Advanced automatic mixing tools for music

PhDThis thesis presents research on several independent systems that when combined together can generate an automatic sound mix out of an unknown set of multi‐channel inputs. The research explores the possibility of reproducing the mixing decisions of a skilled audio engineer with minimal or no human interaction. The research is restricted to non‐time varying mixes for large room acoustics. This research has applications in dynamic sound music concerts, remote mixing, recording and postproduction as well as live mixing for interactive scenes. Currently, automated mixers are capable of saving a set of static mix scenes that can be loaded for later use, but they lack the ability to adapt to a different room or to a different set of inputs. In other words, they lack the ability to automatically make mixing decisions. The automatic mixer research depicted here distinguishes between the engineering mixing and the subjective mixing contributions. This research aims to automate the technical tasks related to audio mixing while freeing the audio engineer to perform the fine‐tuning involved in generating an aesthetically‐pleasing sound mix. Although the system mainly deals with the technical constraints involved in generating an audio mix, the developed system takes advantage of common practices performed by sound engineers whenever possible. The system also makes use of inter‐dependent channel information for controlling signal processing tasks while aiming to maintain system stability at all times. A working implementation of the system is described and subjective evaluation between a human mix and the automatic mix is used to measure the success of the automatic mixing tools

Adaptive notch filtering for tracking multiple complex sinusoid signals

This thesis is related to the field of digital signal processing; where the aim of this research is to develop features of an infinite impulse response adaptive notch filter capable of tracking multiple complex sinusoid signals. Adaptive notch filters are commonly used in: Radar, Sonar, and Communication systems, and have the ability to track the frequencies of real or complex sinusoid signals; thus removing noise from an estimate, and enhancing the performance of a system. This research programme began by implementing four currently proposed adaptive notch structures. These structures were simulated and compared: for tracking between two and four signals; however, in their current form they are only capable of tracking real sinusoid signals. Next, one of these structures is developed further, to facilitate the ability to track complex sinusoid signals. This original structure gives superior performance over Regalia's comparable structure under certain conditions, which has been proven by simulations and results. Complex adaptive notch filter structures generally contain two parameters: the first tracks a target frequency, then the second controls the adaptive notch filter's bandwidth. This thesis develops the notch filter, so that the bandwidth parameter can be adapted via a method of steepest ascent; and also investigates tracking complex-valued chirp signals. Lastly, stochastic search methods are considered; and particle swarm optimisation has been applied to reinitialise an adaptive notch filter, when tracking two signals; thus more quickly locating an unknown frequency, after the frequency of the complex sinusoid signal jumps

Efficient Schemes for Adaptive Frequency Tracking and their Relevance for EEG and ECG

