Dissertations / Theses on the topic 'HARMONICS EXTRACTION'

Diffusion MRI (dMRI) measurements allow us to infer the microstructural properties of white matter and to reconstruct fiber pathways in-vivo. High angular diffusion imaging (HARDI) allows for the creation of more and more complex local models connecting the microstructure to the measured signal. One of the challenges is the derivation of meaningful metrics describing the underlying structure from the local models. The aim hereby is to increase the specificity of the widely used metric fractional anisotropy (FA) by using the additional information contained within the HARDI data. A local model which is connected directly to the underlying microstructure through the model of a single fiber population is spherical deconvolution. It produces a fiber orientation density function (fODF), which can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle). Parameterizing these peaks one is able to disentangle and characterize these bundles. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which metrics for the parametric quantification of fiber bundles are derived. Meaningful relationships between these measures and the underlying microstructural properties are proposed. The focus lies on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. These metrics are compared to the conventionally used fractional anisotropy (FA) and it is shown how they may help to increase the specificity of the characterization of microstructural properties. Visualization of the micro-structural arrangement is another application of dMRI. This is done by using tractography to propagate the fiber layout, extracted from the local model, in each voxel. In practice most tractography algorithms use little of the additional information gained from HARDI based local models aside from the reconstructed fiber bundle directions. In this work an approach to tractography based on the Bingham parameterization of the fODF is introduced. For each of the fiber populations present in a voxel the diffusion signal and tensor are computed. Then tensor deflection tractography is performed. This allows incorporating the complete bundle information, performing local interpolation as well as using multiple directions per voxel for generating tracts. Another aspect of this work is the investigation of the spherical harmonic representation which is used most commonly for the fODF by means of the parameters derived from the Bingham distribution fit. Here a strong connection between the approximation errors in the spherical representation of the Dirac delta function and the distribution of crossing angles recovered from the fODF was discovered. The final aspect of this work is the application of the metrics derived from the Bingham fit to a number of fetal datasets for quantifying the brain’s development. This is done by introducing the Gini-coefficient as a metric describing the brain’s age.

La thèse étudie tout d’abord la relation entre les harmoniques à fentes du rotor (RSHs) et la vitesse du rotor instantanée. Pour suivre directement l'RSH, les exigences du système sont pleinement prises en compte.Dans un deuxième temps, les travaux de thèse ont permis de développer un système sans capteur en fonction de boucle à verrouillage de phase (PLL): La largeur de bande centrale est réglée en ligne sur la base des valeurs de référence, des fréquences d'alimentation et de glissement prévues au convertisseur PWM, la PLL est réglée pour suivre le rotor de la machine à RSH sans la nécessité de toute injection de signal à haute fréquence, ni en rotation, ni de pulsation. Ce système d'estimation de vitesse, qui est approprié pour le contrôleur scalaire, avait été intégré avec le lecteur scalaire, conduisant à un simple calcul peu exigeant, à faible coût de l’entraînement de la machine à induction sans capteur à faible coût. Les résultats expérimentaux montrent que le système est en mesure de suivre la vitesse de la machine dans une plage de vitesse très étendue.Enfin, un système sans capteur amélioré basé sur l'analyse de composant mineur (MCA) neurones est décrit. Selon la théorie de Pisarenko, il a été vérifié que le MC qui se trouve dans le sous-espace de bruit est orthogonale au sous-espace de signal, par conséquent, les fré-quences de signal contenues dans l'entrée peuvent être calculées à partir d'un polynôme formé par la MC. Classiquement, ce qui nécessitera la décomposition propre encombrants, néan-moins, la méthode de neurones proposée dans cette thèse peut récupérer le MC de façon ré-cursive avec moins de calculs et des performances améliorées d'erreur (la solution est sur un total de moins sens carré). En outre, l'estimateur de vitesse est appliquée à l'entraînement scalaire avec vérification expérimentale, l'ensemble du système se comporte bien, et la méthode MCA renforcée par réseaux neuronaux a fourni un bon potentiel dans l'application des harmoniques récupérer
The thesis first studies the relation between the rotor slot harmonics (RSHs) and the instan-taneous rotor speed. To directly track the RSH, the requirements of the system are fully ad-dressed.Second, the thesis presents a sensorless scheme based on phase-locked loop (PLL): The centre bandwidth is tuned on-line on the basis of the reference values of the supply and slip frequencies provided to the PWM converter, the PLL is tuned to track the machine rotor slot-ting harmonic without the need of any high frequency signal injection, neither rotating nor pulsating. This speed estimation scheme, which is suitable for the scalar controller, had been integrated with the scalar drive, leading to a simple, computationally not demanding, low cost sensorless IM drives. The experiment results show that the system is able to track the machine speed in a very wide speed range.Finally, an improved sensorless scheme based on minor component analysis (MCA) neu-rons is described. According to the Pisarenko’s theory, it has been verified that the MC which lies in the noise subspace is orthogonal to the signal subspace, thus, the signal frequencies contained in the input can be computed from a polynomial formed by the MC. Conventional-ly, this will require the bulky eigen-decomposition, nevertheless, the neural method proposed in this thesis can retrieve the MC recursively with less computation and improved error per-formance (the solution is of total least square meaning). Moreover, the speed estimator is ap-plied to the scalar drive with experimental verification, the overall system is well behaved, and the MCA method enhanced by neural networks has provided a good potential in the ap-plication of harmonics retrieve

