Filter Bank Research Articles

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M. The simulation results for design examples outperform the techniques in the available literature in terms of amplitude and aliasing distortion, reaching distortion around 2.4489 × 10−4 and 3.4907 × 10−9, respectively. This optimization algorithm's usefulness is further demonstrated with the sub-band coding of ECG signals. Implementing optimal prototype filters has tangible real-world effects, especially in critical sectors like healthcare and communications, improving diagnostics accuracy, data transmission efficiency, and overall performance.

Iterative feature mode decomposition: A novel adaptive denoising method for mechanical fault diagnosis

Abstract Remaining useful life prediction of rolling bearings highly relies on feature extraction of signals. The use of denoising algorithms helps to better eliminate noise and extract features, thereby constructing health indicators to predict remaining useful life. This paper proposes a novel adaptive denoising method based on iterative feature mode decomposition ( FMD) toI accurately and efficiently extract fault features. The feature mode decomposition (FMD) employs correlation kurtosis (CK) as the objective function for iterative filter bank updates, enabling rapid identification of fault features. To achieve IFMD, the sparrow search algorithm combines sine-cosine algorithm and cauchy variation (SCSSA) to optimize two key parameters in FMD. During the continuous iteration process of the SCSSA algorithm, filter length and number of modes were determined. IFMD does not require empirical setting of initial parameters. During iterative process, the signal is accurately decomposed and the noise is eliminated. Compared with other optimization algorithms, SCSSA has obvious advantages in iterative rate and global optimization. The envelope spectrum feature energy ratio (ES-FER) is used to select decomposed modes, and the mode with the largest ES-FER is chosen as the optimal mode. Bearing fault diagnosis is realized by envelope spectrum analysis of the optimal mode. The numerical simulations and experimental verifications both validate the effectiveness and superiority of the proposed IFMD in mechanical fault diagnosis.

A Study on Speech Recognition by a Neural Network Based on English Speech Feature Parameters

In this study, from the perspective of English speech feature parameters, two feature parameters, the mel-frequency cepstral coefficient (MFCC) and filter bank (Fbank), were selected to identify English speech. The algorithms used for recognition employed the classical back-propagation neural network (BPNN), recurrent neural network (RNN), and long short-term memory (LSTM) that were obtained by improving RNN. The three recognition algorithms were compared in the experiments, and the effects of the two feature parameters on the performance of the recognition algorithms were also compared. The LSTM model had the best identification performance among the three neural networks under different experimental environments; the neural network model using the MFCC feature parameter outperformed the neural network using the Fbank feature parameter; the LSTM model had the highest correct rate and the highest speed, while the RNN model ranked second, and the BPNN model ranked worst. The results confirm that the application of the LSTM model in combination with MFCC feature parameter extraction to English speech recognition can achieve higher speech recognition accuracy compared to other neural networks.

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

An Improved Rational Approximation of Bark Scale Using Low Complexity and Low Delay Filter Banks

Group-Invariant Max Filtering

AbstractGiven a real inner product space V and a group G of linear isometries, we construct a family of G-invariant real-valued functions on V that we call max filters. In the case where $$V={\mathbb {R}}^d$$ V = R d and G is finite, a suitable max filter bank separates orbits, and is even bilipschitz in the quotient metric. In the case where $$V=L^2({\mathbb {R}}^d)$$ V = L 2 ( R d ) and G is the group of translation operators, a max filter exhibits stability to diffeomorphic distortion like that of the scattering transform introduced by Mallat. We establish that max filters are well suited for various classification tasks, both in theory and in practice.

Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the skin lesion images can be extracted using computer techniques to differentiate early between melanoma and benign skin lesions. A new model of empirical wavelet decomposition (EWD) based on tan hyperbolic modulated filter banks (THMFBs) (EWD-THMFBs) was used to obtain the features of skin lesion images by MATLAB software. The EWD-THMFBs model was compared with the empirical short-time Fourier decomposition method based on THMFBs (ESTFD-THMFBs) and the empirical Fourier decomposition method based on THMFBs (EFD-THMFBs). The accuracy rates obtained for EWD-THMFBs, ESTFD-THMFBs, and EFD-THMFBs models were 100%, 98.89%, and 83.33%, respectively. The area under the curve (AUC) was 1, 0.97, and 0.91, respectively. The EWD-THMFBs model performed best in extracting features from skin lesion images. This model can be programmed on a mobile to detect skin lesions in rural areas by a nurse before consulting a dermatologist.

