Subband Filter Banks Research Articles

Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.

Sub-band target alignment common spatial pattern in brain-computer interface

Background and objectiveIn the brain computer interface (BCI) field, using sub-band common spatial pattern (SBCSP) and filter bank common spatial pattern (FBCSP) can improve the accuracy of classification by selection a specific frequency band. However, in the cross-subject classification, due to the individual differences between different subjects, the performance is limited. MethodsThis paper introduces the idea of transfer learning and presents the sub-band target alignment common spatial pattern (SBTACSP) method and applies it to the cross-subject classification of motor imagery (MI) EEG signals. First, the EEG signals are bandpass-filtered into multiple frequency bands (sub-band filtering). Subsequently, the source domain trails are aligned into the target domain space in each frequency band. The CSP algorithm is then employed to extract features among which more representative features are selected by the minimum redundancy maximum relevance (mRMR) approach from each sub-band. Then the features of all sub-bands are fused. Finally, conventional linear discriminant analysis (LDA) algorithm is used for MI classification. ResultsOur method is evaluated on Datasets Ⅱa and Ⅱb of the BCI Competition Ⅳ. Compared with six state-of-the-art algorithms, the proposed SBTACSP method performed relatively the best and achieved a mean classification accuracy of 75.15% and 66.85% in cross-subject classification of Datasets Ⅱa and Ⅱb respectively. ConclusionTherefore, the combination of sub-band filtering and transfer learning achieves superior classification performance compared to either one. The proposed algorithms will greatly promote the practical application of MI based BCIs.

Singular spectrum analysis-based image sub-band decomposition filter banks

This paper presents a novel type of a filter bank for image decomposition that incorporates “singular spectrum analysis” (SSA). SSA is based on “singular value decomposition” (SVD).The presented filter banks provide good directional selectivity. Sub-band images highlight exposed directional features such as 0∘,90∘,+ 45∘, and − 45∘ of the input image. The proposed method can be seen as a tool to separate an image into layers or to present an image as a sum of its high-frequency directional components and a low-frequency approximation.

The VLSI design of the sub-band filterbank in MP3 decoding

The sub-band filterbank is one of the most important modules which has the largest amount of calculation in MP3 decoding. In order to save CPU resources and integrate the sub-band filterbank part into MP3 IP core, the hardware circuit of the sub-band filterbank module is designed in this paper. A fast algorithm suit for hardware implementation is proposed and achieved on FPGA development board. The results show that the sub-band filterbank function is correct in the case of using very few registers and the amount of calculation and ROM resources are reduced greatly.

Research on the Performance of Sub-Band STAP for Wideband Interference Suppression

Sub-band space-time adaptive processing (STAP), which decomposes the wideband signal using sub-band filter bank, is the common intuitive way to suppress wideband interference. Based on studying angel-frequency distribution characteristics of wideband, sub-band interference and echo signal in space-time plane, the paper gives the physical significance of sub-band interference and echo signals space-time distribution and thus reveals theoretically the fundamental reason for the poor performance of sub-band STAP. Furthermore, the paper outlines the novel way for wideband linear frequency modulation (LFM) interference cancellation in the three-dimensional space-fractional Fourier domain.

Phase Watermarking of Images with Complex-Valued Linear-Phase Subband Filter Banks

A new algorithm is proposed for digital watermarking by applying complex-valued linear-phase filter banks to low frequency-band coefficients of images in the DCT domain. The watermark is conveyed in the phase spectrum of the subband coefficients. The robustness of the algorithm is examined in JPEG encoding with different qualities and compared with the DFT-based approach. Because only low frequency-band DCT coefficients are applied to complex-valued filter banks, the computational load introduced by the complex-valued filter banks is kept low. The watermark decoding is only accessible to users with the key information, i.e., impulse responses of the complex-valued linear-phase filter banks that designed with time-frequency spread property.

