Synchrophasor Assisted Efficient Fault Location Techniques In An Active Distribution Network

Reliability of an electrical system can be improved by an efficient fault location identification for the fast repair and remedial actions. This scenario changes when there are large penetrations of distributed generation (DG) which makes the distribution system an active distribution system. An efficient use of synchrophasors in the distribution network is studied with bidirectional power flow, harmonics and low angle difference consideration which are not prevalent in a transmission network. A synchrophasor estimation algorithm for the P class PMU is developed and applied to identify efficient fault location. A fault location technique using two ended synchronized measurement is derived from the principle of transmission line settings to work in a distribution network which is independent of line parameters. The distribution systems have less line length, harmonics and different sized line conductors, which affects the sensitivity of the synchronized measurements, Total Vector Error (TVE) and threshold for angular separation between different points in the network. A new signal processing method based on Discrete Fourier Transform (DFT) is utilized to work in a distribution network as specified in IEEE C37.118 (2011) standard for synchrophasor. A specific P and M classes of synchrophasor measurements are defined in the standard. A tradeoff between fast acting P class and detailed measurement M class is sought to work specifically in the distribution system settings which is subjected to large amount of penetrations from the renewable energy

A hovercraft robot that uses insect-inspired visual autocorrelation for motion control in a corridor

Abstract-In this paper we are concerned with the challenge of flight control of computationally-constrained micro-aerial vehicles that must rely primarily on vision to navigate confined spaces. We turn to insects for inspiration. We demonstrate that it is possible to control a robot with inertial, flight-like dynamics in the plane using insect-inspired visual autocorrelators or "elementary motion detectors" (EMDs) to detect patterns of visual optic flow. The controller, which requires minimal computation, receives visual information from a small omnidirectional array of visual sensors and computes thrust outputs for a fan pair to stabilize motion along the centerline of a corridor. To design the controller, we provide a frequencydomain analysis of the response of an array of correlators to a flat moving wall. The model incorporates the effects of motion parallax and perspective and provides a means for computing appropriate inter-sensor angular spacing and visual blurring. The controller estimates the state of robot motion by decomposing the correlator response into harmonics, an analogous operation to that performed by tangential cells in the fly. This work constitutes the first-known demonstration of control of non-kinematic inertial dynamics using purely correlators

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

Characterization of the singing voice from polyphonic recordings

In order to study the singing voice, researchers have traditionally relied upon lab-based experiments and/or simplified models. Neither of these methods can reasonably be expected to always capture the essence of true performances in environmentally valid settings. Unfortunately, true performances are generally much more difficult to work with because they lack precisely the controls that lab setups and artificial models afford. In particular, true performances are generally polyphonic, making it much more difficult to analyze individual voices than if the voices can be studied in isolation. This thesis approaches the problem of polyphony head on, using a time-aligned electronic score to guide estimation of the vocal line characteristics. First, the exact fundamental frequency track for the voice is estimated using the score notes as guides. Next, the harmonic strengths are estimated using the fundamental frequency information. Third, estimates in notes are automatically validated or discarded based on characteristics of the frequency tracks. Lastly, good harmonic estimates are smoothed across time in order to improve the harmonic strength estimates. These final harmonic estimates, along with the fundamental frequency track estimates, parameterize the essential characteristics of what we hear singers' voices. To explore the potential power of this parameterization, the algorithms are applied to a real data consisting of five sopranos singing six arias. Vowel modification and evidence for the singer's formant are explored

Development of a Data Acquisition System for Unmanned Aerial Vehicle (UAV) System Identification