New two-microphone simplified sub-band forward algorithm based on separated variable step-sizes for acoustic noise reduction

This paper presents an extended two-microphone adaptive filtering algorithm implemented on the sub-band simplified forward technique using new separated variable step-sizes parameters (denoted SSF-SVS: Simplified Sub-band Forward Separated Variable Step-sizes). This algorithm is proposed to reduce the acoustic noise and enhance the quality of estimated speech signal. Firstly, a sub-signal decomposition processing strategy is used to facilitating effective noise reduction with very fast convergence rate using two analysis filter banks. Secondly, a two-microphone simplified forward blind source separation (BSS) structure updated by the normalized least-mean-square algorithm (NLMS) for extracting the estimated speech signal. To guarantee a very fast convergence rate with high level of enhanced speech quality, thirdly we proposed new separated variable step-sizes parameters for controlling the adaptation of sub-band filters. The concept involves suggesting individual variable step-size parameter for each adaptive sub-filter, which are updated independently using the input sub-signal and the estimated output sub-signal. Experimental results confirm significant improvements in terms of convergence rate based on the mean square error criterion (MSE) in diverse acoustical environments. In other hand, the good performance of the proposed SSF-SVS algorithm in terms of speech quality is verified by on output signal-to-noise ratio (Output-SNR). The proposed algorithm compared with six two-microphone adaptive filtering algorithms in term of convergence rate, speech quality and computational complexity.

Cascaded maximum median-margin discriminant projection with its application to face recognition

This paper introduces a novel method called cascaded maximum median-margin discriminant projection (CMMDP) for robust face recognition. Initially, we introduce the maximum median-margin discriminant projection (MMDP) method, which aims to maximize the distances between class medians and their inter-class neighbors, while simultaneously minimizing the distances between class medians and their intra-class samples. To extract features at multiple levels, we extend the MMDP method to its deep version, known as CMMDP. The CMMDP algorithm incorporates the MMDP technique to learn multistage filter banks specifically designed for facial images. Afterward, we utilize straightforward binary hashing techniques for efficient indexing, along with block histograms for effective feature pooling. The CMMDP model is simple and easy to train, while taking full account of the global and local structure of the samples by maximizing the distances between samples that are dissimilar and minimizing the distances between samples that are similar after projection. This results in a low-dimensional data representation with better discriminative performance. We evaluate the performance of CMMDP on face datasets including AR, ORL, ExtYaleB and CMU PIE databases. The results indicate that CMMDP achieves superior performance than most advanced methods.

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Locating the informative frequency band of rolling bearing fault signals is of great significance for feature extraction and fault diagnosis. Benefiting from the adjustable center frequency and bandwidth as well as the similarity to impulse-like characteristics induced by bearing failures, Morlet wavelets are commonly used in resonance demodulation. However, fault impulses are highly susceptible to contamination by strong noise, which impedes the efficacy of existing wavelet parameter selection strategies and frequency band optimization methods. In this paper, an adaptive Morlet wavelet-based iterative filtering (AMIF) method is proposed for frequency band optimization under strong noise. The resonance frequency band is pinpointed based on adaptive Morlet wavelet filter banks, with off-band noise being canceled and fault features being refined during the level-by-level filtering process. Additional iterative operations are leveraged to enhance fault features of in-band signals to facilitate the optimization of the filtering parameters. Effectiveness of the proposed AMIF method and its superiority over the wavelet packet transform-based kurtogram and minimum entropy deconvolution are verified through simulation and experimental analysis. The results demonstrate that AMIF can accurately localize the informative frequency band, thereby extracting high-quality fault features, making it suitable for bearing fault diagnosis under strong noise condition with different fault types.

