Adaptive Comb Filter Research Articles

Neural Comb Filtering Using Sliding Window Attention Network for Speech Enhancement

In this paper, we demonstrate the significance of restoring harmonics of the fundamental frequency (pitch) in the deep neural network (DNN)-based speech enhancement. The parameters of the DNN can be estimated by minimizing the mask loss, but it does not restore the pitch harmonics, especially at higher frequencies. In this paper, we propose to restore the pitch harmonics in the spectral domain by minimizing cepstral loss around the pitch peak. Restoring the cepstral pitch peak, in turn, helps in restoring the pitch harmonics in the enhanced spectrum. The proposed cepstral pitch-peak loss acts as an adaptive comb filter on voiced segments and emphasizes the pitch harmonics in the speech spectrum. The network parameters are estimated using a combination of mask loss and cepstral pitch-peak loss. We show that this combination offers the complementary advantages of enhancing both the voiced and unvoiced regions. The DNN-based methods primarily rely on the network architecture, and hence, the prediction accuracy improves with the increasing complexity of the architecture. The lower complex models are essential for real-time processing systems. In this work, we propose a compact model using a sliding-window attention network (SWAN). The SWAN is trained to regress the spectral magnitude mask (SMM) from the noisy speech signal. Our experimental results demonstrate that the proposed approach achieves comparable performance with the state-of-the-art noncausal and causal speech enhancement methods with much lesser computational complexity. Our three-layered noncausal SWAN achieves 2.99 PESQ on the Valentini database with only \(10^9\) floating-point operations (FLOPs).

Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control

For reliable speech recognition, it is necessary to handle the usage environments. In this study, we target voice-driven multi-unmanned aerial vehicles (UAVs) control. Although many studies have introduced several systems for voice-driven UAV control, most have focused on a general speech recognition architecture to control a single UAV. However, for stable voice-controlled driving, it is essential to handle the environmental conditions of UAVs carefully, including environmental noise that deteriorates recognition accuracy, and the operating scheme, e.g., how to direct a target vehicle among multiple UAVs and switch targets using speech commands. To handle these issues, we propose an efficient vehicle-embedded speech recognition front-end for multi-UAV control via voice. First, we propose a noise reduction approach that considers non-stationary noise in outdoor environments. The proposed method improves the conventional minimum mean squared error (MMSE) approach to handle non-stationary noises, e.g., babble and vehicle noises. In addition, we propose a multi-channel voice trigger method that can control multiple UAVs while efficiently directing and switching the target vehicle via speech commands. We evaluated the proposed methods on speech corpora, and the experimental results demonstrate that the proposed methods outperform the conventional approaches. In trigger word detection experiments, our approach yielded approximately 7%, 12%, and 3% relative improvements over spectral subtraction, adaptive comb filtering, and the conventional MMSE, respectively. In addition, the proposed multi-channel voice trigger approach achieved approximately 51% relative improvement over the conventional approach based on a single trigger word.

A Null Space-Based Blind Source Separation for Fetal Electrocardiogram Signals.

This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different additive signal-to-noise ratio (SNR) increasing from 0 dB to 12 dB, and considering different fetal-to-maternal SNR increasing from −30 dB to 0 dB. The FECG detection of the NSITM is evaluated using statistical measures and results show considerable improvement in the sensitivity (SE), the accuracy (ACC), and the positive predictive value (PPV), as compared with other algorithms. The study demonstrated that the NSITM is a feasible algorithm for FECG extraction.

A Robust Tool for Monitoring and Synchronizing Smart Grid through Adaptive Comb Filter

The power system signals are often polluted with harmonics and noise as a result of nonlinear load. This non stationary signal has to be monitored carefuly before it propagates as a grid problem.This article describes the design of adaptive comb filter, extraction of amplitude, frequency and phase with respect to time for monitoring purpose and extraction of harmonic components for suppressing the contamination present in the signal for synchronization with smart grid. The adaptive comb filter algorithm is a synchronizing tool implemented in Matlab/Simulink environment.The response of the filter is compared with enhanced phased locked loop to describe the characteristics of adaptive comb filter.The algorithm tracks the transient (dynamic) and steady state behaviour of the signal effectively, efficiently and accurately.