Aircraft system identification techniques are developed for fixed wing Unmanned Aerial Vehicles (UAV). The use of a designed flight experiment with measured system inputs/outputs can be used to derive aircraft stability derivatives. This project set out to develop a methodology to support an experiment to model pitch damping in the longitudinal short-period mode of a UAV. A Central Composite Response Surface Design was formed using angle of attack and power levels as factors to test for the pitching moment coefficient response induced by a multistep pitching maneuver. Selecting a high-quality data acquisition platform was critical to the success of the project. This system was designed to support fixed wing research through the addition of a custom air data vane capable of measuring angle of attack and sideslip, as well as an airspeed sensor. A Pixhawk autopilot system serves as the core and modification of the device firmware allowed for the integration of custom sensors and custom RC channels dedicated to performing system identification maneuvers. Tests were performed on all existing Pixhawk sensors to validate stated uncertainty values. The air data system was calibrated in a low speed wind tunnel and dynamic performance was verified. The assembled system was then installed in a commercially available UAV known as an Air Titan FPV in order to test the Pixhawk’s automated flight maneuvers and determine the final performance of each sensor. Flight testing showed all the critical sensors produced acceptable data for further research. The Air Titan FPV airframe was found to be very flexible and did not lend itself well to accurate measurement of inertial properties. This realization prohibited the construction of the required math models for longitudinal dynamics. It is recommended that future projects using the developed methods choose an aircraft with a more rigid airframe

A Summary of NASA Rotary Wing Research: Circa 20082018

The general public may not know that the first A in NASA stands for Aeronautics. If they do know, they will very likely be surprised that in addition to airplanes, the A includes research in helicopters, tiltrotors, and other vehicles adorned with rotors. There is, arguably, no subsonic air vehicle more difficult to accurately analyze than a vehicle with lift-producing rotors. No wonder that NASA has conducted rotary wing research since the days of the NACA and has partnered, since 1965, with the U.S. Army in order to overcome some of the most challenging obstacles to understanding the behavior of these vehicles. Since 2006, NASA rotary wing research has been performed under several different project names [Gorton et al., 2015]: Subsonic Rotary Wing (SRW) (20062012), Rotary Wing (RW) (20122014), and Revolutionary Vertical Lift Technology (RVLT) (2014present). In 2009, the SRW Project published a report that assessed the status of NASA rotorcraft research; in particular, the predictive capability of NASA rotorcraft tools was addressed for a number of technical disciplines. A brief history of NASA rotorcraft research through 2009 was also provided [Yamauchi and Young, 2009]. Gorton et al. [2015] describes the system studies during 20092011 that informed the SRW/RW/RVLT project investment prioritization and organization. The authors also provided the status of research in the RW Project in engines, drive systems, aeromechanics, and impact dynamics as related to structural dynamics of vertical lift vehicles. Since 2009, the focus of research has shifted from large civil VTOL transports, to environmentally clean aircraft, to electrified VTOL aircraft for the urban air mobility (UAM) market. The changing focus of rotorcraft research has been a reflection of the evolving strategic direction of the NASA Aeronautics Research Mission Directorate (ARMD). By 2014, the project had been renamed the Revolutionary Vertical Lift Technology Project. In response to the 2014 NASA Strategic Plan, ARMD developed six Strategic Thrusts. Strategic Thrust 3B was defined as the Ultra-Efficient Commercial VehiclesVertical Lift Aircraft. Hochstetler et al. [2017] uses Thrust 3B as an example for developing metrics usable by ARMD to measure the effectiveness of each of the Strategic Thrusts. The authors provide near-, mid-, and long-term outcomes for Thrust 3B with corresponding benefits and capabilities. The importance of VTOL research, especially with the rapidly expanding UAM market, eventually resulted in a new Strategic Thrust (to begin in 2020): Thrust 4Safe, Quiet, and Affordable Vertical Lift Air Vehicles. The underlying rotary wing analysis tools used by NASA are still applicable to traditional rotorcraft and have been expanded in capability to accommodate the growing number of VTOL configurations designed for UAM. The top-level goal of the RVLT Project remains unchanged since 2006: Develop and validate tools, technologies and concepts to overcome key barriers for vertical lift vehicles. In 2019, NASA rotary wing/VTOL research has never been more important for supporting new aircraft and advancements in technology. 2 A decade is a reasonable interval to pause and take stock of progress and accomplishments. In 10 years, digital technology has propelled progress in computational efficiency by orders of magnitude and expanded capabilities in measurement techniques. The purpose of this report is to provide a compilation of the NASA rotary wing research from ~2008 to ~2018. Brief summaries of publications from NASA, NASA-funded, and NASA-supported research are provided in 12 chapters: Acoustics, Aeromechanics, Computational Fluid Dynamics (External Flow), Experimental Methods, Flight Dynamics and Control, Drive Systems, Engines, Crashworthiness, Icing, Structures and Materials, Conceptual Design and System Analysis, and Mars Helicopter. We hope this report serves as a useful reference for future NASA vertical lift researchers