Series Active Filters (SAF) are designed for harmonic isolation and load voltage regulation of single-phase and three-phase voltage harmonic source type nonlinear loads. The novel Absolute Value Method (AVM) for load voltage harmonic extraction is proposed and applied in the control algorithm of SAF. The SAF compensated systems are represented by simplified linear models such that SAF controller gains can be easily determined. Harmonic isolation and load voltage regulation performances of 2.5 kW single-phase and 10 kW three-phase SAF compensated systems are evaluated by detailed simulations. Laboratory prototype single-phase and three-phase SAFs and loads are designed and manufactured. Digital signal processor based control platform is employed. Exclusive laboratory tests are conducted. Via laboratory experiments and simulations it is shown that AVM yields superior harmonic isolation and load voltage regulation performance compared to the conventional low/high pass filtering method. Theory, simulations, and experiments are well correlated and illustrate the feasibility of the proposed method.

L'amélioration du contraste des images échographiques occupe une place importante en imagerie ultrasonore médicale puisque la qualité des images impacte directement le diagnostic médical. Les imageries de contraste sous et ultra-harmoniques sont deux techniques permettant de produire des images à fort contraste. Le développement de techniques de filtrage, permettant de mieux extraire les composantes sous et ultra-harmoniques présentes dans les signaux renvoyés par les produits de contraste ultrasonore, a pour conséquence d'améliorer encore plus le contraste des images. Les modèles non linéaires dits " boîtes noires " du type Volterra, Hammerstein, Wiener, sont souvent utilisés pour identifier et modéliser les systèmes non linéaires tels que les produits de contraste ultrasonore. Ces modèles, bien que très bien adaptés pour modéliser les composantes harmoniques entières, ne le sont plus en présence de sous et ultra-harmoniques. Dans les années 2000, une méthode permettant la modélisation de sous et ultra-harmoniques en utilisant un modèle de Volterra à plusieurs entrées (Muliple Input Single Output : MISO) a été développée. Pourtant, bien que cette méthode soit complètement originale dans le domaine considéré, les points clés qui la sous-tendent ne sont pas clairement identifiés. C'est en identifiant clairement les points clés de la méthode existante que nous pouvons la généraliser et proposer plusieurs nouveaux paradigmes pour la modélisation et l'extraction de sous et ultra-harmoniques. Nous proposons alors des alternatives beaucoup plus simples du type mono-entrée mono-sortie (Single Input Single Output: SISO). En modulant en fréquence l'entrée du système, nous avons pu ainsi identifier les réponses impulsionnelles des différents canaux du modèle de Hammerstein généralisé. À partir de ce cadre général, nous avons aussi proposé de nouvelles approches pour réduire la complexité du modèle de Volterra.