Feature fusion improves brain-interface paradigm based on steady state visual evoked potential blocking response

The steady-state visual evoked potential blocking response (SSVEP-BR) is produced on an electroencephalogram (EEG) when the SSVEP is interrupted or abolished and allows augmentation of brain-computer interface (BCI) paradigms. Integration of the SSVEP-BR with the SSVEP enables an increase in the number of commands without the addition of further stimuli but refinement would improve performance. The current study evaluated SSVEP-BR and a novel method to combine multiple features and enhance the performance of frequency recognition and SSVEP-BR identification is proposed. Correlation features were extracted by filter bank canonical correlation analysis (FBCCA) and synchronization features by multivariate synchronization index (MSI) before being integrated. The performance of correlation features extracted by task-related component analysis (TRCA) were also evaluated. The novel integrated method was compared with FBCCA, MSI and TRCA for performance in frequency recognition and SSVEP-BR identification. The novel integrated method achieved higher classification accuracy than FBCCA and MSI for benchmark datasets when the sliding window was used for EEG data. However, the accuracy of TRCA was not stable when the sliding window was used. The novel integrated method produced an improvement in SSVEP-BR identification over FBCCA and MSI in the blocking dataset. TRCA was not found to be effective for SSVEP-BR identification. A novel integrated method is proposed which gives higher classification accuracy and more stable performance than FBCCA, MSI and TRCA for frequency recognition and SSVEP-BR identification when the EEG data sliding window was used and shows superior performance for the BCI paradigm based on SSVEP and SSVEP-BR.

Enabling temporal–spectral decoding in multi-class single-side upper limb classification

This manuscript presents a novel approach for decoding pre-movement patterns from brain signals using a two-stage-training temporal–spectral neural network (TTSNet). The TTSNet employs a combination of filter bank task-related component analysis (FBTRCA) and convolutional neural network (CNN) techniques to enhance the classification of single-upper limb movements in non-invasive brain–computer interfaces (BCIs).In our previous work, we introduced the FBTRCA method which utilized filter banks and spatial filters to handle spectral and spatial information, respectively. However, we observed limitations in the temporal decoding phase, where correlation features failed to effectively utilize temporal information because of misaligned onset and noisy spikes. To address this issue, our proposed method focuses on analyzing multi-channel signals in the temporal–spectral domain. The TTSNet first divides the signals into various filter banks, employing task-related component analysis to reduce dimensionality and eliminate noise, respectively. Subsequently, a CNN is employed to optimize the temporal characteristics of the signals and extract class-related features. Finally, the class-related features from all filter banks are concatenated and classified using the fully connected layer.To evaluate the effectiveness of our proposed method, we conducted experiments on two publicly available datasets. In binary classification tasks, the TTSNet achieved an improved accuracy of 0.7707 ± 0.1168, surpassing the performance of EEGNet (accuracy: 0.7340 ± 0.1246) and FBTRCA (accuracy: 0.7487 ± 0.1250). In multi-class tasks, TTSNet achieved an accuracy of 0.4588 ± 0.0724, exhibiting a 4.27% and 3.95% accuracy increase over EEGNet and FBTRCA, respectively.The findings of this study suggest that the proposed TTSNet method holds promise for detecting limb movements and assisting in the rehabilitation of stroke patients. The classification of single-side limb movements is expected to facilitate the interaction between patients and external environment by increasing the number of control commands in BCIs.

Effective Technique for Face Recognition System Using CNN in Attendance Applications

Face recognition is a personal identification system that uses individual characteristics of a person to recognize the person's identity. The software which is used for face recognition is MATLAB. The project area is image processing. Different types of ordinal measures are derived from magnitude and phase components of Gabor images. Two orientations and two wavelengths are used. Gabor filters are used in the pre-processing step. Array of Gabor filters is called as filter bank. Convolutional Neural Network (CNN) is used for face recognition, which outperforms the traditional techniques. It has three layers in which Convolution Layer is used for feature extraction. Pooling Layer is used, which crops the particular part of the image containing the object and then resizes it, because classification requires fixed size of an image. SoftMax Layer is used to normalize the un-normalized vectors and make the predictions into the probabilistic forms. Loss function is used to figure out the error. Finally, the face of a student is recognized by using CNN.

A Novel 4 × 1 MISO-VLC System with FBMC-OQAM Downlink Signals

A novel visible-light communication (VLC) system with 4 × 1 multi-input–single-output (MISO) channels is designed. In the system, the filter bank multicarrier (FBMC) and offset quadrature amplitude modulation (OQAM) techniques are used to generate downlink signals. The principles and implementation methods are proposed and analyzed, and the light intensity and received light power distribution of four LED emitters are discussed. The results demonstrate that it not only satisfies the requirements of indoor information access but also provides daily lighting. The used FBMC-OQAM signals exhibit better reception performance than orthogonal frequency division multiplexing (OFDM) signals. The system used has a lower bit error rate (BER) and larger access bandwidth compared to a 1 × 1 single-input–single-output (SISO) system. It has the potential for application advantages in future indoor VLC system applications.

