Fractionally-addressed delay lines

While traditional implementations of variable-length digital delay lines are based on a circular buffer accessed by two pointers, we propose an implementation where a single fractional pointer is used both for read and write operations. On modern general-purpose architectures, the proposed method is nearly as efficient as the popularinterpolated circular buffer, and it behaves well for delay-length modulations commonly found in digital audio effects. The physical interpretation of the new implementation shows that it is suitable for simulating tension or density modulations in wave-propagating media.Comment: 11 pages, 19 figures, to be published in IEEE Transactions on Speech and Audio Processing Corrected ACM-clas

Distributed state estimation in sensor networks with randomly occurring nonlinearities subject to time delays

This is the post-print version of the Article. The official published version can be accessed from the links below - Copyright @ 2012 ACM.This article is concerned with a new distributed state estimation problem for a class of dynamical systems in sensor networks. The target plant is described by a set of differential equations disturbed by a Brownian motion and randomly occurring nonlinearities (RONs) subject to time delays. The RONs are investigated here to reflect network-induced randomly occurring regulation of the delayed states on the current ones. Through available measurement output transmitted from the sensors, a distributed state estimator is designed to estimate the states of the target system, where each sensor can communicate with the neighboring sensors according to the given topology by means of a directed graph. The state estimation is carried out in a distributed way and is therefore applicable to online application. By resorting to the Lyapunov functional combined with stochastic analysis techniques, several delay-dependent criteria are established that not only ensure the estimation error to be globally asymptotically stable in the mean square, but also guarantee the existence of the desired estimator gains that can then be explicitly expressed when certain matrix inequalities are solved. A numerical example is given to verify the designed distributed state estimators.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008, 60804028 and 61174136, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Model-Based Machine Learning for Joint Digital Backpropagation and PMD Compensation

In this paper, we propose a model-based machine-learning approach for dual-polarization systems by parameterizing the split-step Fourier method for the Manakov-PMD equation. The resulting method combines hardware-friendly time-domain nonlinearity mitigation via the recently proposed learned digital backpropagation (LDBP) with distributed compensation of polarization-mode dispersion (PMD). We refer to the resulting approach as LDBP-PMD. We train LDBP-PMD on multiple PMD realizations and show that it converges within 1% of its peak dB performance after 428 training iterations on average, yielding a peak effective signal-to-noise ratio of only 0.30 dB below the PMD-free case. Similar to state-of-the-art lumped PMD compensation algorithms in practical systems, our approach does not assume any knowledge about the particular PMD realization along the link, nor any knowledge about the total accumulated PMD. This is a significant improvement compared to prior work on distributed PMD compensation, where knowledge about the accumulated PMD is typically assumed. We also compare different parameterization choices in terms of performance, complexity, and convergence behavior. Lastly, we demonstrate that the learned models can be successfully retrained after an abrupt change of the PMD realization along the fiber.Comment: 10 pages, 11 figures, to appear in the IEEE/OSA Journal of Lightwave Technolog

A digital waveguide-based approach for Clavinet modeling and synthesis

The Clavinet is an electromechanical musical instrument produced in the mid-twentieth century. As is the case for other vintage instruments, it is subject to aging and requires great effort to be maintained or restored. This paper reports analyses conducted on a Hohner Clavinet D6 and proposes a computational model to faithfully reproduce the Clavinet sound in real time, from tone generation to the emulation of the electronic components. The string excitation signal model is physically inspired and represents a cheap solution in terms of both computational resources and especially memory requirements (compared, e.g., to sample playback systems). Pickups and amplifier models have been implemented which enhance the natural character of the sound with respect to previous work. A model has been implemented on a real-time software platform, Pure Data, capable of a 10-voice polyphony with low latency on an embedded device. Finally, subjective listening tests conducted using the current model are compared to previous tests showing slightly improved results

Spatially distributed computational modeling of a nonlinear vibrating string

Värähtelevän kielen epälineaarinen käyttäytyminen saa monissa kielisoittimissa aikaan soittimelle luonteenomaisen ja helposti tunnistettavan äänen. Laadukkaan kielisoitinsynteesin vuoksi onkin tärkeää, että nykyaikaiset äänisynteesimenetelmät ottavat huomioon myös kielten epälineaarisuudet. Tässä diplomityössä esitellään kaksi uutta synteesimenetelmää, jotka fysikaalisen mallinnuksen avulla simuloivat epälineaarisia näpättyjä kieliä paikkajakautuneesti, keskittyen jännitysmodulaation tuottamiin epälineaarisuuksiin. Toinen menetelmistä käyttää hajautettuja murtoviivesuotimia digitaalisen aaltojohtomallin viivesilmukan pituuden ajonaikaisessa virittämisessä, kun taas toinen hyödyntää murtoviivesuotimia äärelliseen erotukseen pohjautuvan mallin aikaresoluution muuttamisessa ajon aikana. Jännitysmodulaation suuruus arvioidaan kummankin mallin tapauksessa jokaisella aika-askeleella kielen pidentymästä. Molempien mallien simulaatiotulokset esitellään ja niitä verrataan toisiinsa sekä myös mitattuihin arvoihin. Epälineaarisen aaltojohtomallin avulla on toteutettu reaaliaikainen kantelemalli.Nonlinearities in string instruments are responsible for several interesting acoustical features, resulting in characteristic and easily recognizable tones. For this reason, modern synthesis models have to be capable of modeling this nonlinear behavior, when high quality results are desired. This thesis presents two novel physical modeling algorithms for simulating the tension modulation nonlinearity in plucked strings in a spatially distributed manner. The first method uses fractional delay filters within a digital waveguide structure, allowing the length of the string to be modulated during run time. The second method uses a nonlinear finite difference approach, where the string state is approximated between sampling instants also using fractional delay filters, thus allowing run-time modulation of the temporal sampling location. The magnitude of the tension modulation is evaluated from the elongation of the string at every time step in both cases. Simulation results of the two models are presented and compared. Real-time sound synthesis of the kantele, a traditional Finnish plucked-string instrument with strong effect of tension modulation, has been implemented using the nonlinear digital waveguide algorithm