The work of this thesis focuses on how to construct a smooth and periodic motion from motion capture data of a motion with a periodic structure, such as walk cycles. To identify periodic structures in the motion capture data we rely on previously developed methods from algebraic topology to project each frame of the motion capture data onto a circle. This gives us a description of how the periodic motion looks structurally. To construct a typical periodic motion from this projection we propose a harmonic regression model on the positional joint rotations. To prevent overfitting of the regression model we use the joints velocities and accelerations as constraints, which helps in maintaining a natural flow for the motion. Our method helps in preventing overfitting of the regression model, but the extracted motion can still suffer from temporal artefacts due to difficulties in performing the inverse mapping from structure to time. The method described is implemented as part of a plugin for Blender to provide a tool for performing periodic motion extraction in a 3D animation tool-kit.
Arbetet i detta examensarbete fokuserar på hur man kan konstruera en jämn och periodisk rörelse från motion capture data av en rörelse med periodisk struktur, som till exempel en gångcykel. För att identifiera periodiska strukturer i motion capture data använder vi tidigare utvecklade tekniker ifrån algebraisk topologi för att projicera varje bildruta av motion capture data på en cirkel. Detta ger oss en beskrivning av hur den periodiska rörelsen ser ut strukturellt. För att konstruera en typisk periodisk rörelse från denna projicering föreslår vi en harmonisk regressions modell på ledernas rotations positioner. För att förhindra overfitting av regressions modellen använder vi oss av ledernas hastigheter och accelerationer som restriktioner för att bibehålla det naturliga flödet hos rörelsen. Vår modell hjälper att förhindra overfitting av regressions modellen, men den extraherade rörelsen kan fortfarande lida av temporala artefakter på grund av svårigheter i att göra en inverterad mappning från struktur till tid. Metoden som beskrivits är implementerade i form av ett plugin till Blender för att ge tillgång till ett verktyg som kan extrahera periodiska rörelser i ett 3D animations verktyg.

This thesis investigates the detection and classification of small boats using a passive sonar system. Noise radiated from a small boats consists of broadband noise and harmonically related tones that correspond to parameters in the boats engine and propeller. A novel signal processing method for detection and discrimination of noise radiated from small boats has been developed. There are two main components to the algorithm. The first component detects the presence of small boats by the harmonic tonals radiated from the boat propeller and engine. The second component was designed to extract the a signature from passive sonar data. The Harmonic Extraction and Analysis Tool (HEAT) was designed to estimate the fundamental frequency of the harmonic tones, track the fundamental frequency using a Kalman filter, and automatically extract the amplitudes of the harmonic tonals to generate a harmonic signature for the boat. The algorithm is shown to accurately extract theses signatures, and results show that the signatures are unique enough that the same boat passing by the hydrophone multiple times can be recognized.

This thesis is concerned with the problem of automatic extraction of harmonic and rhythmic information from music audio signals using statistical framework and advanced signal processing methods. Among different research directions, automatic extraction of chords and key has always been of a great interest to Music Information Retrieval (MIR) community. Chord progressions and key information can serve as a robust mid-level representation for a variety of MIR tasks. We propose statistical approaches to automatic extraction of chord progressions using Hidden Markov Models (HMM) based framework. General ideas we rely on have already proved to be effective in speech recognition. We propose novel probabilistic approaches that include acoustic modeling layer and language modeling layer. We investigate the usage of standard N-grams and Factored Language Models (FLM) for automatic chord recognition. Another central topic of this work is the feature extraction techniques. We develop a set of new features that belong to chroma family. A set of novel chroma features that is based on the application of Pseudo-Quadrature Mirror Filter (PQMF) bank is introduced. We show the advantage of using Time-Frequency Reassignment (TFR) technique to derive better acoustic features. Tempo estimation and beat structure extraction are amongst the most challenging tasks in MIR community. We develop a novel method for beat/downbeat estimation from audio. It is based on the same statistical approach that consists of two hierarchical levels: acoustic modeling and beat sequence modeling. We propose the definition of a very specific beat duration model that exploits an HMM structure without self-transitions. A new feature set that utilizes the advantages of harmonic-impulsive component separation technique is introduced. The proposed methods are compared to numerous state-of-the-art approaches by participation in the MIREX competition, which is the best impartial assessment of MIR systems nowadays.