A Signal-Specific QMF Bank Design Technique Using Karhunen-Loéve Transform Approximation

Block Wavelet Transforms (BWTs) are orthogonal matrix transforms that can be obtained from orthogonal subband filter banks. They were initially generated to produce matrix transforms which may carry nice properties inheriting from wavelets, as alternatives to DCT and similar matrix transforms. Although the construction methodology of BWT is clear, the reverse operation was not researched. In certain cases, a desirable matrix transform can be generated from available data using the Karhunen-Loéve transform (KLT). It is, therefore, of interest to develop a subband decomposition filter bank that leads to this particular KLT as its BWT. In this work, this dual problem is considered as a design attempt for the filter bank, hence the wavelets. The filters of the decomposition are obtained through lattice parameterization by minimizing the error between the KLT and the BWT matrices. The efficiency of the filters is measured according to the coding gains obtained after the subband decomposition and the experimental results are compared with Daubechies-2 and Daubechies-4 filter banks. It is shown that higher coding gains are obtained as the number of stages in the subband decomposition is increased.

Low power reconfigurable sub-band filter bank ASIC for MP3 decoder

There is an ever demanding need to develop low power audio devices using MP3 technology. From the profiled results of MP3 algorithm on ARM processors, it has been observed that the synthesis filter bank in the audio decoder consumes maximum power. Hence, to reduce the power consumption of the filter bank, we developed an IEEE 754 single precision floating-point runtime reconfigurable architecture. The proposed architecture consumes less power at run time as the last 12 bits of the mantissa part of the synthesis filter coefficients are zero most of the time and, hence, the corresponding multipliers will be switched off. Since the active multipliers during inverse polyphase quadrature mirror filter banks (IPQMF) are less, we are able to achieve low powered decoding process without significantly compromising on the accuracy and speed. We synthesised and simulated the architecture using 0.35 μm process technology under synopsys environment. A uniform worst case power reduction of 23.7% has been achieved in the frequency range from 1 MHz to 20 MHz when all the multipliers are in active state.

Enhancing audio signals by nonlinear spectral operations

A system and method are disclosed for enhancing audio signals by nonlinear spectral operations. Successive portions of the audio signal are processed using a subband filter bank. A nonlinear modification is applied to the output of the subband filter bank for each successive portion of the audio signal to generate a modified subband filter bank output for each successive portion. The modified subband filter bank output for each successive portion is processed using an appropriate synthesis subband filter bank to construct a modified time-domain audio signal. High modulation frequency portions of the audio signal may be emphasized or de-emphasized, as desired. The modification may be applied within one or more frequency bands.

The wavelet transform-domain LMS adaptive filter with partial subband-coefficient updating

In this paper, we propose and analyze the wavelet transform-domain LMS (WTDLMS) algorithm where only a subset of the adaptive filter coefficients are updated at each iteration. The use of wavelets or subband filterbanks can lead to several novel schemes whose combinations can lead to other sub-schemes as well. In the proposed scheme, the coefficients can be selected altogether as a block and not just individually as done earlier in the literature. This algorithm is then tested in the context of system identification and equalization

영상압축을 위한 최적의 서브밴드 양자화 필터 설계

본 논문은 다차원 서브밴드 필터 뱅크에서 양자화 효과에 대한 분석과 그에 따른 최적의 필터 설계 방법에 대한 이론을 제안한다. 기존의 서브밴드 필터뱅크에 대한 연구에서 양자화 과정에 대한 고려는 매우 미미하였다. 본 연구에서는 확률 밀도에 최적화된 양자화기가 이득에 무상관성 잡음이 더하여진 비선형 형태로 모델링 되어 서브밴드 구조에 결합된다. 분석/합성 필터 뱅크에 다위상 분리 방법을 적용하여 양자화 과정에 의한 출력에서의 MSE가 유도되며 이 에러의 최소화 과정을 통하여 최적의 필터 설계 방법이 개발된다. 또한 5점형 구조의 표본화 격자를 이용한 최적의 비분리 파라유니터리와 이중직교 필터의 설계와 이를 이용한 영상 데이터의 압축 및 복원 과정을 통하여 성능 분석을 한다. 시뮬레이션 결과 제안한 설계방법에 따른 필터가 기존의 단순한 PR 서브밴드 필터보다 영상의 압축 및 복원에서 10에서 20퍼센트 정도의 MSE 감소를 보여주었고 낮은 비트율에서 특히 우수한 성능을 나타내었다. This paper provides a rigorous theory for analysis of quantization effects and optimum filter bank design in quantized multidimensional subband filter banks. Even though subband filter design has been a hot topic for last decades, a few results have been reported on the subband filter with a quantizer. Each pdf-optimized quantizer is modeled by a nonlinear gain-plus-additive uncorrelated noise and embedded into the subband structure. Using polyphase decomposition of the analysis/synthesis filter banks, we derive the exact expression for the output mean square quantization error. Based on the minimization of the output mean square error, the technique for optimal filter design methodology is developed. Numerical design examples for optimum nonseparable paraunitary and biorthogonal filter banks are presented with a quincunx subsampling lattice. Through the simulation, <TEX>$10\~20\;\%$</TEX> decreases in MSE have been observed compared with subband filter with no quantizers especially for low bit rate cases.