Model based control strategies for a class of nonlinear mechanical sub-systems

This paper presents a comparison between various control strategies for a class of mechanical actuators common in heavy-duty industry. Typical actuator components are hydraulic or pneumatic elements with static non-linearities, which are commonly referred to as Hammerstein systems. Such static non-linearities may vary in time as a function of the load and hence classical inverse-model based control strategies may deliver sub-optimal performance. This paper investigates the ability of advanced model based control strategies to satisfy a tolerance interval for position error values, overshoot and settling time specifications. Due to the presence of static non-linearity requiring changing direction of movement, control effort is also evaluated in terms of zero crossing frequency (up-down or left-right movement). Simulation and experimental data from a lab setup suggest that sliding mode control is able to improve global performance parameters

Re-Sonification of Objects, Events, and Environments

abstract: Digital sound synthesis allows the creation of a great variety of sounds. Focusing on interesting or ecologically valid sounds for music, simulation, aesthetics, or other purposes limits the otherwise vast digital audio palette. Tools for creating such sounds vary from arbitrary methods of altering recordings to precise simulations of vibrating objects. In this work, methods of sound synthesis by re-sonification are considered. Re-sonification, herein, refers to the general process of analyzing, possibly transforming, and resynthesizing or reusing recorded sounds in meaningful ways, to convey information. Applied to soundscapes, re-sonification is presented as a means of conveying activity within an environment. Applied to the sounds of objects, this work examines modeling the perception of objects as well as their physical properties and the ability to simulate interactive events with such objects. To create soundscapes to re-sonify geographic environments, a method of automated soundscape design is presented. Using recorded sounds that are classified based on acoustic, social, semantic, and geographic information, this method produces stochastically generated soundscapes to re-sonify selected geographic areas. Drawing on prior knowledge, local sounds and those deemed similar comprise a locale's soundscape. In the context of re-sonifying events, this work examines processes for modeling and estimating the excitations of sounding objects. These include plucking, striking, rubbing, and any interaction that imparts energy into a system, affecting the resultant sound. A method of estimating a linear system's input, constrained to a signal-subspace, is presented and applied toward improving the estimation of percussive excitations for re-sonification. To work toward robust recording-based modeling and re-sonification of objects, new implementations of banded waveguide (BWG) models are proposed for object modeling and sound synthesis. Previous implementations of BWGs use arbitrary model parameters and may produce a range of simulations that do not match digital waveguide or modal models of the same design. Subject to linear excitations, some models proposed here behave identically to other equivalently designed physical models. Under nonlinear interactions, such as bowing, many of the proposed implementations exhibit improvements in the attack characteristics of synthesized sounds.Dissertation/ThesisPh.D. Electrical Engineering 201

Revisiting Efficient Multi-Step Nonlinearity Compensation with Machine Learning: An Experimental Demonstration

Efficient nonlinearity compensation in fiber-optic communication systems is considered a key element to go beyond the "capacity crunch''. One guiding principle for previous work on the design of practical nonlinearity compensation schemes is that fewer steps lead to better systems. In this paper, we challenge this assumption and show how to carefully design multi-step approaches that provide better performance--complexity trade-offs than their few-step counterparts. We consider the recently proposed learned digital backpropagation (LDBP) approach, where the linear steps in the split-step method are re-interpreted as general linear functions, similar to the weight matrices in a deep neural network. Our main contribution lies in an experimental demonstration of this approach for a 25 Gbaud single-channel optical transmission system. It is shown how LDBP can be integrated into a coherent receiver DSP chain and successfully trained in the presence of various hardware impairments. Our results show that LDBP with limited complexity can achieve better performance than standard DBP by using very short, but jointly optimized, finite-impulse response filters in each step. This paper also provides an overview of recently proposed extensions of LDBP and we comment on potentially interesting avenues for future work.Comment: 10 pages, 5 figures. Author version of a paper published in the Journal of Lightwave Technology. OSA/IEEE copyright may appl