This thesis is concerned with the problem of automatic extraction of harmonic and rhythmic information from music audio signals using statistical framework and advanced signal processing methods. Among different research directions, automatic extraction of chords and key has always been of a great interest to Music Information Retrieval (MIR) community. Chord progressions and key information can serve as a robust mid-level representation for a variety of MIR tasks. We propose statistical approaches to automatic extraction of chord progressions using Hidden Markov Models (HMM) based framework. General ideas we rely on have already proved to be effective in speech recognition. We propose novel probabilistic approaches that include acoustic modeling layer and language modeling layer. We investigate the usage of standard N-grams and Factored Language Models (FLM) for automatic chord recognition. Another central topic of this work is the feature extraction techniques. We develop a set of new features that belong to chroma family. A set of novel chroma features that is based on the application of Pseudo-Quadrature Mirror Filter (PQMF) bank is introduced. We show the advantage of using Time-Frequency Reassignment (TFR) technique to derive better acoustic features. Tempo estimation and beat structure extraction are amongst the most challenging tasks in MIR community. We develop a novel method for beat/downbeat estimation from audio. It is based on the same statistical approach that consists of two hierarchical levels: acoustic modeling and beat sequence modeling. We propose the definition of a very specific beat duration model that exploits an HMM structure without self-transitions. A new feature set that utilizes the advantages of harmonic-impulsive component separation technique is introduced. The proposed methods are compared to numerous state-of-the-art approaches by participation in the MIREX competition, which is the best impartial assessment of MIR systems nowadays.

