Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking advantage of these different time and frequency features between the useful signals and the interferences, the interference mitigation is formulated as an optimization problem: a sparse and low-rank decomposition of a Hankel matrix constructed by lifting the measurements. Then, an iterative optimization algorithm is proposed to tackle it by exploiting the Alternating Direction of Multipliers (ADMM) scheme. Compared to the existing methods, the proposed approach does not need to detect the interference and also improves the estimation accuracy of the separated useful signals. Both numerical simulations with point-like targets and experiment results with distributed targets (i.e., raindrops) are presented to demonstrate and verify its performance. The results show that the proposed approach is generally applicable for interference mitigation in both stationary and moving target scenarios.Comment: 12 pages, 8 figure

Machine Learning for Beamforming in Audio, Ultrasound, and Radar

Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar. Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more. In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech. Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data. Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system

고유 특성을 활용한 음악에서의 보컬 분리

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부, 2018. 2. 이교구.보컬 분리란 음악 신호를 보컬 성분과 반주 성분으로 분리하는 일 또는 그 방법을 의미한다. 이러한 기술은 음악의 특정한 성분에 담겨 있는 정보를 추출하기 위한 전처리 과정에서부터, 보컬 연습과 같이 분리 음원 자체를 활용하는 등의 다양한 목적으로 사용될 수 있다. 본 논문의 목적은 보컬과 반주가 가지고 있는 고유한 특성에 대해 논의하고 그것을 활용하여 보컬 분리 알고리즘들을 개발하는 것이며, 특히 `특징 기반' 이라고 불리는 다음과 같은 상황에 대해 중점적으로 논의한다. 우선 분리 대상이 되는 음악 신호는 단채널로 제공된다고 가정하며, 이 경우 신호의 공간적 정보를 활용할 수 있는 다채널 환경에 비해 더욱 어려운 환경이라고 볼 수 있다. 또한 기계 학습 방법으로 데이터로부터 각 음원의 모델을 추정하는 방법을 배제하며, 대신 저차원의 특성들로부터 모델을 유도하여 이를 목표 함수에 반영하는 방법을 시도한다. 마지막으로, 가사, 악보, 사용자의 안내 등과 같은 외부의 정보 역시 제공되지 않는다고 가정한다. 그러나 보컬 분리의 경우 암묵 음원 분리 문제와는 달리 분리하고자 하는 음원이 각각 보컬과 반주에 해당한다는 최소한의 정보는 제공되므로 각각의 성질들에 대한 분석은 가능하다. 크게 세 종류의 특성이 본 논문에서 중점적으로 논의된다. 우선 연속성의 경우 주파수 또는 시간 측면으로 각각 논의될 수 있는데, 주파수축 연속성의 경우 소리의 음색적 특성을, 시간축 연속성은 소리가 안정적으로 지속되는 정도를 각각 나타낸다고 볼 수 있다. 또한, 저행렬계수 특성은 신호의 구조적 성질을 반영하며 해당 신호가 낮은 행렬계수를 가지는 형태로 표현될 수 있는지를 나타내며, 성김 특성은 신호의 분포 형태가 얼마나 성기거나 조밀한지를 나타낸다. 본 논문에서는 크게 두 가지의 보컬 분리 방법에 대해 논의한다. 첫 번째 방법은 연속성과 성김 특성에 기반을 두고 화성 악기-타악기 분리 방법 (harmonic-percussive sound separation, HPSS) 을 확장하는 방법이다. 기존의 방법이 두 번의 HPSS 과정을 통해 보컬을 분리하는 것에 비해 제안하는 방법은 성긴 잔여 성분을 추가해 한 번의 보컬 분리 과정만을 사용한다. 논의되는 다른 방법은 저행렬계수 특성과 성김 특성을 활용하는 것으로, 반주가 저행렬계수 모델로 표현될 수 있는 반면 보컬은 성긴 분포를 가진다는 가정에 기반을 둔다. 이러한 성분들을 분리하기 위해 강인한 주성분 분석 (robust principal component analysis, RPCA) 을 이용하는 방법이 대표적이다. 본 논문에서는 보컬 분리 성능에 초점을 두고 RPCA 알고리즘을 일반화하거나 확장하는 방식에 대해 논의하며, 트레이스 노름과 l1 노름을 각각 샤텐 p 노름과 lp 노름으로 대체하는 방법, 스케일 압축 방법, 주파수 분포 특성을 반영하는 방법 등을 포함한다. 제안하는 알고리즘들은 다양한 데이터셋과 대회에서 평가되었으며 최신의 보컬 분리 알고리즘들보다 더 우수하거나 비슷한 결과를 보였다.Singing voice separation (SVS) refers to the task or the method of decomposing music signal into singing voice and its accompanying instruments. It has various uses, from the preprocessing step, to extract the musical features implied in the target source, to applications for itself such as vocal training. This thesis aims to discover the common properties of singing voice and accompaniment, and apply it to advance the state-of-the-art SVS algorithms. In particular, the separation approach as follows, which is named `characteristics-based,' is concentrated in this thesis. First, the music signal is assumed to be provided in monaural, or as a single-channel recording. It is more difficult condition compared to multiple-channel recording since spatial information cannot be applied in the separation procedure. This thesis also focuses on unsupervised approach, that does not use machine learning technique to estimate the source model from the training data. The models are instead derived based on the low-level characteristics and applied to the objective function. Finally, no external information such as lyrics, score, or user guide is provided. Unlike blind source separation problems, however, the classes of the target sources, singing voice and accompaniment, are known in SVS problem, and it allows to estimate those respective properties. Three different characteristics are primarily discussed in this thesis. Continuity, in the spectral or temporal dimension, refers the smoothness of the source in the particular aspect. The spectral