Heart Rate Monitoring Using a Slow–Fast Adaptive Comb Filter to Eliminate Motion Artifacts

This paper proposes an algorithm for eliminating motion artifacts in heart rate monitoring using a photoplethysmography (PPG) sensor. No additional hardware, such as accelerometers or other reference signals, is needed. The algorithm improves the quality of PPG signals by implementing a comb filter during signal processing. The proposed slow–fast adaptive comb filter (SFACF) distinguishes motion artifacts in PPG signals based on harmonic and continuous features of the heart rate. The effectiveness of fundamental and harmonic frequency enhancement in real time is experimentally demonstrated, with the heart rate updated every second. The performance of the SFACF, which does not require additional reference signals, is compared with those of adaptive noise cancellation (ANC) and spectral peak search-comb filtering (SPS-CF). It is found that for a low-quality PPG signal, the SFACF outperforms ANC and SPS-CF. Finally, measurements obtained using a commercial heart rate monitor and the proposed algorithm are shown to be highly correlated.

Emotional information processing based on feature vector enhancement and selection for human–computer interaction via speech

This paper proposes techniques for enhancement and selection of emotional feature vectors to correctly process emotional information from users' spoken data. In real-world devices, speech signals may contain emotional information that is distorted or anomalous owing to environmental noises and the acoustic similarities between emotions. To correctly enhance harmonics of the noise-contaminated speech and thereby utilize them as emotional features, we propose a modified adaptive comb filter, in which the frequency response of the conventional comb filter is re-estimated on the basis of speech presence probability. In addition, to eliminate acoustically anomalous emotional data, we propose a feature vector classification scheme. In this approach, emotional feature vectors are categorized as either discriminative or indiscriminative in an iterative manner, and then only the discriminative vectors are selected for emotional information processing. In emotion recognition experiments using noise-contaminated emotional speech data, our approach exhibited superior performance over the conventional approaches.

A Simple Method for Power System Harmonic Analysis

With the increasing use of non-linear electrical loads in power system, the harmonic pollution has become a severe problem. In this paper, we proposed a simple method for power system harmonic analysis. Cascaded adaptive comb filter is designed specifically to suppress the electric harmonic pollution. And a high-precision but easily implemented frequency estimation algorithm is presented based on least square estimation method. So the electric signal frequency fluctuation can be real-time tracked, and coefficients of the comb filters can be adaptively updated with this algorithm.

Unsupervised noise reduction scheme for voice-based information retrieval in mobile environments

This study proposes an unsupervised noise reduction scheme that improves the performance of voice-based information retrieval tasks in mobile environments. Various types of noises could interfere with speech processing tasks, and noise reduction has become an essential technique in this field. In particular, noise reduction needs to be carefully processed in mobile environments based on the speech coding system and the client-server architecture. In this study, we propose an effective noise reduction scheme that employs the adaptive comb filtering technique. A way of directly using several codec parameters during the filtering process is also investigated. In particular, we modify the conventional comb filter using line spectral pair parameters. To verify the efficiency of the proposed noise reduction approach, we conducted speech recognition experiments using the Aurora2 database. Our approach provided superior recognition performance under various noise conditions compared to the conventional techniques.

Noninvasive fetal ECG estimation using adaptive comb filter

This paper describes a robust and simple algorithm for fetal electrocardiogram (FECG) estimation from abdominal signal using adaptive comb filter (ACF). The ACF can adjust itself to the temporal variations in fundamental frequency, which makes it qualified for the estimation of quasi-periodic component from physiologic signal, such as ECG. The validity and performance of the described method are confirmed through experiments on real fetal ECG data. A comparison with the well-known independent component analysis (ICA) method has also been presented.

Compressed domain speech enhancement method based on ITU-T G.722.2

Based on the bit-stream of ITU-T G.722.2 speech coding standard, through the modification of codebook gains in the codec, a compressed domain speech enhancement method that is compatible with the discontinuous transmission (DTX) mode and frame erasure condition is proposed in this paper. In non-DTX mode, the Voice Activity Detection (VAD) is carried out in the compressed domain, and the background noise is classified into full-band distributed noise and low-frequency distributed noise. Then, the noise intensity is estimated based on the algebraic codebook power, and the a priori SNR is estimated according to the noise type. Next, the codebook gains are jointly modified under the rule of energy compensation. Especially, the adaptive comb filter is adopted to remove the residual noise in the excitation signal in low-frequency distributed noise. Finally, the modified codebook gains are re-quantized in speech or excitation domain. For non-speech frames in DTX mode, the logarithmic frame energy is attenuated to remove the noise, while the spectral envelope is kept unchanged. When frame erasure occurs, the recovered algebraic codebook gain is exponentially attenuated, and based on the reconstructed algebraic codebook vector, all the codec parameters are re-quantized to form the error concealed bit-stream. The result of performance evaluation under ITU-T G.160 shows that, with much lower computational complexity, better noise reduction, SNR improvement, and objective speech quality performances are achieved by the proposed method comparing with the state-of-art compressed domain methods. The subjective speech quality test shows that, the speech quality of the proposed method is better than the method that only modifies the algebraic codebook gain, and similar to the one with the assistance of linear domain speech enhancement method.

