):  
Jun-Zheng Jiang ◽  
Fang Zhou ◽  
Peng-Lang Shui ◽  
Shan Ouyang
Keyword(s):  
Filter Bank ◽  
Two Dimensional

scholarly journals Task 1A DCASE 2021: Acoustic Scene Classification with mismatch-devices using squeeze-excitation technique and low-complexity constraint

2021 ◽  
Author(s):  
Javier Naranjo-Alcazar ◽  
Sergi Perez-Castanos ◽  
Maximo Cobos ◽  
Francesc J. Ferri ◽  
Pedro Zuccarello
Keyword(s):  
Filter Bank ◽  
Low Complexity ◽  
Two Dimensional ◽  
Scene Classification ◽  
Dimensional Representation ◽  
Percentage Points ◽  
Machine Listening ◽  
Audio Clip ◽  
Audio Data ◽  
Excitation Techniques

Acoustic scene classification (ASC) is one of the most popular problems in the field of machine listening. The objective of this problem is to classify an audio clip into one of the predefined scenes using only the audio data. This problem has considerably progressed over the years in the different editions of DCASE. It usually has several subtasks that allow to tackle this problem with different approaches. The subtask presented in this report corresponds to a ASC problem that is constrained by the complexity of the model as well as having audio recorded from different devices, known as mismatch devices (real and simulated). The work presented in this report follows the research line carried out by the team in previous years. Specifically, a system based on two steps is proposed: a two-dimensional representation of the audio using the Gamamtone filter bank and a convolutional neural network using squeeze-excitation techniques. The presented system outperforms the baseline by about 17 percentage points.

A Multiplier-less Implementation of Two-Dimensional Circular-Support Wavelet Transform on FPGA

2013 ◽  
Vol 9 (1) ◽  
pp. 16-28
Author(s):  
Jassim Abdul-Jabbar ◽  
Zahraa Abede ◽  
Akram Dawood
Keyword(s):  
Wavelet Transform ◽  
Filter Bank ◽  
Lattice Structure ◽  
Critical Path ◽  
Correlation Factor ◽  
Discrete Wavelet ◽  
Two Dimensional ◽  
Maximum Frequency ◽  
Bank Branches ◽  
Low Pass

In this paper, a two-dimensional (2-D) circular-support wavelet transform (2-D CSWT) is presented. 2-D CSWT is a new geometrical image transform, which can efficiently represent images using 2-D circular spectral split schemes (circularlydecomposed frequency subspaces). 2-D all-pass functions and lattice structure are used to produce 1-level circular symmetric 2-D discrete wavelet transform with approximate linear phase 2-D filters. The classical one-dimensional (1-D) analysis Haar filter bank branches H0(z) and H1(z) which work as low-pass and high-pass filters, respectively are transformed into their 2-D counterparts H0(z1,z2) and H1(z1,z2) by applying a circular-support version of the digital spectral transformation (DST). The designed 2-D wavelet filter bank is realized in a separable architecture. The proposed architecture is simulated using Matlab program to measure the deflection ratio (DR) of the high frequency coefficient to evaluate its performance and compare it with the performance of the classical 2-D wavelet architecture. The correlation factor between the input and reconstructed images is also calculated for both architectures. The FPGA (Spartan-3E) Kit is used to implement the resulting architecture in a multiplier-less manner and to calculate the die area and the critical path or maximum frequency of operation. The achieved multiplier-less implementation takes a very small area from FPGA Kit (the die area in 3-level wavelet decomposition takes 300 slices with 7% occupation ratio only at a maximum frequency of 198.447 MHz).

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