EFFICIENT PARAMETER ESTIMATION METHODS FOR AUTOMOTIVE RADAR SYSTEMS

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 김성철.As the demand for safety and convenience in the automotive-technology field increased, many applications of advanced driving assistance systems were developed. To provide driving information, among the sensors, such as cameras sensor, light detection and ranging sensor, radar sensor, and ultrasonic sensor, a radar sensor is known to exhibit excellent performance in terms of visibility for different weather conditions. Especially with the legislation of the adaptive cruise control system and autonomous emergency braking system in a global environment, the market of the automotive radar sensor is expected to grow explosively. At present, the development of cost-effective radar offering high performance with small size is required. In addition, the radar system should be enforced to have a simultaneous functionality for both long and short ranges. Thus, challenging issues still remain with respect to radar signal processing including high-resolution parameter estimation, multi-target detection, clutter suppression, and interference mitigation. For high-resolution parameter estimation, direction-of-arrival (DOA) estimation method has been investigated to identify the target object under complex unban environment. To separate closely spaced target having similar range and distance, high-resolution techniques, such as multiple signal classification (MUSIC), the estimation of signal parameters via rotational invariance techniques (ESPRIT), and maximum likelihood (ML) algorithm, are applied for automotive radars. In general, cycle time for radar system, which is the processing time for one snapshot, is very short, thus to establish a high-resolution estimation algorithm with computational efficiency is additional issue. On the other hands, multi-target detection scheme is required to identify many targets in the field of view. Multi-target detection is regarded as target pairing solution, whose task is to associate frequency components obtained from multiple targets. Under certain conditions, the association may fail and real target may be combined to ghost components. Thus, reliable paring or association method is essential for automotive radar systems. The clutter denotes undesired echoes due to reflected wave from background environment, which includes guardrail, traffic signs, and stationary structures around the load. To minimize the effect of clutter, conventional radar systems use high pass filter based on the assumption that the clutter is stationary with energy concentrated in the low frequency domain. However, the clutter is presented with various energy and frequency under automotive radar environment. Especially, under the specific environment with iron materials, target component is not detected due to clutter with large power. Mutual interference is a crucial issue that must be resolved for improved safety functions. Given the increasing number of automotive radar sensors operating at the same instant, the probability that radar sensors may receive signals from other radar