Amplitude and frequency are the two primary features of one-dimensional signals, and thus both are widely utilized to analysis data in numerous fields. While amplitude can be examined directly, frequency requires more elaborate approaches, except in the simplest cases. Consequently, a large number of techniques have been proposed over the years to retrieve information about frequency. The most famous method is probably power spectral density estimation. However, this approach is limited to stationary signals since the temporal information is lost. Time-frequency approaches were developed to tackle the problem of frequency estimation in non-stationary data. Although they can estimate the power of a signal in a given time interval and in a given frequency band, these tools have two drawbacks that make them less valuable in certain situations. First, due to their interdependent time and frequency resolutions, improving the accuracy in one domain means decreasing it in the other one. Second, it is difficult to use this kind of approach to estimate the instantaneous frequency of a specific oscillatory component. A solution to these two limitations is provided by adaptive frequency tracking algorithms. Typically, these algorithms use a time-varying filter (a band-pass or notch filter in most cases) to extract an oscillation, and an adaptive mechanism to estimate its instantaneous frequency. The main objective of the first part of the present thesis is to develop such a scheme for adaptive frequency tracking, the single frequency tracker. This algorithm compares favorably with existing methods for frequency tracking in terms of bias, variance and convergence speed. The most distinguishing feature of this adaptive algorithm is that it maximizes the oscillatory behavior at its output. Furthermore, due to its specific time-varying band-pass filter, it does not introduce any distortion in the extracted component. This scheme is also extended to tackle certain situations, namely the presence of several oscillations in a single signal, the related issue of harmonic components, and the availability of more than one signal with the oscillation of interest. The first extension is aimed at tracking several components simultaneously. The basic idea is to use one tracker to estimate the instantaneous frequency of each oscillation. The second extension uses the additional information contained in several signals to achieve better overall performance. Specifically, it computes separately instantaneous frequency estimates for all available signals which are then combined with weights minimizing the estimation variance. The third extension, which is based on an idea similar to the first one and uses the same weighting procedure as the second one, takes into account the harmonic structure of a signal to improve the estimation performance. A non-causal iterative method for offline processing is also developed in order to enhance an initial frequency trajectory by using future information in addition to past information. Like the single frequency tracker, this method aims at maximizing the oscillatory behavior at the output. Any approach can be used to obtain the initial trajectory. In the second part of this dissertation, the schemes for adaptive frequency tracking developed in the first part are applied to electroencephalographic and electrcardiographic data. In a first study, the single frequency tracker is used to analyze interactions between neuronal oscillations in different frequency bands, known as cross-frequency couplings, during a visual evoked potential experiment with illusory contour stimuli. With this adaptive approach ensuring that meaningful phase information is extracted, the differences in coupling strength between stimuli with and without illusory contours are more clearly highlighted than with traditional methods based on predefined filter-banks. In addition, the adaptive scheme leads to the detection of differences in instantaneous frequency. In a second study, two organization measures are derived from the harmonic extension. They are based on the power repartition in the frequency domain for the first one and on the phase relation between harmonic components for the second one. These measures, computed from the surface electrocardiogram, are shown to help predicting the outcome of catheter ablation of persistent atrial fibrillation. The proposed adaptive frequency tracking schemes are also applied to signals recorded in the field of sport sciences in order to illustrate their potential uses. To summarize, the present thesis introduces several algorithms for adaptive frequency tracking. These algorithms are presented in full detail and they are then applied to practical situations. In particular, they are shown to improve the detection of coupling mechanisms in brain activity and to provide relevant organization measures for atrial fibrillation

Towards Real-Time Non-Stationary Sinusoidal Modelling of Kick and Bass Sounds for Audio Analysis and Modification

Sinusoidal Modelling is a powerful and flexible parametric method for analysing and processing audio signals. These signals have an underlying structure that modern spectral models aim to exploit by separating the signal into sinusoidal, transient, and noise components. Each of these can then be modelled in a manner most appropriate to that component's inherent structure. The accuracy of the estimated parameters is directly related to the quality of the model's representation of the signal, and the assumptions made about its underlying structure. For sinusoidal models, these assumptions generally affect the non-stationary estimates related to amplitude and frequency modulations, and the type of amplitude change curve. This is especially true when using a single analysis frame in a non-overlapping framework, where biased estimates can result in discontinuities at frame boundaries. It is therefore desirable for such a model to distinguish between the shape of different amplitude changes and adapt the estimation of this accordingly. Intra-frame amplitude change can be interpreted as a change in the windowing function applied to a stationary sinusoid, which can be estimated from the derivative of the phase with respect to frequency at magnitude peaks in the DFT spectrum. A method for measuring monotonic linear amplitude change from single-frame estimates using the first-order derivative of the phase with respect to frequency (approximated by the first-order difference) is presented, along with a method of distinguishing between linear and exponential amplitude change. An adaption of the popular matching pursuit algorithm for refining model parameters in a segmented framework has been investigated using a dictionary comprised of sinusoids with parameters varying slightly from model estimates, based on Modelled Pursuit (MoP). Modelling of the residual signal using a segmented undecimated Wavelet Transform (segUWT) is presented. A generalisation for both the forward and inverse transforms, for delay compensations and overlap extensions for different lengths of Wavelets and the number of decomposition levels in an Overlap Save (OLS) implementation for dealing with convolution block-based artefacts is presented. This shift invariant implementation of the DWT is a popular tool for de-noising and shows promising results for the separation of transients from noise

