Multichannel Linear Prediction Research Articles

Blind MMSE Equalization of SISO IIR Channels Using Oversampling and Multichannel Linear Prediction

In this correspondence, a novel linear prediction based blind equalization algorithm for Single Input Single Output (SISO) IIR channels is proposed. The new algorithm is based on second-order statistics and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special Single Input Multiple Output (SIMO) model. Two forward linear predictors with consecutive prediction steps are applied to the subchannel outputs of the SIMO model. It is proved that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive Minimum-Mean-Square-Error (MMSE) equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

Joint DOA Estimation and Dereverberation Based on Multi-Channel Linear Prediction Filtering and Azimuth Sparsity

A tensor decomposition based multichannel linear prediction approach to speech dereverberation

Dereverberation technology is needed in a wide range of speech applications as reverberation often greatly degrades the quality and intelligibility of the speech signal of interest captured by microphones. The commonly used weighted-prediction-error method generally requires long prediction-error filters to remove the reverberation components, which makes it computationally expensive. To deal with this issue, this paper proposes a computationally efficient dereverberation algorithm based on tensor decomposition in which the long prediction-error filter is decomposed into a group of short sub-filters through multiple Kronecker products. Consequently, the high dimensional cross-correlation matrix that needs to be inverted in the dereverberation algorithm is then converted into a set of low dimensional matrices, which leads to significant reduction in the computational complexity. Simulation results demonstrate the properties of the proposed algorithm.

Parallel processing of distributed beamforming and multichannel linear prediction for speech denoising and deverberation in wireless acoustic sensor networks

More and more smart home devices with microphones come into our life in these years; it is highly desirable to connect these microphones as wireless acoustic sensor networks (WASNs) so that these devices can be better controlled in an enclosure. For indoor applications, both environmental noise and room reverberation may severely degrade speech quality, and thus both of them need to be removed to improve users’ experience. For this goal, this paper proposes a parallel processing framework of distributed beamforming and multichannel linear prediction (DB-BFMCLP), which consists of generalized sidelobe canceler and multichannel linear prediction for simultaneous speech dereverberation and noise reduction in WASNs. By sharing a common desired response vector, the proposed DB-BFMCLP can provide a significant reduction in communication bandwidth without sacrificing performance. The convergence guarantee of the DB-BFMCLP to its centralized implementation is derived mathematically. Simulation results verify the superiority of the proposed method to the existing related methods in noisy and reverberant scenarios.

Effective Dereverberation with a Lower Complexity at Presence of the Noise

Adaptive beamforming and deconvolution techniques have shown effectiveness for reducing noise and reverberation. The minimum variance distortionless response (MVDR) beamformer is the most widely used for adaptive beamforming, whereas multichannel linear prediction (MCLP) is an excellent approach for the deconvolution. How to solve the problem where the noise and reverberation occur together is a challenging task. In this paper, the MVDR beamformer and MCLP are effectively combined for noise reduction and dereverberation. Especially, the MCLP coefficients are estimated by the Kalman filter and the MVDR filter based on the complex Gaussian mixture model (CGMM) is used to enhance the speech corrupted by the reverberation with the noise and to estimate the power spectral density (PSD) of the target speech required by the Kalman filter, respectively. The final enhanced speech is obtained by the Kalman filter. Furthermore, a complexity reduction method with respect to the Kalman filter is also proposed based on the Kronecker product. Compared to two advanced algorithms, the integrated sidelobe cancellation and linear prediction (ISCLP) method and the weighted prediction error (WPE) method, which are very effective for removing reverberation, the proposed algorithm shows better performance and lower complexity.

VLSI Implementation of Multi-Channel ECG Lossless Compression System

Electrocardiogram (ECG) is used to record the electrical activity of heart. If the instrument monitoring the signal for a long time, it will produce large amount of data. So, the effective lossless ECG compression system can help to reduce the storage space. This brief presents a hardware architecture of multi-channel lossless ECG compression system. The system is based on the algorithm including multi-channel linear prediction and adaptive linear prediction. Also, Golomb rice code (GRC) is used for entropy coding. The hardware implementation has been designed with low hardware complexity usage while using optimum hardware resources. And the architecture can process multiple channels in parallel that can obtain the high throughput. PTB database has been used for verification and testing purposes. This design was implemented in TSMC 180nm. The implementation results show the gate count is 476K and power consumption is 69.18μW while working frequency is 1 KHz.