sensors gradually increases. In such a situation, the system may fail to detect the correct target given the serious interference. Effective countermeasures, therefore, have to be considered. In this dissertation, we propose efficient parameter estimation methods for automotive radar system. The proposed methods include the radar signal processing issues as above described, respectively. First, the high-resolution DOA estimation method is proposed by using frequency domain analysis. The scheme is based on the MUSIC algorithm, which use distinct beat frequency of the target. The target beat frequency also gives distance and velocity. Thus, the proposed algorithm provides either high-resolution angle information of target or natural target pairing solution. Secondly, we propose the clutter suppression method under iron-tunnel conditions. The clutter in iron-tunnel environments is known to severely degrade the target detection performance because of the signal reflection from iron structures. The suppression scheme is based on cepstral analysis of received signal. By using periodical characteristic of the iron-tunnel clutter, the suppressed frequency response is obtained. Finally, the interference mitigation scheme is studied. Mutual interference between frequency modulated continuous waveform (FMCW) radars appears in the form of increased noise levels in the frequency domain and results in a failure to separate the target object from interferer. Thus, we propose a high-resolution frequency estimation technique for use in interference environments.Chapter 1. Introduction 1 1.1 Background 1 1.2 ADAS Applications for Automotive Radar 3 1.3 Motivation and Organization 5 Chapter 2. High-Resolution Direction-of-Arrvial Estimation with Pairing function for Automotive Radar Systems 8 2.1 Introduction 8 2.2 High-Resolution DOA Estimation for automotive Radars 10 2.2.1 DOA Estimation in the Time-domain Processing 11 2.2.2 DOA Estimation in the Frequency-domain Processing 15 2.3 Simulation Result 18 2.3.1 Simulation setup 18 2.3.2 Performance Comparison of the DOA Estimation in Time- and Frquency-domain Processing 19 2.3.3 Performance Analysis of the DOA Estimation in Frequency-domain 23 2.4 Conclusion 26 Chapter 3. Clutter Suppression Method of Iron Tunnel using Cepstral Analysis for Automotive Radars 27 3.1 Introduction 27 3.2 Clutter Suppression under Iron Tunnels 30 3.2.1 Radar Model of an Iron Tunnel 30 3.2.2 Cepstrum Analysis of an Iron Tunnel 33 3.2.3 Cepstrum Based Clutter Suppression Method 36 3.3 Experimental Result 39 3.4 Conclusion 46 Chapter 4. Interference Mitigation by High-Resolution Frequency Estimation in Automotive FMCW Radar 47 4.1 Introduction 47 4.2 Automotive FMCW Radars in an Interference Environment 50 4.2.1 The Same Sign-Chirp Case 54 4.2.2 The Different Sign-Chirp Case 56 4.3 High-Resolution Frequency Estimation Method 58 4.3.1 Data Model 58 4.3.2 Estimation of Correlation Matrix 61 4.3.3 Application of the MUSIC Algorithm 62 4.3.4 Application of the MUSIC Algorithm 63 4.3.5 Number of Frequency Estimation 65 4.4 Experimental Result 66 4.5 Conclusion 71 Bibliography 72 Abstract in Korean 78Docto