Split algorithms for LMS adaptive systems.

by Ho King Choi.Thesis (Ph.D.)--Chinese University of Hong Kong, 1991.Includes bibliographical references.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Adaptive Filter and Adaptive System --- p.1Chapter 1.2 --- Applications of Adaptive Filter --- p.4Chapter 1.2.1 --- System Identification --- p.4Chapter 1.2.2 --- Noise Cancellation --- p.6Chapter 1.2.3 --- Echo Cancellation --- p.8Chapter 1.2.4 --- Speech Processing --- p.10Chapter 1.3 --- Chapter Summary --- p.14References --- p.15Chapter 2. --- Adaptive Filter Structures and Algorithms --- p.17Chapter 2.1 --- Filter Structures for Adaptive Filtering --- p.17Chapter 2.2 --- Adaptation Algorithms --- p.22Chapter 2.2.1 --- The LMS Adaptation Algorithm --- p.24Chapter 2.2.1.1 --- Convergence Analysis --- p.28Chapter 2.2.1.2 --- Steady State Performance --- p.33Chapter 2.2.2 --- The RLS Adaptation Algorithm --- p.35Chapter 2.3 --- Chapter Summary --- p.39References --- p.41Chapter 3. --- Parallel Split Adaptive System --- p.45Chapter 3.1 --- Parallel Form Adaptive Filter --- p.45Chapter 3.2 --- Joint Process Estimation with a Split-Path Adaptive Filter --- p.49Chapter 3.2.1 --- The New Adaptive System Identification Configuration --- p.53Chapter 3.2.2 --- Analysis of the Split-Path System Modeling Structure --- p.57Chapter 3.2.3 --- Comparison with the Non-Split Configuration --- p.63Chapter 3.2.4 --- Some Notes on Even Filter Order Case --- p.67Chapter 3.2.5 --- Simulation Results --- p.70Chapter 3.3 --- Autoregressive Modeling with a Split-Path Adaptive Filter --- p.75Chapter 3.3.1 --- The Split-Path Adaptive Filter for AR Modeling --- p.79Chapter 3.3.2 --- Analysis of the Split-Path AR Modeling Structure --- p.84Chapter 3.3.3 --- Comparison with Traditional AR Modeling System --- p.89Chapter 3.3.4 --- Selection of Step Sizes --- p.90Chapter 3.3.5 --- Some Notes on Odd Filter Order Case --- p.94Chapter 3.3.6 --- Simulation Results --- p.94Chapter 3.3.7 --- Application to Noise Cancellation --- p.99Chapter 3.4 --- Chapter Summary --- p.107References --- p.109Chapter 4. --- Serial Split Adaptive System --- p.112Chapter 4.1 --- Serial Form Adaptive Filter --- p.112Chapter 4.2 --- Time Delay Estimation with a Serial Split Adaptive Filter --- p.125Chapter 4.2.1 --- Adaptive TDE --- p.125Chapter 4.2.2 --- Split Filter Approach to Adaptive TDE --- p.132Chapter 4.2.3 --- Analysis of the New TDE System --- p.136Chapter 4.2.3.1 --- Least-mean-square Solution --- p.138Chapter 4.2.3.2 --- Adaptation Algorithm and Performance Evaluation --- p.142Chapter 4.2.4 --- Comparison with Traditional Adaptive TDE Method --- p.147Chapter 4.2.5 --- System Implementation --- p.148Chapter 4.2.6 --- Simulation Results --- p.148Chapter 4.2.7 --- Constrained Adaptation for the New TDE System --- p.156Chapter 4.3 --- Chapter Summary --- p.163References --- p.165Chapter 5. --- Extension of the Split Adaptive Systems --- p.167Chapter 5.1 --- The Generalized Parallel Split System --- p.167Chapter 5.2 --- The Generalized Serial Split System --- p.170Chapter 5.3 --- Comparison between the Parallel and the Serial Split Adaptive System --- p.172Chapter 5.4 --- Integration of the Two Forms of Split Predictors --- p.177Chapter 5.5 --- Application of the Integrated Split Model to Speech Encoding --- p.179Chapter 5.6 --- Chapter Summary --- p.188References --- p.139Chapter 6. --- Conclusions --- p.191References --- p.19

