Time-domain Adaptive Algorithms Research Articles

An adaptive control algorithm for harmonic cancellation in multiple-input multiple-output vibration systems

This paper presents a time-domain adaptive algorithm designed to eliminate harmonics in a Multiple-input Multiple-output (MIMO) system, taking into account its coupling characteristics. The algorithm is an improvement over the X-filtered LMS (FxLMS) algorithm, and is specifically tailored to the triaxial vibration system. The proposed algorithm has been shown to achieve high convergence, even in the presence of identification errors, as confirmed through both theoretical derivation and numerical simulations. To validate the efficacy of the proposed MIMO harmonic cancellation algorithm, it was tested on a genuine triaxial vibration system. The experimental results clearly demonstrate that the algorithm effectively reduces the output harmonic disturbance of the three axes’ frequency components.

A Frequency-Domain Output-Constrained Active Noise Control Algorithm Based on an Intuitive Circulant Convolutional Penalty Factor

Due to their computational efficiency, least mean square (LMS)–based algorithms are still widely utilized to achieve optimal control in active noise control (ANC) applications. Real-world implementation of advanced ANC functionalities, such as selective cancellation of frequencies, is nonetheless hampered by complexity trade-offs, especially with computationally-expensive frequency-domain approaches. Prevailing time-domain adaptive algorithms – proposed to alleviate complexities from transformation – continue to incur increased complexities while constraining the magnitude of frequency bins in the time-domain filters. To address existing complexities in time-domain approaches, this paper proposes a circulant convolutional penalty factor that assists the extended leaky filtered-reference LMS (FxLMS) algorithm in achieving frequency constraint without any frequency-domain transform. This circulant convolutional penalty factor is readily determined by methods for designing finite response filters, such as frequency sampling. Additionally, the coordinate descent method is adopted to further reduce the proposed algorithm's computations, significantly increasing its feasibility for implementation on conventional real-time processors. Finally, the numerical simulations performed on the measured primary and secondary paths demonstrate the effectiveness of the proposed algorithm.

Performance Evaluation of Selected Adaptive Filters for Acoustic Echo Cancelation over Voice over Internet Protocol

Advancement of technology has increased the value of the telecommunications industry. One of the services with the rapid growth is Voice over Internet Protocol (VoIP). However, echo is a common disturbing effect in VoIP calls. Instead of suppressing the presence of echo in the VoIP communication channel, echo cancellation works better in full-duplex mode. This paper compares performances of some selected adaptive filters for cancelation of acoustic echo in VoIP channel. The framework for adaptive echo cancellation is built around finite impulse response and fourth-order infinite impulse response Chebyshev type II filters for the modeling of echo and acoustic environment, respectively. Selected adaptive algorithms for analysis are least mean square, recursive least square and frequency domain adaptive filter. The analysis is carried out in MATLAB 2018a environment. Echo return loss enhancement, error estimate and convergence rate are the performance indices employed for performance comparison of selected adaptive algorithms. It was found that the FDAF algorithm performed best based on computed error return loss enhancement and error estimate figures. In addition, FDAF converges faster than LMS and RLS with average time of 0.026431 s over 2048 filter lengths. Thus, FDAF shows a better promise over the two variants of time domain adaptive algorithm (LMS and RLS) analyzed in this paper.

“Mixed” Meshless Time-Domain Adaptive Algorithm for Solving Elasto-Dynamics Equations

A time-domain adaptive algorithm was developed for solving elasto-dynamics problems through a mixed meshless local Petrov-Galerkin finite volume method (MLPG5). In this time-adaptive algorithm, each time-dependent variable is interpolated by a time series function of n-order, which is determined by a criterion in each step. The high-order series of expanded variables bring high accuracy in the time domain, especially for the elasto-dynamic equations, which are second-order PDE in the time domain. In the present mixed MLPG5 dynamic formulation, the strains are interpolated independently, as are displacements in the local weak form, which eliminates the expensive differential of the shape function. In the traditional MLPG5, both shape function and its derivative for each node need to be calculated. By taking the Heaviside function as the test function, the local domain integration of stiffness matrix is avoided. Several numerical examples, including the comparison of our method, the MLPG5–Newmark method and FEM (ANSYS) are given to demonstrate the advantages of the presented method: (1) a large time step can be used in solving a elasto-dynamics problem; (2) computational efficiency and accuracy are improved in both space and time; (3) smaller support sizes can be used in the mixed MLPG5.

Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds

AbstractThis article presents recent outcomes of the author's research on musical complex adaptive systems (CASs). The first part focuses on the concepts of adaptation and complexity within the framework of CASs and suggests a rigorous placing of the concepts within the musical domain. This analysis involves a distinction of the notions of context and information between the engineering field of information theory and the philosophical one of radical constructivism. I conclude this section by showing that, in this approach, information and context are mutually determining.Then, I introduce a technique related to the notion of evolvability in biology and genetic algorithms and that has significantly increased the complexity and long-term variety in music systems during autonomous evolutions. This technique distributes adaptation across higher levels and allows the system to reorganize the relationships among its agents and their structure circularly while interpreting and constructing its context.To conclude, an autonomous live performance piece from 2019–2020, “Constructing Realities (Homage to Heinz von Foerster),” which implements the theories mentioned above, is described, showing DSP processes and techniques that relate to evolvability, autopoiesis, fitness, and complexity through agent-based modeling. This article is accompanied by a companion article discussing the technical aspects of information processing algorithms, which are an essential part for the implementation of music CASs: “Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing.”

Time-Domain Adaptive Algorithms for Low- and High-Level Audio Information Processing

Abstract In this paper, we present a set of time-domain algorithms for the low- and high-level analysis of audio streams. These include spectral centroid, noisiness, and spectral spread for the low level, and dynamicity, heterogeneity, and complexity for the high level. The low-level algorithms provide a continuous measure of the features and can operate with short analysis frames. The high-level algorithms, on the other hand, are original designs informed both perceptually and by complexity theory for the analysis of musically meaningful information, both in short sounds or articulated streams with long-term nontrivial variations. These algorithms are suitable for the implementation of real-time audio analysis in diverse live performance setups that require the extraction of information from several streams at the same time. For example, the low-level algorithms can be deployed in large audio networks of adaptive agents, or in small-to-large ensembles for the analysis of various characteristics of the instruments for computer-assisted performance. Furthermore, the high-level algorithms can be implemented as part of fitness functions in music systems based on evolutionary algorithms that follow musically-informed criteria, or as analysis tools to assess the quality of some of the characteristics of a musical output. Musical applications of these algorithms can be found in a companion paper in this issue of Computer Music Journal: “Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds.”

Adaptive Joint Decoding and Equalization for Space-Time Block-Coded Amplify-and-Forward Relaying Systems

We develop practical time- and frequency-domain adaptive equalizers for distributed time-reversal space-time block-coded (TR-STBC) systems. The adaptive equalizers eliminate the need for explicit channel impulse response estimation at the receiver. The length of the time-domain adaptive equalizer is also independent of the data block length, making it particularly suitable for frequency selective fading channels with short delay spreads. We derive the block minimum mean square error solutions for both time- and frequency-domain distributed TR-STBC systems and from these we develop recursive least squares adaptive algorithms for the block structures. We use computer simulations to compare both the time- and frequency-domain adaptive equalizers for amplify-and-forward relay networks. We show that the adaptive algorithms work well for Protocols I and III proposed by Nabar The time-domain adaptive algorithms perform better than the frequency-domain algorithms, and overall the Protocol I receivers outperform the Protocol III receivers. We also show that, if only the Protocol III receiver is used, it can be susceptible to noise amplification due to a weaker source-to-relay link compared to the relay-to-destination link. This problem can be mitigated by using the Protocol I receivers with some extra complexity but much superior diversity performance.

A time-domain adaptive algorithm for rapid convergence

A time-domain adaptive LMS algorithm is presented which has, theoretically, the fastest convergence rates. The algorithm requires o(N2) operations where N is the length of the adaptive filter. It is shown that this algorithm is equivalent to the transform-domain adaptive algorithm with the optimum convergence rates proposed by Narayan et al. However the present algorithm does not require Karhunen-Loeve transforms for its implementation. It is also shown that the algorithm can be implemented with a complexity of o(mN) operations if the input process to the adaptive filter can be modeled as an autoregressive process of order m.