Music was the first mass-market industry to be completely restructured by digital technology, and today we can have access to thousands of tracks stored locally on our smartphone and millions of tracks through cloud-based music services. Given the vast quantity of music at our fingertips, we now require novel ways of describing, indexing, searching and interacting with musical content. In this thesis we focus on a technology that opens the door to a wide range of such applications: automatically estimating the pitch sequence of the melody directly from the audio signal of a polyphonic music recording, also referred to as melody extraction. Whilst identifying the pitch of the melody is something human listeners can do quite well, doing this automatically is highly challenging. We present a novel method for melody extraction based on the tracking and characterisation of the pitch contours that form the melodic line of a piece. We show how different contour characteristics can be exploited in combination with auditory streaming cues to identify the melody out of all the pitch content in a music recording using both heuristic and model-based approaches. The performance of our method is assessed in an international evaluation campaign where it is shown to obtain state-of-the-art results. In fact, it achieves the highest mean overall accuracy obtained by any algorithm that has participated in the campaign to date. We demonstrate the applicability of our method both for research and end-user applications by developing systems that exploit the extracted melody pitch sequence for similarity-based music retrieval (version identification and query-by-humming), genre classification, automatic transcription and computational music analysis. The thesis also provides a comprehensive comparative analysis and review of the current state-of-the-art in melody extraction and a first of its kind analysis of melody extraction evaluation methodology.
La industria de la música fue una de las primeras en verse completamente reestructurada por los avances de la tecnología digital, y hoy en día tenemos acceso a miles de canciones almacenadas en nuestros dispositivos móviles y a millones más a través de servicios en la nube. Dada esta inmensa cantidad de música al nuestro alcance, necesitamos nuevas maneras de describir, indexar, buscar e interactuar con el contenido musical. Esta tesis se centra en una tecnología que abre las puertas a nuevas aplicaciones en este área: la extracción automática de la melodía a partir de una grabación musical polifónica. Mientras que identificar la melodía de una pieza es algo que los humanos pueden hacer relativamente bien, hacerlo de forma automática presenta mucha complejidad, ya que requiere combinar conocimiento de procesado de señal, acústica, aprendizaje automático y percepción sonora. Esta tarea se conoce en el ámbito de investigación como “extracción de melodía”, y consiste técnicamente en estimar la secuencia de alturas correspondiente a la melodía predominante de una pieza musical a partir del análisis de la señal de audio. Esta tesis presenta un método innovador para la extracción de la melodía basado en el seguimiento y caracterización de contornos tonales. En la tesis, mostramos cómo se pueden explotar las características de contornos en combinación con reglas basadas en la percepción auditiva, para identificar la melodía a partir de todo el contenido tonal de una grabación, tanto de manera heurística como a través de modelos aprendidos automáticamente. A través de una iniciativa internacional de evaluación comparativa de algoritmos, comprobamos además que el método propuesto obtiene resultados punteros. De hecho, logra la precisión más alta de todos los algoritmos que han participado en la iniciativa hasta la fecha. Además, la tesis demuestra la utilidad de nuestro método en diversas aplicaciones tanto de investigación como para usuarios finales, desarrollando una serie de sistemas que aprovechan la melodía extraída para la búsqueda de música por semejanza (identificación de versiones y búsqueda por tarareo), la clasificación del estilo musical, la transcripción o conversión de audio a partitura, y el análisis musical con métodos computacionales. La tesis también incluye un amplio análisis comparativo del estado de la cuestión en extracción de melodía y el primer análisis crítico existente de la metodología de evaluación de algoritmos de este tipo
La indústria musical va ser una de les primeres a veure's completament reestructurada pels avenços de la tecnologia digital, i avui en dia tenim accés a milers de cançons emmagatzemades als nostres dispositius mòbils i a milions més a través de serveis en xarxa. Al tenir aquesta immensa quantitat de música al nostre abast, necessitem noves maneres de descriure, indexar, buscar i interactuar amb el contingut musical. Aquesta tesi es centra en una tecnologia que obre les portes a noves aplicacions en aquesta àrea: l'extracció automàtica de la melodia a partir d'una gravació musical polifònica. Tot i que identificar la melodia d'una peça és quelcom que els humans podem fer relativament fàcilment, fer-ho de forma automàtica presenta una alta complexitat, ja que requereix combinar coneixement de processament del senyal, acústica, aprenentatge automàtic i percepció sonora. Aquesta tasca es coneix dins de l'àmbit d'investigació com a “extracció de melodia”, i consisteix tècnicament a estimar la seqüència de altures tonals corresponents a la melodia predominant d'una peça musical a partir de l'anàlisi del senyal d'àudio. Aquesta tesi presenta un mètode innovador per a l'extracció de la melodia basat en el seguiment i caracterització de contorns tonals. Per a fer-ho, mostrem com es poden explotar les característiques de contorns combinades amb regles basades en la percepció auditiva per a identificar la melodia a partir de tot el contingut tonal d'una gravació, tant de manera heurística com a través de models apresos automàticament. A més d'això, comprovem a través d'una iniciativa internacional d'avaluació comparativa d'algoritmes que el mètode proposat obté resultats punters. De fet, obté la precisió més alta de tots els algoritmes proposats fins la data d'avui. A demés, la tesi demostra la utilitat del mètode en diverses aplicacions tant d'investigació com per a usuaris finals, desenvolupant una sèrie de sistemes que aprofiten la melodia extreta per a la cerca de música per semblança (identificació de versions i cerca per taral•larà), la classificació de l'estil musical, la transcripció o conversió d'àudio a partitura, i l'anàlisi musical amb mètodes computacionals. La tesi també inclou una àmplia anàlisi comparativa de l'estat de l'art en extracció de melodia i la primera anàlisi crítica existent de la metodologia d'avaluació d'algoritmes d'aquesta mena.