Split Bregman Approach to Linear Prediction Based Dereverberation With Enforced Speech Sparsity

The recordings of speech in enclosures are corrupted by reverberation caused by multipath wave propagation from the speaker to the distant microphones. In this letter, we address the problem of reducing the late part of room reverberation. The presented blind dereverberation method consists in multichannel linear prediction (MCLP) and enforces sparsity of the dereverberated speech by adopting the split Bregman approach. The proposed algorithm alternately solves two optimization problems, where the former cost function is derived by assuming that speech is modelled using a sparse prior distribution, while the latter optimization emphasizes speech sparsity by an additional incorporation of a weighted $\ell _1$ -norm of the output signal to the standard linear prediction based cost function. The results of experiments performed using simulated and measured room impulse responses for various reverberation time values indicate superior performance of the proposed sparse split Bregman (SSB) method over state-of-the-art non-sparse and sparse MCLP-based dereverberation methods in terms of standard evaluation measures and as pre-processsing to the automatic speech recognition.

Online Speech Dereverberation Using Mixture of Multichannel Linear Prediction Models

We extend the state-of-the-art online dereverberation method, online weighted prediction error (WPE), which predicts late reverberation components using a multichannel linear prediction (MCLP) filter. The multi-input/output inverse theorem states that in general such an MCLP filter for WPE exists only if the number of sources is less than that of microphones M and there is no additive noise; otherwise, the WPE model has error, degrading its performance especially when M is small. To mitigate this WPE drawback, we recently developed an offline dereverberation method called switching WPE (SwWPE), which has multiple MCLP filters and switches them for each time-frequency bin. We here propose a recursive least squares (RLS) algorithm for the online optimization of SwWPE. Our proposed RLS has forgetting weights for each MCLP filter, and their decay rates are controlled based on a switching mechanism of SwWPE to stably improve the dereverberation performance of the online WPE. Experimental results show that our online SwWPE clearly outperforms online WPE in speech dereverberation tasks.

Robust Dereverberation With Kronecker Product Based Multichannel Linear Prediction

Reverberation impairs not only the speech quality, but also intelligibility. The weighted-prediction-error (WPE) method, which estimates the late reverberation component based on a multichannel linear predictor, is by far one of the most effective algorithms for dereverberation. Generally, the WPE prediction filter in every short-time-Fourier-transform (STFT) subband has to be long enough to estimate accurately the late reverberation component. As a consequence, WPE is computationally expensive, which makes it difficult to implement into real-time embedded or edge computing devices. Moreover, WPE is sensitive to additive noise and its performance may suffer from dramatic degradation even in environments where the signal-to-noise ratio (SNR) is high. To address these drawbacks, this letter proposes to decompose the multichannel linear prediction filter as a Kronecker product of a temporal (interframe) prediction filter and a spatial filter. An iterative algorithm is then developed to optimize the two filters. In comparison with the original WPE algorithm, the presented method not only exhibits better performance in terms of dereverberation and robustness to additive noise, as there are fewer parameters to estimate for a given number of observation signal samples, but is also computationally more efficient, since the dimensions of the covariance matrices after Kronecker product decomposition are smaller.

Speech Dereverberation Based on Improved Wasserstein Generative Adversarial Networks

In reality, the sound we hear is not only disturbed by noise, but also the reverberant, whose effects are rarely taken into account. Recently, deep learning has shown great advantages in speech signal processing. But among the existing dereverberation approaches, very few methods apply deep learning at the waveform level. In addition, in the case of sever reverberation, the conventional speech dereverberation methods perform poorly, such as MCLP (multi-channel linear prediction). We proposed a new speech dereverberation method in this paper, which is based on improved WGAN (Wasserstein Generative Adversarial Networks), called WGAN-GP, whose generator uses strided-convolutional networks and the discriminator is structured on DNNs. Due to the addition of the gradient penalty item, WGAN-GP improves the stability of training and the generalization of the model. In the case of severe reverberation, according to the experimental results, the proposed system can perform better than MCLP. As the proposed method based on WAGN-GP can improve speech quality, speech signal processing systems may be able to apply it to pre-processing stage.

Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction

In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this paper, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly $M^2$ times less expensive than both reference algorithms, where $M$ denotes the number of microphones, it is shown to perform similarly as compared to the former, and to outperform the latter.

Robust Multichannel Linear Prediction for Online Speech Dereverberation Using Weighted Householder Least Squares Lattice Adaptive Filter

Speech dereverberation has been an important component of effective far-field voice interfaces in many applications. Algorithms based on multichannel linear prediction (MCLP) have been shown to be especially effective for blind speech dereverberation and numerous variants have been introduced in the literature. Most of these approaches can be derived from a common framework, where the MCLP problem for speech dereverberation is formulated as a weighted least squares problem that can be solved analytically. Since conventional batch MCLP-based dereverberation algorithms are not suitable for low-latency applications, a number of online variants based on the recursive least squares (RLS) algorithm have been proposed. However, RLS-based approaches often suffer from numerical instability and their use in online systems can further be limited due to high computational complexity with a large number of channels or filter taps. In this paper, we aim to address the issues of numerical robustness and computational complexity. More specifically, we derive alternative online weighted least squares algorithms through Householder RLS and Householder least squares lattice (HLSL), which are numerically stable and retain the fast convergence capability of the RLS algorithm. Furthermore, we derive an angle-normalized variant of the HLSL algorithm and show that it is robust to speech cancellation for a wide range of forgetting factors and filter taps. Finally, we support our findings through experimental results and demonstrate numerical and algorithmic robustness, long-term stability, linear complexity in filter taps, low memory footprint, and effectiveness in speech recognition applications.

Blind speech dereverberation using sparse decomposition and multi-channel linear prediction

In this study, a blind speech dereverberation method in a noiseless single input multiple output acoustic channel is proposed. The method is based on multichannel linear prediction (MCLP) in STFT domain assuming sparsity in both residual speech and channel coefficients. The proposed speech dereverberation algorithm assumes that both the residual speech signal and the linear prediction coefficients is sparse. The optimization was performed by convex optimization using ADMM and CVX. The proposed model was compared with state of the art methods with lp norm optimization criteria. Simulations were evaluated in different room models with various reverberation times, numbers of microphones and parameter adjustments. The results show that the performance of the proposed method is superior in terms of speech dereverberation assessment criteria.

Late Reverberation Cancellation Using Bayesian Estimation of Multi-Channel Linear Predictors and Student's t-Source Prior

Multi-channel linear prediction MCLP can model the late reverberation in the short-time Fourier transform domain using a delayed linear predictor and the prediction residual is taken as the desired early reflection component. Traditionally, a Gaussian source model with time-dependent precision inverse of variance is considered for the desired signal. In this paper, we propose a Student's t-distribution model for the desired signal, which is realized as a Gaussian source with a Gamma distributed precision. Further, since the choice of a proper MCLP order is critical, we also incorporate a Gaussian distribution prior for the prediction coefficients and a higher order. We consider a batch estimation scenario and develop variational Bayes expectation maximization VBEM algorithm for joint posterior inference and hyper-parameter estimation. This has lead to more accurate and robust estimation of the late reverb component and hence its cancellation, benefitting the desired residual signal estimation. Along with these stochastic models, we formulate single channel output MISO and multi channel output MIMO schemes using shared priors for the desired signal precision and the estimated MCLP coefficients at each microphone. Experiments using real room impulse responses show improved late reverberation suppression with the proposed VBEM approach over the traditional methods, for different room conditions. Additionally, we achieve a sparse coefficient vector for the MCLP avoiding the criticality of manually choosing the model order. The MIMO formulation is easily extended to include spatial filtering of the enhanced signals, which further improves the estimation of the desired signal.