Signal Identification In Discrete-Time Based On Internal-Model-Principle

This work presents an implementation of a signal identification algorithm which is based on the internal model principle. By using several internal models in feedback with a tuning function, this algorithm can decompose a signal into narrow-band signals and identify the frequencies, amplitudes and relative phases. A desired band-pass filter response can be achieved by selecting appropriate coefficients of the controllers and tuning functions, which can reject the noise and improve the performance. To achieve a result with fast transient characteristics, this system is then modified by adding a low-pass filter. This work is based on the previous work in continuous time. However, a discrete implementation should be much more practical. The simulation result shows a good tracking of the original signal with minimal response to measurement noise

일반화된 디리클레 사전확률을 이용한 비지도적 음원 분리 방법

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부, 2018. 2. 이교구.Music source separation aims to extract and reconstruct individual instrument sounds that constitute a mixture sound. It has received a great deal of attention recently due to its importance in the audio signal processing. In addition to its stand-alone applications such as noise reduction and instrument-wise equalization, the source separation can directly affect the performance of the various music information retrieval algorithms when used as a pre-processing. However, conventional source separation algorithms have failed to show satisfactory performance especially without the aid of spatial or musical information about the target source. To deal with this problem, we have focused on the spectral and temporal characteristics of sounds that can be observed in the spectrogram. Spectrogram decomposition is a commonly used technique to exploit such characteristicshowever, only a few simple characteristics such as sparsity were utilizable so far because most of the characteristics were difficult to be expressed in the form of algorithms. The main goal of this thesis is to investigate the possibility of using generalized Dirichlet prior to constrain spectral/temporal bases of the spectrogram decomposition algorithms. As the generalized Dirichlet prior is not only simple but also flexible in its usage, it enables us to utilize more characteristics in the spectrogram decomposition frameworks. From harmonic-percussive sound separation to harmonic instrument sound separation, we apply the generalized Dirichlet prior to various tasks and verify its flexible usage as well as fine performance.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Task of interest 4 1.2.1 Number of channels 4 1.2.2 Utilization of side-information 5 1.3 Approach 6 1.3.1 Spectrogram decomposition with constraints 7 1.3.2 Dirichlet prior 11 1.3.3 Contribution 12 1.4 Outline of the thesis 13 Chapter 2 Theoretical background 17 2.1 Probabilistic latent component analysis 18 2.2 Non-negative matrix factorization 21 2.3 Dirichlet prior 23 2.3.1 PLCA framework 24 2.3.2 NMF framework 26 2.4 Summary 28 Chapter 3 Harmonic-Percussive Source Separation Using Harmonicity and Sparsity Constraints . . 30 3.1 Introduction 30 3.2 Proposed method 33 3.2.1 Formulation of Harmonic-Percussive Separation 33 3.2.2 Relation to Dirichlet Prior 35 3.3 Performance evaluation 37 3.3.1 Sample Problem 37 3.3.2 Qualitative Analysis 38 3.3.3 Quantitative Analysis 42 3.4 Summary 43 Chapter 4 Exploiting Continuity/Discontinuity of Basis Vectors in Spectrogram Decomposition for Harmonic-Percussive Sound Separation 46 4.1 Introduction 46 4.2 Proposed Method 51 4.2.1 Characteristics of harmonic and percussive components 51 4.2.2 Derivation of the proposed method 56 4.2.3 Algorithm interpretation 61 4.3 Performance Evaluation 62 4.3.1 Parameter setting 63 4.3.2 Toy examples 66 4.3.3 SiSEC 2015 dataset 69 4.3.4 QUASI dataset 84 4.3.5 Subjective performance evaluation 85 4.3.6 Audio demo 87 4.4 Summary 87 Chapter 5 Informed Approach to Harmonic Instrument sound Separation 89 5.1 Introduction 89 5.2 Proposed method 91 5.2.1 Excitation-filter model 92 5.2.2 Linear predictive coding 94 5.2.3 Spectrogram decomposition procedure 96 5.3 Performance evaluation 99 5.3.1 Experimental settings 99 5.3.2 Performance comparison 101 5.3.3 Envelope extraction 102 5.4 Summary 104 Chapter 6 Blind Approach to Harmonic Instrument sound Separation 105 6.1 Introduction 105 6.2 Proposed method 106 6.3 Performance evaluation 109 6.3.1 Weight optimization 109 6.3.2 Performance comparison 109 6.3.3 Effect of envelope similarity 112 6.4 Summary 114 Chapter 7 Conclusion and Future Work 115 7.1 Contributions 115 7.2 Future work 119 7.2.1 Application to multi-channel audio environment 119 7.2.2 Application to vocal separation 119 7.2.3 Application to various audio source separation tasks 120 Bibliography 121 초 록 137Docto

