Electrodynamic loudspeaker linearization using a low complexity pth-Order inverse nonlinear filter

Nonlinear distortions are very challenging to tackle in electromechanical loudspeakers. They are observed in large signals mode, where high amplitude stimulus drives different components of the transducer to operate in their nonlinear region, resulting in harmonic and intermodulation distortions in the reproduced sounds. Many linearization schemes have been proposed to address this problem, they operate by pre-distorting the input signal before exciting the loudspeaker, in the aim of radiating distortion-free sound waves. In this work, we are interested in the performance evaluation of a low computational complexity feedforward linearization structure which is based on the pth order inverse of a one-dimension Volterra model of the driver. The scheme is designed to compensate for the 2nd and 3rd harmonic distortions. We will study the effect of varying the input voltage amplitude on the harmonic distortions reduction performance. A lumped-parameters model with parameters of a real driver will be used for the evaluation

Optimization and improvements in spatial sound reproduction systems through perceptual considerations

[ES] La reproducción de las propiedades espaciales del sonido es una cuestión cada vez más importante en muchas aplicaciones inmersivas emergentes. Ya sea en la reproducción de contenido audiovisual en entornos domésticos o en cines, en sistemas de videoconferencia inmersiva o en sistemas de realidad virtual o aumentada, el sonido espacial es crucial para una sensación de inmersión realista. La audición, más allá de la física del sonido, es un fenómeno perceptual influenciado por procesos cognitivos. El objetivo de esta tesis es contribuir con nuevos métodos y conocimiento a la optimización y simplificación de los sistemas de sonido espacial, desde un enfoque perceptual de la experiencia auditiva. Este trabajo trata en una primera parte algunos aspectos particulares relacionados con la reproducción espacial binaural del sonido, como son la escucha con auriculares y la personalización de la Función de Transferencia Relacionada con la Cabeza (Head Related Transfer Function - HRTF). Se ha realizado un estudio sobre la influencia de los auriculares en la percepción de la impresión espacial y la calidad, con especial atención a los efectos de la ecualización y la consiguiente distorsión no lineal. Con respecto a la individualización de la HRTF se presenta una implementación completa de un sistema de medida de HRTF y se introduce un nuevo método para la medida de HRTF en salas no anecoicas. Además, se han realizado dos experimentos diferentes y complementarios que han dado como resultado dos herramientas que pueden ser utilizadas en procesos de individualización de la HRTF, un modelo paramétrico del módulo de la HRTF y un ajuste por escalado de la Diferencia de Tiempo Interaural (Interaural Time Difference - ITD). En una segunda parte sobre reproducción con altavoces, se han evaluado distintas técnicas como la Síntesis de Campo de Ondas (Wave-Field Synthesis - WFS) o la panoramización por amplitud. Con experimentos perceptuales se han estudiado la capacidad de estos sistemas para producir sensación de distancia y la agudeza espacial con la que podemos percibir las fuentes sonoras si se dividen espectralmente y se reproducen en diferentes posiciones. Las aportaciones de esta investigación pretenden hacer más accesibles estas tecnologías al público en general, dada la demanda de experiencias y dispositivos audiovisuales que proporcionen mayor inmersión.[CA] La reproducció de les propietats espacials del so és una qüestió cada vegada més important en moltes aplicacions immersives emergents. Ja siga en la reproducció de contingut audiovisual en entorns domèstics o en cines, en sistemes de videoconferència immersius o en sistemes de realitat virtual o augmentada, el so espacial és crucial per a una sensació d'immersió realista. L'audició, més enllà de la física del so, és un fenomen perceptual influenciat per processos cognitius. L'objectiu d'aquesta tesi és contribuir a l'optimització i simplificació dels sistemes de so espacial amb nous mètodes i coneixement, des d'un criteri perceptual de l'experiència auditiva. Aquest treball tracta, en una primera part, alguns aspectes particulars relacionats amb la reproducció espacial binaural del so, com són l'audició amb auriculars i la personalització de la Funció de Transferència Relacionada amb el Cap (Head Related Transfer Function - HRTF). S'ha realitzat un estudi relacionat amb la influència dels auriculars en la percepció de la impressió espacial i la qualitat, dedicant especial atenció als efectes de l'equalització i la consegüent distorsió no lineal. Respecte a la individualització de la HRTF, es presenta una implementació completa d'un sistema de mesura de HRTF i s'inclou un nou mètode per a la mesura de HRTF en sales no anecoiques. A mès, s'han realitzat dos experiments diferents i complementaris que han donat com a resultat dues eines que poden ser utilitzades en processos d'individualització de la HRTF, un model paramètric del mòdul de la HRTF i un ajustament per escala de la Diferencià del Temps Interaural (Interaural Time Difference - ITD). En una segona part relacionada amb la reproducció amb altaveus, s'han avaluat distintes tècniques com la Síntesi de Camp d'Ones (Wave-Field Synthesis - WFS) o la panoramització per amplitud. Amb experiments perceptuals, s'ha estudiat la capacitat d'aquests sistemes per a produir una sensació de distància i l'agudesa espacial amb que podem percebre les fonts sonores, si es divideixen espectralment i es reprodueixen en diferents posicions. Les aportacions d'aquesta investigació volen fer més accessibles aquestes tecnologies al públic en general, degut a la demanda d'experiències i dispositius audiovisuals que proporcionen major immersió.[EN] The reproduction of the spatial properties of sound is an increasingly important concern in many emerging immersive applications. Whether it is the reproduction of audiovisual content in home environments or in cinemas, immersive video conferencing systems or virtual or augmented reality systems, spatial sound is crucial for a realistic sense of immersion. Hearing, beyond the physics of sound, is a perceptual phenomenon influenced by cognitive processes. The objective of this thesis is to contribute with new methods and knowledge to the optimization and simplification of spatial sound systems, from a perceptual approach to the hearing experience. This dissertation deals in a first part with some particular aspects related to the binaural spatial reproduction of sound, such as listening with headphones and the customization of the Head Related Transfer Function (HRTF). A study has been carried out on the influence of headphones on the perception of spatial impression and quality, with particular attention to the effects of equalization and subsequent non-linear distortion. With regard to the individualization of the HRTF a complete implementation of a HRTF measurement system is presented, and a new method for the measurement of HRTF in non-anechoic conditions is introduced. In addition, two different and complementary experiments have been carried out resulting in two tools that can be used in HRTF individualization processes, a parametric model of the HRTF magnitude and an Interaural Time Difference (ITD) scaling adjustment. In a second part concerning loudspeaker reproduction, different techniques such as Wave-Field Synthesis (WFS) or amplitude panning have been evaluated. With perceptual experiments it has been studied the capacity of these systems to produce a sensation of distance, and the spatial acuity with which we can perceive the sound sources if they are spectrally split and reproduced in different positions. The contributions of this research are intended to make these technologies more accessible to the general public, given the demand for audiovisual experiences and devices with increasing immersion.Gutiérrez Parera, P. (2020). Optimization and improvements in spatial sound reproduction systems through perceptual considerations [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/142696TESI