Comparative Analysis of Generalized Sidelobe Cancellation and Multi-Channel Linear Prediction for Speech Dereverberation and Noise Reduction

For blind speech dereverberation, two frameworks are commonly used: on the one hand, the multi-channel linear prediction (MCLP) framework, and on the other hand, data-dependent beamforming, e.g., the generalized sidelobe canceler (GSC) framework. The MCLP framework is designed to perform deconvolution and hence has gained increased prominence in blind speech dereverberation. The GSC framework is commonly used for noise reduction, but may be applied for dereverberation as well. In previous work, we have shown that for the noiseless case, MCLP and the GSC yield in theory mathematically equivalent results in terms of dereverberation. In this paper, we assume additional coherent as well as incoherent-noise components and formally analyze and compare both frameworks in terms of dereverberation and noise reduction performance. Both the theoretical analysis and time domain simulation results demonstrate that unlike the GSC, MCLP expectably shows limited performance in terms of noise reduction, while both perform equally well in terms of dereverberation, provided that the GSC blocking matrix achieves complete blocking of the early reverberant-speech component and sufficiently many microphones are available. In case of incomplete blocking, however, the GSC performs inferior to MCLP in terms of dereverberation, as shown in short-time Fourier transform domain simulations.

Multi-channel adaptive dereverberation robust to abrupt change of target speaker position.

Adaptive algorithm based on multi-channel linear prediction (MCLP) is an effective dereverberation method. However, the abrupt change of the target speech source position makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this letter, the recursive-least-squares (RLS)-based and Kalman-filter-based adaptive MCLP method for speech dereverberation are investigated. Based on the relative weighted change of the adaptive filter coefficients, a time-varying forgetting factor for the RLS algorithm and a re-initialization mechanism for the Kalman filter are proposed to make the algorithm robust to the abrupt change of the target speaker positions. The advantages of the proposed scheme are demonstrated in the experiments.

MIMO beamforming system for speech enhancement in realistic environment with multiple noise sources

Multiple noise sources in a realistic environment severely degrade the quality and intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several noise reduction algorithms have been proposed with a main goal to solve this problem. However, the good performances of such algorithms are severely impaired in realistic environment under multi-noise sources condition. In this paper, the author treats the noise cancellation system as a multiple-input multiple-output (MIMO) beamformer system. The proposed approach consists of two steps. First, the noise signals are generated by applying the white noise sources to a MIMO AR system. Then, the noisy microphone signals are sequentially processed by employing multi-channel linear prediction error filters (MCLPEFs) and multi-channel adaptive noise estimation filters (MCANEFs) in the lower path of the proposed beamformer. The MCLPEFs are used to whiten the input signals, while the MCANEFs are used as a MIMO system identification to perform the modeling process of the noise signals. Finally, the noise signals in the upper path are subtracted from the estimated noises in the lower path to recover an enhanced speech signal. Moreover, the performance of the proposed MIMO approach was validated under a realistic environment with real noise sources.

Adaptive Speech Dereverberation Using Constrained Sparse Multichannel Linear Prediction

In this letter, we present an adaptive speech dereverberation method based on constrained sparse multichannel linear prediction (MCLP), minimizing the mixed $\ell _{2,p}$ norm of the desired component. In order to prevent overestimation of the undesired reverberant component, possibly leading to severe distortions of the output, we propose to use a statistical model for late reverberation to limit the power of the MCLP-based estimate. The resulting constrained optimization problem is solved by using the alternating direction method of multipliers, resulting in two variants of the dereverberation algorithm. Simulation results show that the proposed constraint increases the robustness with respect to parameter selection and improves the usability for dynamic scenarios in comparison to the unconstrained method.

Multi-Channel Linear Prediction-Based Speech Dereverberation With Sparse Priors

The quality of speech signals recorded in an enclosure can be severely degraded by room reverberation. In this paper, we focus on a class of blind batch methods for speech dereverberation in a noiseless scenario with a single source, which are based on multi-channel linear prediction in the short-time Fourier transform domain. Dereverberation is performed by maximum-likelihood estimation of the model parameters that are subsequently used to recover the desired speech signal. Contrary to the conventional method, we propose to model the desired speech signal using a general sparse prior that can be represented in a convex form as a maximization over scaled complex Gaussian distributions. The proposed model can be interpreted as a generalization of the commonly used time-varying Gaussian model. Furthermore, we reformulate both the conventional and the proposed method as an optimization problem with an lp-norm cost function, emphasizing the role of sparsity in the considered speech dereverberation methods. Experimental evaluation in different acoustic scenarios show that the proposed approach results in an improved performance compared to the conventional approach in terms of instrumental measures for speech quality.

Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes

This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum. We also apply the method to a music information retrieval task and demonstrate its effectiveness.