Dans le cadre de la séparation sélective pour le traitement et la valorisation des combustibles nucléaires usés, l'extraction liquide/liquide est largement utilisée au niveau industriel. Néanmoins, ce procédé est encore mal compris en ce qui concerne les phénomènes physico-chimiques qui se produisent à l'interface liquide/liquide. Ce travail porte sur la compréhension de la dynamique de l'interface liquide nanométrique durant le transfert d'une espèce entre une phase aqueuse et une phase organique. Deux techniques expérimentales ont principalement été utilisées: la mesure de tension interfaciale et l'optique non linéaire. Ce travail a également donné lieu au développement d'un modèle numérique de dynamique de transfert de phase prenant en compte à la fois des phénomènes de transport diffusif proche de l'interface et une cinétique chimique sur l'interface décrivant les processus d'adsorption/désorption. Des systèmes modèles constitués de molécules surfactantes et/ou chromophores et/ou complexantes ont été étudiés aux interfaces air/liquide et liquide/liquide. L'adsorption/désorption, l'agrégation en surface, la complexation d'ion à une interface liquide et la structuration des systèmes ont ainsi pu être étudiées tant d'un point de vue des états d'équilibre que de la dynamique. Enfin, ces études ont été appliquées à un système d'intérêt industriel utilisé dans le procédé de dépollution DIAMEX.

This thesis presents work on some power quality issues and mitigation techniques. It consists of six chapters. Chapter one presents a review of power quality issues and selfexcited induction generator systems. Chapter two consists of techniques for harmonic extraction in presence of non-linear load. Techniques used for harmonic extraction include d-q transformation, notch filter and p-q theory. Chapter three presents a number of conventional control algorithms for estimation of reference of supply currents and compensation of harmonics in presence of non-linear load in a three-phase-three-wire distribution system. This chapter discusses modeling and control of DSTATCOM which is operated in closed loop feedback for mitigation of power quality problems. The algorithms discussed in this chapter include Synchronous Reference Frame Theory, Instantaneous Reactive Power Theory, Modified Instantaneous Symmetrical Component Theory and Notch Filter. These algorithms have been modeled and the performance of system is discussed and compared. Chapter four presents harmonic extraction and mitigation techniques based on conventional and modified versions of pq theory in presence of non-linear load under distorted grid. Results have been presented for power factor correction and voltage regulation in presence of non-linear load. Chapter five discusses the modelling and control of Self-Excited Induction Generator for voltage regulation.

Diffusion MRI (dMRI) measurements allow us to infer the microstructural properties of white matter and to reconstruct fiber pathways in-vivo. High angular diffusion imaging (HARDI) allows for the creation of more and more complex local models connecting the microstructure to the measured signal. One of the challenges is the derivation of meaningful metrics describing the underlying structure from the local models. The aim hereby is to increase the specificity of the widely used metric fractional anisotropy (FA) by using the additional information contained within the HARDI data. A local model which is connected directly to the underlying microstructure through the model of a single fiber population is spherical deconvolution. It produces a fiber orientation density function (fODF), which can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle). Parameterizing these peaks one is able to disentangle and characterize these bundles. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which metrics for the parametric quantification of fiber bundles are derived. Meaningful relationships between these measures and the underlying microstructural properties are proposed. The focus lies on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. These metrics are compared to the conventionally used fractional anisotropy (FA) and it is shown how they may help to increase the specificity of the characterization of microstructural properties. Visualization of the micro-structural arrangement is another application of dMRI. This is done by using tractography to propagate the fiber layout, extracted from the local model, in each voxel. In practice most tractography algorithms use little of the additional information gained from HARDI based local models aside from the reconstructed fiber bundle directions. In this work an approach to tractography based on the Bingham parameterization of the fODF is introduced. For each of the fiber populations present in a voxel the diffusion signal and tensor are computed. Then tensor deflection tractography is performed. This allows incorporating the complete bundle information, performing local interpolation as well as using multiple directions per voxel for generating tracts. Another aspect of this work is the investigation of the spherical harmonic representation which is used most commonly for the fODF by means of the parameters derived from the Bingham distribution fit. Here a strong connection between the approximation errors in the spherical representation of the Dirac delta function and the distribution of crossing angles recovered from the fODF was discovered. The final aspect of this work is the application of the metrics derived from the Bingham fit to a number of fetal datasets for quantifying the brain’s development. This is done by introducing the Gini-coefficient as a metric describing the brain’s age.