Pitch-Informed Solo and Accompaniment Separation

Das Thema dieser Dissertation ist die Entwicklung eines Systems zur Tonhöhen-informierten Quellentrennung von Musiksignalen in Soloinstrument und Begleitung. Dieses ist geeignet, die dominanten Instrumente aus einem Musikstück zu isolieren, unabhängig von der Art des Instruments, der Begleitung und Stilrichtung. Dabei werden nur einstimmige Melodieinstrumente in Betracht gezogen. Die Musikaufnahmen liegen monaural vor, es kann also keine zusätzliche Information aus der Verteilung der Instrumente im Stereo-Panorama gewonnen werden. Die entwickelte Methode nutzt Tonhöhen-Information als Basis für eine sinusoidale Modellierung der spektralen Eigenschaften des Soloinstruments aus dem Musikmischsignal. Anstatt die spektralen Informationen pro Frame zu bestimmen, werden in der vorgeschlagenen Methode Tonobjekte für die Separation genutzt. Tonobjekt-basierte Verarbeitung ermöglicht es, zusätzlich die Notenanfänge zu verfeinern, transiente Artefakte zu reduzieren, gemeinsame Amplitudenmodulation (Common Amplitude Modulation CAM) einzubeziehen und besser nichtharmonische Elemente der Töne abzuschätzen. Der vorgestellte Algorithmus zur Quellentrennung von Soloinstrument und Begleitung ermöglicht eine Echtzeitverarbeitung und ist somit relevant für den praktischen Einsatz. Ein Experiment zur besseren Modellierung der Zusammenhänge zwischen Magnitude, Phase und Feinfrequenz von isolierten Instrumententönen wurde durchgeführt. Als Ergebnis konnte die Kontinuität der zeitlichen Einhüllenden, die Inharmonizität bestimmter Musikinstrumente und die Auswertung des Phasenfortschritts für die vorgestellte Methode ausgenutzt werden. Zusätzlich wurde ein Algorithmus für die Quellentrennung in perkussive und harmonische Signalanteile auf Basis des Phasenfortschritts entwickelt. Dieser erreicht ein verbesserte perzeptuelle Qualität der harmonischen und perkussiven Signale gegenüber vergleichbaren Methoden nach dem Stand der Technik. Die vorgestellte Methode zur Klangquellentrennung in Soloinstrument und Begleitung wurde zu den Evaluationskampagnen SiSEC 2011 und SiSEC 2013 eingereicht. Dort konnten vergleichbare Ergebnisse im Hinblick auf perzeptuelle Bewertungsmaße erzielt werden. Die Qualität eines Referenzalgorithmus im Hinblick auf den in dieser Dissertation beschriebenen Instrumentaldatensatz übertroffen werden. Als ein Anwendungsszenario für die Klangquellentrennung in Solo und Begleitung wurde ein Hörtest durchgeführt, der die Qualitätsanforderungen an Quellentrennung im Kontext von Musiklernsoftware bewerten sollte. Die Ergebnisse dieses Hörtests zeigen, dass die Solo- und Begleitspur gemäß unterschiedlicher Qualitätskriterien getrennt werden sollten. Die Musiklernsoftware Songs2See integriert die vorgestellte Klangquellentrennung bereits in einer kommerziell erhältlichen Anwendung.This thesis addresses the development of a system for pitch-informed solo and accompaniment separation capable of separating main instruments from music accompaniment regardless of the musical genre of the track, or type of music accompaniment. For the solo instrument, only pitched monophonic instruments were considered in a single-channel scenario where no panning or spatial location information is available. In the proposed method, pitch information is used as an initial stage of a sinusoidal modeling approach that attempts to estimate the spectral information of the solo instrument from a given audio mixture. Instead of estimating the solo instrument on a frame by frame basis, the proposed method gathers information of tone objects to perform separation. Tone-based processing allowed the inclusion of novel processing stages for attack refinement, transient interference reduction, common amplitude modulation (CAM) of tone objects, and for better estimation of non-harmonic elements that can occur in musical instrument tones. The proposed solo and accompaniment algorithm is an efficient method suitable for real-world applications. A study was conducted to better model magnitude, frequency, and phase of isolated musical instrument tones. As a result of this study, temporal envelope smoothness, inharmonicty of musical instruments, and phase expectation were exploited in the proposed separation method. Additionally, an algorithm for harmonic/percussive separation based on phase expectation was proposed. The algorithm shows improved perceptual quality with respect to state-of-the-art methods for harmonic/percussive separation. The proposed solo and accompaniment method obtained perceptual quality scores comparable to other state-of-the-art algorithms under the SiSEC 2011 and SiSEC 2013 campaigns, and outperformed the comparison algorithm on the instrumental dataset described in this thesis.As a use-case of solo and accompaniment separation, a listening test procedure was conducted to assess separation quality requirements in the context of music education. Results from the listening test showed that solo and accompaniment tracks should be optimized differently to suit quality requirements of music education. The Songs2See application was presented as commercial music learning software which includes the proposed solo and accompaniment separation method