Stability Analysis of Normalized Frequency Adaptive Comb Filter

A normalized frequency adaptive comb filter composed of a number of normalized frequency estimators in parallel is proposed to track unknown frequency and unknown amplitude of each component of an almost periodic signal. The filter consists of two coupled nonlinear differential equations respectively for updating frequencies and estimating state variables. A nonlinear autonomous equation for frequency estimation is deduced after average method is applied to the almost periodic dynamic system resulting from system decoupling by slow integral manifold. Three kinds of local stability of the autonomous system:the exponential stability of isolated equilibrium point,the semistability on center manifold,and the robustness under unknown periodic disturbance are proved. The convergence and the boundedness of amplitude estimation and signal tracking as well as the effect of parameters on the transient and steady-state performance of frequency and amplitude are investigated. Each sinusoidal component and its amplitude are accurately tracked on the condition of given intervals instead of the values of frequencies. The response speed is independent of the component amplitude. Simulation results reveal the validity of the proposed algorithm.

Neural timing nets for fundamental frequency (F0)-based auditory scene analysis.

Neural timing nets are idealized networks of delay lines, coincidence detectors, and adaptive processing elements that operate in the time domain on temporally coded signals to compare, extract, and separate auditory objects [Cariani, Neural Networks 14, 737–753 (2001); J. New Music Res. 30, 107–135; IEEE Trans. Neural Netw. 15(5) (2004)]. Timing nets constitute an alternative, potential mode of neural signal processing in which information resides in neural signals rather than in patterns of activated elements. Recurrent timing nets with delay loops act as dense arrays of recursive, comblike filters to effect a period-by-period analysis that builds up and separates component invariant time patterns with different fundamentals (F0s). Using both linear and nonlinear processing rules, the latter were used to process and separate synthetic double vowels, running speech, and polyphonic musical excerpts, with varying results. Relations to, and combinations with, processing strategies based on autocorrelation and all-order interspike intervals, adaptive comb filtering, correlogram duplex analysis, cancellation, Fourier zero-crossings, and demodulation, with and without prior bandpass filtering, are discussed. Bottom-up/top-down mechanisms for dynamic facilitation of lower level temporal processing loops are also considered. [Work supported by AFOSR FA9550-09-1-0119.]

System for noise suppression, transceiver and method for noise suppression

A system with an accompanying method is provided to improve the signal-to-noise ratio (SNR) of noisy speech by suppressing acoustic background noise. The background noise consists of narrow band noise from rotating machine, audio signals from stereo-loudspeakers of audio entertainment device, and other ambient noise. In this system/apparatus, a microphone senses the speech intermingled with the background noise, and another microphone senses the noisy background. In addition, a measurement sensor is used to measure RPM (revolutions-per-minutes) of the rotating machine and two wires are used to acquire audio signals from the stereo-loudspeakers of the audio entertainment device. Furthermore, to provide better suppression performance for the acoustic audio signals, the characteristics of these loudspeakers are used to compensate for the distortion caused by the loudspeakers. Adaptive comb filters and adaptive FIR filters are applied to estimate the ambient noise and suppress the background noise. After processing, the system outputs the enhanced speech signal with higher SNR.

Generalized adaptive notch and comb filters for identification of quasi-periodically varying systems

The problem of identification/tracking of quasi-periodically varying real-valued systems is considered. This problem is a generalization, to the system case, of a classical signal processing task of either elimination or extraction of nonstationary sinusoidal signals buried in noise. The solution is based on the exponentially weighted basis function (EWBF) approach. The proposed algorithms are capable of tracking slow changes in system frequencies, which means that not only the expansion coefficients in the basis function description of the analyzed system but also the basis functions themselves are adjusted in an adaptive manner. First, assuming that the system frequencies are known and constant, the running basis and fixed basis variants of the EWBF algorithm are derived, and their relationship to the classical notch filter with constrained poles and zeros is established. Next, the frequency-adaptive versions of both algorithms are obtained using the gradient search and recursive prediction error principles, respectively. Finally, the interrelated frequencies case is analyzed and two additional parameter tracking algorithms (generalized adaptive comb filters) are derived.