Title: Cover Song Identification using Music Harmony Features, Model and Complexity Analysis Author: Ladislav Maršík Department: Department of Software Engineering Supervisor: Prof. RNDr. Jaroslav Pokorný, CSc., Department of Software Engineering Abstract: Analysis of digital music and its retrieval based on the audio fe- atures is one of the popular topics within the music information retrieval (MIR) field. Every musical piece has its characteristic harmony structure, but harmony analysis is seldom used for retrieval. Retrieval systems that do not focus on similarities in harmony progressions may consider two versions of the same song different, even though they differ only in instrumentation or a singing voice. This thesis takes various paths in exploring, how music harmony can be used in MIR, and in particular, the cover song identification (CSI) task. We first create a music harmony model based on the knowledge of music theory. We define novel concepts: a harmonic complexity of a mu- sical piece, as well as the chord and chroma distance features. We show how these concepts can be used for retrieval, complexity analysis, and how they compare with the state-of-the-art of music harmony modeling. An extensive comparison of harmony features is then performed, using both the novel fe- atures and the...

碩士
國立臺北科技大學
電腦與通訊研究所
98
Robust pitch extraction under high level of noise interference is an important issue for many applications. In this paper, a new frequency modulation harmonic and noise model (FM-HNM) is proposed for robust pitch contour extraction. The main idea is that fundamental frequency is usually not stable and varying over time. Therefore, the set of sinusoid bases in traditional HNM should be modified to reflect this phenomenon. The performance of the method is evaluated using the Keele pitch detection database with artificially added background noise, and compare with several standard methods, including RAPT(Robust Algorithm for Pitch Tracking), voicebox RAPT, YIN, STRAIGHT(Speech Transformation and Representation based on Adaptive Interpolation of weighted spectrogram). It is found from experimental results that when take voiced/unvoiced decision error and traditional GEC(Gross Error Count) into account at the same time, (1) RAPT is most stable in all conditions, (2) STRAIGHT has better performance in clean and white noise situation, but (3) in babble noise case, FM-HNM is better than others. Therefore, the proposed FM-HNM is promising.

Music analysis and source separation have become important and allied areas of research over the last decade. Towards this, analyzing a music signal for important events such as onsets, offsets and transients are important problems. These tasks help in music source separation and transcription. Approaches in source separation too have been making great strides, but most of these techniques are aimed at Western music and fail to perform well for Indian music. The fluid style of instrumentation in Indian music requires a slightly modified approach to analysis and source separation. We propose an onset detection algorithm that is motivated by the human auditory system. This algorithm has the advantage of having a unified framework for the detection of both onsets and offsets in music signals. This onset detection algorithm is further extended to detect percussive transients. Percussive transients have sharp onsets followed closely by sharp offsets. This characteristic is exploited in the percussive transients detection algorithm. This detection does not lend itself well to the extraction of transients and hence we propose an iterative algorithm to extract all types of transients from a polyphonic music signal. The proposed iterative algorithm is both fast and accurate to extract transients of various strengths. This problem of transient extraction can be extended to the problem of harmonic/percussion sound separation(HPSS), where a music signal is separated into two streams consisting of components mainly from percussion and harmonic instruments. Many algorithms that have been proposed till date deal with HPSS for Western music. But with Indian classical/film music, a different style of instrumentation or singing is seen, including high degree of vibratos or glissando content. This requires new approaches to HPSS. We propose extensions to two existing HPSS techniques, adapting them for Indian music. In both the extensions, we retain the original framework of the algorithm, showing that it is easy to incorporate the changes needed to handle Indian music. We also propose a new HPSS algorithm that is inspired by our transient extraction technique. This algorithm can be considered a generalized extension to our transient extraction algorithm and showcases our view that HPSS can be considered as an extension to transient analysis. Even the best HPSS techniques have leakages of harmonic components into percussion and this can lead to poor performances in tasks like rhythm analysis. In order to reduce this leakage, we propose a post processing technique on the percussion stream of the HPSS algorithm. The proposed method utilizes signal stitching by exploiting a commonly used model for percussive envelopes. We also developed a vocals extraction algorithm from the harmonic stream of the HPSS algorithm. The vocals extraction follows the popular paradigm of extracting the predominant pitch followed by generation of the vocals signal corresponding to the pitch. We show that HPSS as a pre-processing technique gives an advantage in reducing the interference from percussive sources in the extraction stage. It is also shown that the performance of vocal extraction algorithms improve with the knowledge about locations of the vocal segments. This is shown with the help of an oracle to locate the vocal segments. The use of the oracle greatly reduces the interferences from other dominating sources in the extracted vocals signal.