Various nonlinear models and their identification, equalization and linearization

System identification is a pre-requisite to analysis of a dynamic system and design of an appropriate controller for improving its performance. The more accurate the mathematical model identified for a system, the more effective will be the controller designed for it. The identification of nonlinear systems is a topic which has received considerable attention over the last two decades. Generally speaking, when it is difficult to model practical systems by mathematical analysis method, system identification may be an efficient way to overcome the shortage of mechanism analysis method. The goal of the modeling is to find a simple and efficient model which is in accord with the practical system. In many cases, linear models are not suitable to present these systems and nonlinear models have to be considered. Since there are nonlinear effects in practical systems, e.g. harmonic generation, intermediation, desensitization, gain expansion and chaos, we can infer that most control systems are nonlinear. Nonlinear models are more widely used in practice, because most phenomena are nonlinear in nature. Indeed, for many dynamic systems the use of nonlinear models is often of great interest and generally characterizes adequately physical processes over their whole operating range. Thus, accuracy and performance of the control law increase significantly. Therefore, nonlinear system modeling is much more important than linear system identification. We will deal with various nonlinear models and their processing

Automatic Optimal Input Command for Linearization of cMUT Output by a Temporal Target

2014 IEEE. Reprinted, with permission, from Sébastien Ménigot, Dominque Certon, Dominque Gross and Jean-Marc Girault, Automatic Optimal Input Command for Linearization of cMUT Output by a Temporal Target, 2014 IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the Université François Rabelais de Tours' products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected] audienceCapacitive micromachined ultrasonic transducers (cMUTs) are a promising alternative to the piezoelectric transducer. However, their native nonlinear behavior is a limitation for their use in medical ultrasound applications. Several methods based on the pre-compensation of a preselected input voltage have been proposed to cancel out the harmonic components generated. Unfortunately, these existing pre-compensation methods have two major flaws. The first is that the pre-compensation procedure is not generally automatic, and the second is that they can only reduce the second harmonic component. This can, therefore, limit their use for some imaging methods, which require a broader bandwidth, e.g., to receive the third harmonic component. In this study, we generalized the presetting methods to reduce all nonlinearities in the cMUT output. Our automatic pre-compensation method can work whatever the excitation waveform. The precompensation method is based on the nonlinear modeling of harmonic components from a Volterra decomposition in which the parameters are evaluated by using a Nelder-Mead algorithm. To validate the feasibility of this approach, the method was applied to an element of a linear array with several types of excitation often encountered in encoded ultrasound imaging. The results showed that the nonlinear components were reduced by up to 21.2 dB