New method for the implementation of an NTSC digital video decoder

This paper presents a new method for the implementation of an NTSC digital video decoder. This new fully digital architecture uses a phase-locked loop to compensate the unstable video source and an adaptive comb filter to improve the quality of Y/C separation. Simulation results show that it can successfully combat a sinusoidal timing variation of /spl plusmn/2 /spl mu/s for horizontal line lengths within one frame. It can also lock to a 3.58 MHz color subcarrier with frequency variation up to /spl plusmn/500 Hz.

Frequency-domain periodic active noise control and equalization

This paper analyzes a frequency-domain periodic active noise control (ANC) system. The adaptive filter employs the frequency domain complex least mean square (LMS) algorithm driven by a unit value at each frequency bin. The synchronously sampled frequency-domain adaptive structure acts as a comb filter to cancel narrowband noises with harmonically related frequencies. A frequency-domain periodic active noise equalization (ANE) system, which reshapes the residual noise by controlling the output of the adaptive comb filter at each frequency bin, is also developed and analyzed in this paper. Computer simulations and real-time experiments conducted attest to the practical usefulness of the proposed ANC and ANE systems.

Adaptive comb-filtering using neural networks

Neural networks of multi-layer perceptron type perform well as adaptive comb-filters for PAL and NTSC color decoding. They are optimized by learning algorithms. Sampled encoded and original images serve as training patterns.

Adaptive IIR comb filter for harmonic signal cancellation

A novel adaptive infinite impulse response (IIR) comb filter structure for the estimation and elimination of harmonic interference signals is presented. Two filter blocks are presented. The first is an adaptive notch filter implemented by a computationally simple but exact gradient-based algorithm. The adaptive notch filter is used to estimate and track the fundamental frequency of the harmonic series. The second block is a tunable IIR comb filter whose parameters are determined based on the estimate of the fundamental frequency. The purpose of both filter blocks is to effectively cancel the harmonic series. The proposed comb filtering is more robust as well as computationally simpler than the comb filtering technique used by Nehorai and Poraz (1986). Error surface analysis is included to indicate that the error surface characteristics for the proposed solution is a significant improvement when compared to the conventional adaptive comb filter approach used by Nehorai and Poraz (1986). Simulation results are included to demonstrate the performance characteristics of the proposed solution.

An automatic musical frequency detection and MIDI control system for brass instruments

Fundamental frequency detection algorithms optimized for use with instruments in the brass instrument family are presented. A new frequency tracking scheme based on adaptive periodic prediction is presented, and it is shown that this algorithm is equivalent to a high-precision adaptive comb filter. Frequency detection schemes that do not take into account the unique spectral and acoustical properties of a particular instrument family usually generate errors of three types: (1) octave and harmonic detection errors, (2) half-step errors that jitter rapidly about the true estimate, and (3) latency of detection. A frequency detection and live MIDI control system has been constructed for the trumpet, which significantly reduces detection errors and latency. By limiting the search range to the natural playing range of the trumpet, sampling rate and computation can be optimized, reducing latency in the estimates. By measuring and utilizing valve position in the frequency detection algorithm, the half-step jitter problem is completely eliminated, and latency can be further reduced. Schemes for reducing harmonic detection errors will be presented. The trumpet system as implemented will be quantitatively compared to two popularly available frequency to MIDI systems. A short demonstration of the trumpet performance system will be presented.

An adaptive on-line sensor for roll eccentricity measurement and control

This paper is concerned with the problem of roll eccentricity compensation in steel strip rolling mills. A new roll eccentricity sensor is proposed for the task of retrieving the desired reference signal from the available roll force measurement. The sensor implements a modified comb filter structure which can be arranged in either tunable or adaptive form. The comb filter structure consists of a number of second-order infinite impulse response notch filter modules. Error surface analysis is carried out for the adaptive comb filter. Results from this analysis confirms that convergence is guaranteed provided that the initial frequency estimate is selected within the unimodal region. Such a condition is easily satisfied in practice since the fundamental frequency is related to the roll speed. Simulation results based on typical experimental data clearly indicate that the proposed sensor is appropriate and offers distinct advantages over other methods. The final roll eccentricity compensator is based on this new sensor and the functional units making up the complete compensation system are discussed in some detail within the paper.