Channel estimation techniques based on intrinsic interference mitigation for FBMC/OQAM systems

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) emerges as a noteworthy alternative to orthogonal frequency division multiplexing (OFDM). FBMC/OQAM addresses the limitations of OFDM by utilizing real-valued, even-symmetrical prototype filters with smaller side lobes and faster spectrum decay. This results in lower out-of-band energy and higher spectral efficiency (SE). However, the orthogonality condition of FBMC/OQAM in the real domain introduces an intrinsic interference (II) component that poses challenges for channel estimation (CE). The conventional straightforward OFDM CE techniques cannot be applied directly. To obtain a precise CSI with the FBMC/OQAM system, Kalman filter (KF) is altered and incorporated into the use of AP and coding. The first proposed method, KalCod, employs a data spreading method to diminish the II effect. The simulation results indicate that KalCod has a superior capacity to the other CE methods, while still maintaining a reasonable level of complexity. In the second proposed method, modified Kalman-auxiliary pilot (KAP), an AP is used to eliminate the II at pilot symbols then updating the channel coefficients using modified KF. When used as a CE method for FBMC/OQAM systems, the modified KAP outperforms its competitors in terms of estimation accuracy, simpler implementation, as well as higher throughput and spectral efficiency.

Creation of a diverse mixed-lingual emotional speech corpus with a framework for enhanced emotion detection

Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition.

Convolutional Neural Networks to Facilitate the Continuous Recognition of Arabic Speech with Independent Speakers

Automatic speech recognition (ASR) is a field of research that focuses on the ability of computers to process and interpret speech feedback from humans and to provide the highest degree of accuracy in recognition. Speech is one of the simplest ways to convey a message in a basic context, and ASR refers to the ability of machines to process and accept speech data from humans with the greatest degree of accuracy. As the human-to-machine interface continues to evolve, speech recognition is expected to become increasingly important. However, the Arabic language has distinct features that set it apart from other languages, such as the dialect and the pronunciation of words. Until now, insufficient attention has been devoted to continuous Arabic speech recognition research for independent speakers with a limited database. This research proposed two techniques for the recognition of Arabic speech. The first uses a combination of convolutional neural network (CNN) and long short-term memory (LSTM) encoders, and an attention-based decoder, and the second is based on the Sphinx-4 recognizer, which includes pocket sphinx, base sphinx, and sphinx train, with various types and number of features to be extracted (filter bank and mel frequency cepstral coefficients (MFCC)) based on the CMU Sphinx tool, which generates a language model for different sentences spoken by different speakers. These approaches were tested on a dataset containing 7 hours of spoken Arabic from 11 Arab countries, covering the Levant, Gulf, and African regions, which make up the Arab world, and achieved promising results. CNN-LSTM achieved a word error rate (WER) of 3.63% using 120 features for filter bank and 4.04% WER using 39 features for MFCC, respectively, while the Sphinx-4 recognizer technique achieved 8.17% WER and an accuracy of 91.83% using 25 features for MFCC and 8 Gaussian mixtures, respectively, when tested on the same benchmark dataset.

Frequency tunable magnetostatic wave filters with zero static power magnetic biasing circuitry

A single tunable filter simplifies complexity, reduces insertion loss, and minimizes size compared to frequency switchable filter banks commonly used for radio frequency (RF) band selection. Magnetostatic wave (MSW) filters stand out for their wide, continuous frequency tuning and high-quality factor. However, MSW filters employing electromagnets for tuning consume excessive power and space, unsuitable for consumer wireless applications. Here, we demonstrate miniature and high selectivity MSW tunable filters with zero static power consumption, occupying less than 2 cc. The center frequency is continuously tunable from 3.4 GHz to 11.1 GHz via current pulses of sub-millisecond duration applied to a small and nonvolatile magnetic bias assembly. This assembly is limited in the area over which it can achieve a large and uniform magnetic field, necessitating filters realized from small resonant cavities micromachined in thin films of Yttrium Iron Garnet. Filter insertion loss of 3.2 dB to 5.1 dB and out-of-band third order input intercept point greater than 41 dBm are achieved. The filter’s broad frequency range, compact size, low insertion loss, high out-of-band linearity, and zero static power consumption are essential for protecting RF transceivers from interference, thus facilitating their use in mobile applications like IoT and 6 G networks.

