Convergence analysis of the variable weight mixed-norm LMS-LMFadaptive algorithm

In this work, the convergence analysis of the variable weight mixed-norm LMS-LMF (least mean squares-least mean fourth) adaptive algorithm is derived. The proposed algorithm minimizes an objective function defined as a weighted sum of the LMS and LMF cost functions where the weighting factor is time varying and adapts itself so as to allow the algorithm to keep track of the variations in the environment. Sufficient and necessary conditions for the convergence of the algorithm are derived. Furthermore, bounds on the step size to ensure convergence of the LMF algorithm are also derive

IFN-λ3, not IFN-λ4, likely mediates IFNL3-IFNL4 haplotype-dependent hepatic inflammation and fibrosis

Genetic variation in the IFNL3-IFNL4 (interferon-λ3-interferon-λ4) region is associated with hepatic inflammation and fibrosis. Whether IFN-λ3 or IFN-λ4 protein drives this association is not known. We demonstrate that hepatic inflammation, fibrosis stage, fibrosis progression rate, hepatic infiltration of immune cells, IFN-λ3 expression, and serum sCD163 levels (a marker of activated macrophages) are greater in individuals with the IFNL3-IFNL4 risk haplotype that does not produce IFN-λ4, but produces IFN-λ3. No difference in these features was observed according to genotype at rs117648444, which encodes a substitution at position 70 of the IFN-λ4 protein and reduces IFN-λ4 activity, or between patients encoding functionally defective IFN-λ4 (IFN-λ4-Ser70) and those encoding fully active IFN-λ4-Pro70. The two proposed functional variants (rs368234815 and rs4803217) were not superior to the discovery SNP rs12979860 with respect to liver inflammation or fibrosis phenotype. IFN-λ3 rather than IFN-λ4 likely mediates IFNL3-IFNL4 haplotype-dependent hepatic inflammation and fibrosis

Noise Cancellation using Selectable Adaptive Algorithm for Speech in Variable Noise Environment

Some of the teething problems associated in the use of two-sensor noise cancellation systems are the nature of the noise signals—a problem that imposes the use of highly complex algorithms in reducing the noise. The usage of such methods can be impractical for many real time applications, where speed of convergence and processing time are critical. At the same time, the existing approaches are based on using a single, often complex adaptive filter to minimize noise, which has been determined to be inadequate and ineffective. In this paper, a new mechanism is proposed to reduce background noise from speech communications. The procedure is based on a two-sensor adaptive noise canceller that is capable of assigning an appropriate filter adapting to properties of the noise. The criterion to achieve this is based on measuring the eigenvalue spread based on the autocorrelation of the input noise. The proposed noise canceller (INC) applies an adaptive algorithm according to the characteristics of the input signal. Various experiments based on this technique using real-world signals are conducted to gauge the effectiveness of the approach. Initial results illustrated the system capabilities in executing noise cancellation under different types of environmental noise. The results based on the INC technique indicate fast convergence rates; improvements up to 30 dB in signal-to-noise ratio and at the same time shows 65% reduction of computational power compared to conventional method

A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

We introduce an efficient hidden Markov model-based voice activity detection (VAD) algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2)

A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

<p/> <p>We introduce an efficient hidden Markov model-based voice activity detection (VAD) algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2).</p

A Recursive Least-Squares Extension of the Natural Gradient Algorithm for Blind Signal Separation of Audio Mixtures

this paper. 2. EXISTING MAXIMUM-LIKELIHOOD BASED ALGORITHMS Let N i n s i , , 2 , 1 ), ( K = be scalar inputs (or sources) to the blind signal separation model at a time n . For simplicity, it is assumed that the mixing is linear and that the mixing matrix is square, i.e. the number of inputs N is equal to the number of mixtures N i n x i , , 2 , 1 ), ( K = . Therefore, the mixing matrix A is a square matrix of size N N . The mixing model can be expressed as: ) ( ) ( n n s A x = (1). The mixture x is then applied to a whitening matrix V . The resulting whitened mixtures in z are expressed as: ) ( ) ( ) ( ) ( n n n n s B s A V x V z = = = (2), where B is the resulting mixing matrix after the whitening stage. The purpose of the blind signal separation algorithms is to estimate a matrix W such that N N = I B W , where I is an identity matrix. Then the outputs of the separation process referred to as ) (n y would be identical to the source inputs ) (n s . Maximum Likelihood targets a separation via increasing the likelihood between the outputs ) (n i y and the inputs ) (n i s [5]. In the case of pre-whitened inputs, the cost function of the log-likelihood ) (W L of the de-mixing matrix W can be expressed as: # # # # # # # # # = # i i i p E L ) ( log ) ( z w W (3), where {} E refers to the expected value, i w is the th i row of the matrix W and () i p is a probability density function. The above cost function has the gradient ) (W L # as: # # # # # # # # # = = # # i T E L ) ( g log ) ( z y W (4), where i p y g = ) ( and is usually set to ) tanh( 2 i y for supergaussian data, such as audio data. Pre-whitening also constrains the matrix W to be orthogonal, meaning that N N = I W W . This constraint places the optimization of the cost func..