Optimum filter design for quantized multidimensional subband filter bank

This paper provides a rigorous theory for analysis of quantization effects and optimum filter bank design in quantized multidimensional subband filter banks. Each pdf-optimized quantizer is modeled by a nonlinear gain-plus-additive uncorrelated noise and embedded into the subband structure. Using polyphase decomposition of the analysis/synthesis filter banks, we derive the exact expression for the output mean square quantization error. Based on the minimization of the output mean square error, the technique for optimal filter design methodology is developed. Numerical design examples for optimum nonseparable paraunitary and biorthogonal filter banks are presented with a quincunx subsampling lattice.

Digital signal processing acoustic speaker system

A technique for realizing high-speed and high-precision acoustic reproduction using acoustic speakers. The audio signal processing system has a sub-band analysis filter bank that divides the entire frequency band of an input audio signal into multiple sub-bands. Filter coefficient calculation circuits identify equalizable sub-bands and compares equalizable sub-bands and corresponding sub-bands from output audio signals in order to calculate filter coefficients. A sub-band convolution filter bank and a filter circuit perform frequency convolution on calculated filter coefficients for equalizable sub-bands and process input audio signals on the basis of this convolution.

Embedded video coding using invertible motion compensated 3-D subband/wavelet filter bank

Abstract In this paper, we present a new embedded video coding system with local motion compensation. An invertible motion compensated 3-D subband filter bank is utilized for video analysis/synthesis. The efficient embedded image coding scheme EZBC is extended to 3-D coding of video subbands. The coded bitstream is rate scalable and fully embedded thus meeting the increasing need for fine granular scalability in video streaming applications. Our experimental results show the new algorithm outperforms nonscalable standard MPEG-2 by 0.8– 3.3 dB (luminance component) over a broad range of bitrates, while providing an embedded video bitstream. Unlike conventional SNR hybrid coders, this multirate property is achieved without a loss in compression efficiency. Comparisons are also made to two other competing embedded video coders, LZC and 3D-SPIHT, both based on 3-D subband coding as well. The new coder demonstrates a significant advantage for encoding video with high local motion content, thanks to the efficient inclusion of local motion information in the temporal filter bank of our proposed system.

Permutation Based Design of Orthogonal Block Transformsand Filter Banks

In this work, a new efficient design technique for orthogonal block transforms, lapped orthogonal transforms and 4-channel perfect reconstruction subband filter banks is developed. The technique consists of permutation and sign change operations on a reference vector. This approach can be thought of as a generalization of the Hadamard transform in the sense that the reference vector {\mbox{ h }}_{0} (which will be a prototype low-pass filter also forming one of the basis functions of the transform) will in general have components that are not identically 1's. The design technique, a constructive method based on Hadamard arrays, provides a convenient means to explore new transforms. The merit of our method is that the number of unknowns and equality constraints are both reduced significantly which render the design procedure much more feasible while guaranteeing at the same time linear phase.

A new algorithm for linear-phase paraunitary filter banks with pairwise mirror-image frequency responses

Subband filter banks have attracted much attention during the past few years. In this paper, an efficient design algorithm, which leads to linear-phase paraunitary filter banks with pairwise mirror-image frequency responses, is revisited and further studied. New lattice structures are presented to extend the algorithm to the case where the number ( M) of channels is odd. Design examples of filter banks with 3 and 5 channels are presented.