Novel Orthogonal Time Frequency Space Based on Universal Filter Multi Carrier for 6G Wireless Networks

Obtaining an efficient multicarrier signalling technique is an essential part in signal processing of the (6G) wireless networks. In order to achieve their intended objectives, the 6G mobile networks needs the utilization of a multicarrier scheme that possesses the ability to withstand multipath fading and inter-symbol interference (ISI), as well as exhibit resilience against inter-carrier interference (ICI) and Doppler spread. In recent research, several filter-based methods have been suggested, including Filter Bank Multicarrier (FBMC), as well as various Orthogonal Frequency Division Multiplexing (OFDM) based techniques such as Universal Filtered Multicarrier (UFMC). Orthogonal Time-Frequency Space (OTFS) technique has been identified as a promising candidate waveform for 6G, particularly in high-mobility scenarios, where it has demonstrated superior performance compared to traditional OFDM-based techniques. The OTFS method offers an advantage in that it can be considered the pre-and post-processing stages for techniques based on OFDM. This papaer presents the design (OTFS) using (UFMC) technique. Additionally, a novel structure is proposed for coded/uncoded UFMC-based OTFS technique, which combines the advantages of both strategies while mitigating their limitations. The results of our analysis demonstrate that the UFMC-based OTFS system exhibits superior performance in terms of error probability and time-spectral efficiency compared to the OFDM-based OTFS system.

Reconfigurable multi-channel microwave photonic receiver for a multi-band signal based on cascaded microring resonator banks

What we believe to be a novel reconfigurable multi-channel microwave photonic (MWP) receiver for multi-band RF signal is demonstrated for the first time, to the best of our knowledge. A reconfigurable MWP signal processing chip based on two cascaded microring filter banks is employed in the proposed receiver, which slices the multi-band RF input into several narrow band signals and selects optical frequency comb lines for frequency converting of each channel. Due to the significant reconfigurability of the signal processing chip, the proposed receiver can flexibly choose the output frequency band of each channel, and thus different frequency components of the multi-band RF input can be down converted to the intermediate frequency (IF) band for receiving or converted to other frequency band for forwarding. A multi-band RF signal composed of a linear frequency modulation (LFM) signal with 2 GHz bandwidth and a quad-phase shift keyed (QPSK) signal with 100 Mbit/s rate is experimentally received and reconstructed by the proposed receiver, where the reconstructed LFM component exhibits a signal to noise ratio (SNR) of 10.2 dB, and the reconstructed QPSK component reaches a high SNR of 26.1 dB and a great error vector magnitude (EVM) of 11.73%. On the other hand, the QPSK component of the multi-band RF signal centered at 13.5 GHz is successfully converted to 3.1 GHz.

A new design scheme of two-channel graph filter bank

The existing designs of a two-channel graph filter bank (2c-graph-FB) are polynomial-based and have the numerical calculation problem when designing high performance/order 2c-graph-FB. In this study, a new design scheme is proposed to overcome the numerical calculation problem. The basic idea is adopting the expression form of the traditional symmetric finite impulse response (FIR) filter's frequency response as that of the frequency-domain graph filter of a 2c-graph-FB. Further, for solving the contradiction between the order and the vertex-domain localization in the existing designs as well as the proposed design, the idea of approximate vertex-domain localization is proposed. The designed 2c-graph-FB via our proposed scheme can have strict or approximate vertex-domain localization property. The simulation results reveal that the proposed scheme has no numerical calculation problem. Moreover, compared with the existing designs on the condition of all being able to achieve exceedingly small reconstruction error, the proposed design with the strict localization property has superior frequency selectivity. Among the existing designs (also including the proposed design with strict or approximate localization property) which can achieve exceedingly small reconstruction error, the proposed design scheme with the approximate localization property performs best in terms of the frequency selectivity while having certain localization property.