Music analysis and source separation have become important and allied areas of research over the last decade. Towards this, analyzing a music signal for important events such as onsets, offsets and transients are important problems. These tasks help in music source separation and transcription. Approaches in source separation too have been making great strides, but most of these techniques are aimed at Western music and fail to perform well for Indian music. The fluid style of instrumentation in Indian music requires a slightly modified approach to analysis and source separation. We propose an onset detection algorithm that is motivated by the human auditory system. This algorithm has the advantage of having a unified framework for the detection of both onsets and offsets in music signals. This onset detection algorithm is further extended to detect percussive transients. Percussive transients have sharp onsets followed closely by sharp offsets. This characteristic is exploited in the percussive transients detection algorithm. This detection does not lend itself well to the extraction of transients and hence we propose an iterative algorithm to extract all types of transients from a polyphonic music signal. The proposed iterative algorithm is both fast and accurate to extract transients of various strengths. This problem of transient extraction can be extended to the problem of harmonic/percussion sound separation(HPSS), where a music signal is separated into two streams consisting of components mainly from percussion and harmonic instruments. Many algorithms that have been proposed till date deal with HPSS for Western music. But with Indian classical/film music, a different style of instrumentation or singing is seen, including high degree of vibratos or glissando content. This requires new approaches to HPSS. We propose extensions to two existing HPSS techniques, adapting them for Indian music. In both the extensions, we retain the original framework of the algorithm, showing that it is easy to incorporate the changes needed to handle Indian music. We also propose a new HPSS algorithm that is inspired by our transient extraction technique. This algorithm can be considered a generalized extension to our transient extraction algorithm and showcases our view that HPSS can be considered as an extension to transient analysis. Even the best HPSS techniques have leakages of harmonic components into percussion and this can lead to poor performances in tasks like rhythm analysis. In order to reduce this leakage, we propose a post processing technique on the percussion stream of the HPSS algorithm. The proposed method utilizes signal stitching by exploiting a commonly used model for percussive envelopes. We also developed a vocals extraction algorithm from the harmonic stream of the HPSS algorithm. The vocals extraction follows the popular paradigm of extracting the predominant pitch followed by generation of the vocals signal corresponding to the pitch. We show that HPSS as a pre-processing technique gives an advantage in reducing the interference from percussive sources in the extraction stage. It is also shown that the performance of vocal extraction algorithms improve with the knowledge about locations of the vocal segments. This is shown with the help of an oracle to locate the vocal segments. The use of the oracle greatly reduces the interferences from other dominating sources in the extracted vocals signal.

Mineral projects in the global South have been subjected to increasing opposition, especially from adjacent communities, ultimately forcing some firms to abandon government-permitted and financially viable projects. Consequently, the mining literature has become saturated with prescriptions for firms seeking to engage with impacted communities. This literature is limited in that: it has primarily focused on conflict-ridden situations; the role of context has been largely ignored; and there has been little validation of what works and why. This thesis responds to these limitations through an assessment of the Canadian firm Kinross Gold at Paracatu, Brazil, where company-community relations have seemingly been healthy for years. Kinross has exerted considerable effort to engage with the community through several initiatives. While not all initiatives have been effective, community relations are strong, especially when compared to other foreign-owned mines in Latin America. However, Paracatu also boasts a diversified economy, and positive population and governance characteristics. This research shows that both context and company practice are important in determining company-community relations.