Multi-channel overlapping rounding transform for HINT and subband lossless image compression

In this paper, an ORT-based (overlapping rounding transform-based) multi-channel filtering technique is proposed. We show that the proposed technique does not only unify the computations of the HINT (hierarchical interpolation) algorithms, but enables also multi-channel subband filter banks to be applied to lossless image compression. As examples, two efficient non-separable four-channel filter banks are newly presented.

Binary subband decomposition and concatenated arithmetic coding

This paper proposes a new subband coding approach to compression of document images, which is based on nonlinear binary subband decomposition followed by the concatenated arithmetic coding. We choose to use the sampling-exclusive OR (XOR) subband decomposition to exploit its beneficial characteristics to conserve the alphabet size of symbols and provide a small region of support while providing the perfect reconstruction property. We propose a concatenated arithmetic coding scheme to alleviate the degradation of predictability caused by subband decomposition, where three high-pass subband coefficients at the same location are concatenated and then encoded by an octave arithmetic coder. The proposed concatenated arithmetic coding is performed based on a conditioning context properly selected by exploiting the nature of the sampling-XOR subband filter bank as well as taking the advantage of noncausal prediction capability of subband coding. We also introduce a unicolor map to efficiently represent large uniform regions frequently appearing in document images. Simulation results show that each of the functional blocks proposed in the paper performs very well, and consequently, the proposed subband coder provides good compression of document images.

Soft-clipping postprocessor scaling decoded audio signal frame saturation regions to approximate original waveform shape and maintain continuity

An audio coder/decoder ("codec") that is suitable for real-time applications due to reduced computational complexity, and a novel adaptive sparse vector quantization (ASVQ) scheme and algorithms for general purpose data quantization. The codec provides low bit-rate compression for music and speech, while being applicable to higher bit-rate audio compression. The codec includes an in-path implementation of psychoacoustic spectral masking, and frequency domain quantization using the novel ASVQ scheme and algorithms specific to audio compression. More particularly, the inventive audio codec employs frequency domain quantization with critically sampled subband filter banks to maintain time domain continuity across frame boundaries. The input audio signal is transformed into the frequency domain in which in-path spectral masking can be directly applied. This in-path spectral masking usually results in sparse vectors. The ASVQ scheme is a vector quantization algorithm that is particularly effective for quantizing sparse signal vectors. In the preferred embodiment, ASVQ adaptively classifies signal vectors into six different types of sparse vector quantization, and performs quantization accordingly. The ASVQ technique applies to general purpose data quantization as well as to quantization in the context of audio compression. The invention also includes a "soft clipping" algorithm in the decoder as a post-processing stage. The soft clipping algorithm preserves the waveform shapes of the reconstructed time domain audio signal in a frame- or block-oriented stateless manner while maintaining continuity across frame or block boundaries. The invention includes related methods, apparatus, and computer programs.

Optimal subband filter banks for multiple description coding

Multiple description coding refers to the encoding of an information source into multiple bit streams, in a manner that each bit stream independently represents a coarse description of the source, while multiple descriptions jointly convey a higher fidelity source representation. Thus multiple description coding allows enhanced resilience to loss of individual descriptions, and can be viewed as a form of source-based diversity coding. This paper addresses the design of subband filter banks to optimize the rate-distortion performance of multiple description subband coding. The problem is formulated in a Lagrangian optimization framework, and is solved directly in the frequency domain to produce the optimal filter spectral responses. By varying the Lagrangian parameter, our design obtains all points on the redundancy-distortion curve for Gaussian wide-sense stationary input processes. We consider the cases of both orthonormal and more general biorthogonal filter banks. We also analyze the connections between these two solutions, and quantify the gap in their optimal performance. Application of the proposed methods to the popular first-order autoregressive (AR(1)) model yields very interesting and insightful results. In the two extreme cases of maximum and minimum redundancy, our solutions degenerate to previously known results. Finally, comparisons with popularly deployed filter banks reveal large gaps from the theoretical performance bound.