Bayes meets Bach: applications of Bayesian statistics to audio restoration

Memoryless nonlinear distortion can be present in audio signals, from recording to reproduction: bad quality or amateurishly operated equipments, physically degraded media and low quality reproducing devices are some examples where nonlinearities can naturally appear. Another quite common defect in old recordings are the long pulses, caused in general by the reproduction of disks with deep scratches or severely degraded magnetic tapes. Such defects are characterized by an initial discontinuity in the waveform, followed by a low-frequency transient of long duration. In both cases audible artifacts can be created, causing an unpleasant experience to the listener. It is then important to develop techniques to mitigate such defects, having at hand only the degraded signal, in a way to recover the original signal. In this thesis, techniques to deal with both problems are presented: the restoration of nonlinearly degraded recordings is tackled in a Bayesian context, considering both autoregressive models and sparsity in the DCT domain for the original signal, as well as through a deterministic solution also based on sparsity; for the suppression of long pulses, a parametric approach is revisited with the addition of an efficient initialization procedure, and a nonparametric modeling via Gaussian process is also presented.Distorções não-lineares podem aparecer em sinais de áudio desde o momento da sua gravação até a posterior reprodução: equipamentos precários ou operados de maneira indevida, mídias fisicamente degradadas e baixa qualidade dos aparelhos de reprodução são somente alguns exemplos onde não-linearidades podem aparecer de modo natural. Outro defeito bastante comum em gravações antigas são os pulsos longos, em geral causados pela reprodução de discos com arranhões muito profundos ou fitas magnéticas severamente degradadas. Tais defeitos são caracterizados por uma descontinuidade inicial na forma de onda, seguida de um transitório de baixa frequência e longa duração. Em ambos os casos, artefatos auditivos podem ser criados, causando assim uma experiência ruim para o ouvinte. E importante então desenvolver técnicas para mitigar tais efeitos, tendo como base somente uma versão do sinal degradado, de modo a recuperar o sinal original não degradado. Nessa tese são apresentadas técnicas para lidar com esses dois problemas: o problema de restaurar gravações corrompidas com distorções não-lineares é abordado em um contexto bayesiano, considerando tanto modelos autorregressivos quanto de esparsidade no domínio da DCT para o sinal original, bem como por uma solução determinística também em usando esparsidade; para a supressão de pulsos longos, uma abordagem paramétrica é revisitada, junto com o acréscimo de um eficiente procedimento de inicialização, sendo também apresentada uma abordagem não-paramétricausando processos gaussianos

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Adaptive Algorithms for Intelligent Acoustic Interfaces

Modern speech communications are evolving towards a new direction which involves users in a more perceptive way. That is the immersive experience, which may be considered as the “last-mile” problem of telecommunications. One of the main feature of immersive communications is the distant-talking, i.e. the hands-free (in the broad sense) speech communications without bodyworn or tethered microphones that takes place in a multisource environment where interfering signals may degrade the communication quality and the intelligibility of the desired speech source. In order to preserve speech quality intelligent acoustic interfaces may be used. An intelligent acoustic interface may comprise multiple microphones and loudspeakers and its peculiarity is to model the acoustic channel in order to adapt to user requirements and to environment conditions. This is the reason why intelligent acoustic interfaces are based on adaptive filtering algorithms. The acoustic path modelling entails a set of problems which have to be taken into account in designing an adaptive filtering algorithm. Such problems may be basically generated by a linear or a nonlinear process and can be tackled respectively by linear or nonlinear adaptive algorithms. In this work we consider such modelling problems and we propose novel effective adaptive algorithms that allow acoustic interfaces to be robust against any interfering signals, thus preserving the perceived quality of desired speech signals. As regards linear adaptive algorithms, a class of adaptive filters based on the sparse nature of the acoustic impulse response has been recently proposed. We adopt such class of adaptive filters, named proportionate adaptive filters, and derive a general framework from which it is possible to derive any linear adaptive algorithm. Using such framework we also propose some efficient proportionate adaptive algorithms, expressly designed to tackle problems of a linear nature. On the other side, in order to address problems deriving from a nonlinear process, we propose a novel filtering model which performs a nonlinear transformations by means of functional links. Using such nonlinear model, we propose functional link adaptive filters which provide an efficient solution to the modelling of a nonlinear acoustic channel. Finally, we introduce robust filtering architectures based on adaptive combinations of filters that allow acoustic interfaces to more effectively adapt to environment conditions, thus providing a powerful mean to immersive speech communications

Music Production Behaviour Modelling

The new millennium has seen an explosion of computational approaches to the study of music production, due in part to the decreasing cost of computation and the increase of digital music production techniques. The rise of digital recording equipment, MIDI, digital audio workstations (DAWs), and software plugins for audio effects led to the digital capture of various processes in music production. This discretization of traditionally analogue methods allowed for the development of intelligent music production, which uses machine learning to numerically characterize and automate portions of the music production process. One algorithm from the field referred to as ``reverse engineering a multitrack mix'' can recover the audio effects processing used to transform a multitrack recording into a mixdown in the absence of information about how the mixdown was achieved. This thesis improves on this method of reverse engineering a mix by leveraging recent advancements in machine learning for audio. Using the differentiable digital signal processing paradigm, greybox modules for gain, panning, equalisation, artificial reverberation, memoryless waveshaping distortion, and dynamic range compression are presented. These modules are then connected in a mixing chain and are optimized to learn the effects used in a given mixdown. Both objective and perceptual metrics are presented to measure the performance of these various modules in isolation and within a full mixing chain. Ultimately a fully differentiable mixing chain is presented that outperforms previously proposed methods to reverse engineer a mix. Directions for future work are proposed to improve characterization of multitrack mixing behaviours