Distributed adaptive signal processing for frequency estimation

It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms. To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way. To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements. An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques. Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.Open Acces

INSIGHTS INTO HEPATIC ALPHA-FETOPROTEIN GENE REGULATION DURING LIVER DEVELOPMENT AND DISEASE

The liver is an essential organ for cholesterol homeostasis. If this process becomes dysregulated, cardiovascular disease (CVD) develops. Zinc-fingers and homeoboxes 2 (Zhx2) as an important hepatic gene regulator and contributes to CVD. BALB/cJ mice, with mutant Zhx2 allele, have fewer atherosclerotic plaques compared to other strains on a high fat diet. In my dissertation, I focused on the liver phenotype in BALB/cJ mice on a high-fat diet and found increased liver damage compared to wild-type Zhx2 mice. These data indicates that reduced Zhx2 in the liver leads to CVD resistance, but increases liver damage. Therefore, Zhx2 has an important role in lipid metabolism and liver function. Hepatic alpha-fetoprotein (AFP) is expressed abundantly in the fetal liver and repressed after birth regulated through three enhancers (E1, E2, and E3). E3 activity is restricted to a single layer of hepatocytes surrounding central veins (pericentral region) along with glutamine synthetase (GS). In my dissertation, I explore pericentral gene regulation in the adult liver. A GS enhancer (GSe) also exhibits pericentral activity which, along with E3, is regulated by the β-catenin signaling pathway. Orphan receptors, Rev-erbα, Rev-erbβ, and RORα, contribute to E3 activity elucidating a potential mechanism for zonation

Study of Liver Surface Imaging Marker to Monitor Chronic Liver Disease Progression

Ph.DDOCTOR OF PHILOSOPH

Classification and Separation Techniques based on Fundamental Frequency for Speech Enhancement

[ES] En esta tesis se desarrollan nuevos algoritmos de clasificación y mejora de voz basados en las propiedades de la frecuencia fundamental (F0) de la señal vocal. Estas propiedades permiten su discriminación respecto al resto de señales de la escena acústica, ya sea mediante la definición de características (para clasificación) o la definición de modelos de señal (para separación). Tres contribuciones se aportan en esta tesis: 1) un algoritmo de clasificación de entorno acústico basado en F0 para audífonos digitales, capaz de clasificar la señal en las clases voz y no-voz; 2) un algoritmo de detección de voz sonora basado en la aperiodicidad, capaz de funcionar en ruido no estacionario y con aplicación a mejora de voz; 3) un algoritmo de separación de voz y ruido basado en descomposición NMF, donde el ruido se modela de una forma genérica mediante restricciones matemáticas.[EN]This thesis is focused on the development of new classification and speech enhancement algorithms based, explicitly or implicitly, on the fundamental frequency (F0). The F0 of speech has a number of properties that enable speech discrimination from the remaining signals in the acoustic scene, either by defining F0-based signal features (for classification) or F0-based signal models (for separation). Three main contributions are included in this work: 1) an acoustic environment classification algorithm for hearing aids based on F0 to classify the input signal into speech and nonspeech classes; 2) a frame-by-frame basis voiced speech detection algorithm based on the aperiodicity measure, able to work under non-stationary noise and applicable to speech enhancement; 3) a speech denoising algorithm based on a regularized NMF decomposition, in which the background noise is described in a generic way with mathematical constraints.Tesis Univ. Jaén. Departamento de Ingeniería de Telecomunición. Leída el 11 de enero de 201