continuity is related with the timbre, while the temporal continuity represents the stability of sounds. On the other hand, the low-rankness refers how the signal is well-structured and can be represented as a low-rank data, and the sparsity represents how rarely the sounds in signals occur in time and frequency. This thesis discusses two SVS approaches using above characteristics. First one is based on the continuity and sparsity, which extends the harmonic-percussive sound separation (HPSS). While the conventional algorithm separates singing voice by using a two-stage HPSS, the proposed one has a single stage procedure but with an additional sparse residual term in the objective function. Another SVS approach is based on the low-rankness and sparsity. Assuming that accompaniment can be represented as a low-rank model, whereas singing voice has a sparse distribution, conventional algorithm decomposes the sources by using robust principal component analysis (RPCA). In this thesis, generalization or extension of RPCA especially for SVS is discussed, including the use of Schatten p-/lp-norm, scale compression, and spectral distribution. The presented algorithms are evaluated using various datasets and challenges and achieved the better comparable results compared to the state-of-the-art algorithms.Chapter 1 Introduction 1 1.1 Motivation 4 1.2 Applications 5 1.3 Definitions and keywords 6 1.4 Evaluation criteria 7 1.5 Topics of interest 11 1.6 Outline of the thesis 13 Chapter 2 Background 15 2.1 Spectrogram-domain separation framework 15 2.2 Approaches for singing voice separation 19 2.2.1 Characteristics-based approach 20 2.2.2 Spatial approach 21 2.2.3 Machine learning-based approach 22 2.2.4 informed approach 23 2.3 Datasets and challenges 25 2.3.1 Datasets 25 2.3.2 Challenges 26 Chapter 3 Characteristics of music sources 28 3.1 Introduction 28 3.2 Spectral/temporal continuity 29 3.2.1 Continuity of a spectrogram 29 3.2.2 Continuity of musical sources 30 3.3 Low-rankness 31 3.3.1 Low-rankness of a spectrogram 31 3.3.2 Low-rankness of musical sources 33 3.4 Sparsity 34 3.4.1 Sparsity of a spectrogram 34 3.4.2 Sparsity of musical sources 36 3.5 Experiments 38 3.6 Summary 39 Chapter 4 Singing voice separation using continuity and sparsity 43 4.1 Introduction 43 4.2 SVS using two-stage HPSS 45 4.2.1 Harmonic-percussive sound separation 45 4.2.2 SVS using two-stage HPSS 46 4.3 Proposed algorithm 48 4.4 Experimental evaluation 52 4.4.1 MIR-1k Dataset 52 4.4.2 Beach boys Dataset 55 4.4.3 iKala dataset in MIREX 2014 56 4.5 Conclusion 58 Chapter 5 Singing voice separation using low-rankness and sparsity 61 5.1 Introduction 61 5.2 SVS using robust principal component analysis 63 5.2.1 Robust principal component analysis 63 5.2.2 Optimization for RPCA using augmented Lagrangian multiplier method 63 5.2.3 SVS using RPCA 65 5.3 SVS using generalized RPCA 67 5.3.1 Generalized RPCA using Schatten p- and lp-norm 67 5.3.2 Comparison of pRPCA with robust matrix completion 68 5.3.3 Optimization method of pRPCA 69 5.3.4 Discussion of the normalization factor for λ 69 5.3.5 Generalized RPCA using scale compression 71 5.3.6 Experimental results 72 5.4 SVS using RPCA and spectral distribution 73 5.4.1 RPCA with weighted l1-norm 73 5.4.2 Proposed method: SVS using wRPCA 74 5.4.3 Experimental results using DSD100 dataset 78 5.4.4 Comparison with state-of-the-arts in SiSEC 2016 79 5.4.5 Discussion 85 5.5 Summary 86 Chapter 6 Conclusion and Future Work 88 6.1 Conclusion 88 6.2 Contributions 89 6.3 Future work 91 6.3.1 Discovering various characteristics for SVS 91 6.3.2 Expanding to other SVS approaches 92 6.3.3 Applying the characteristics for deep learning models 92 Bibliography 94 초 록 110Docto

Wave Propagation and Source Localization in Random and Refracting Media

This thesis focuses on understanding the way that acoustic and electromagnetic waves propagate through an inhomogeneous or turbulent environment, and analyzes the effect that this uncertainty has on signal processing algorithms. These methods are applied to determining the effectiveness of matched-field style source localization algorithms in uncertain ocean environments, and to analyzing the effect that random media composed of electrically large scatterers has on propagating waves. The first half of this dissertation introduces the frequency-difference autoproduct, a surrogate field quantity, and applies this quantity to passive acoustic remote sensing in waveguiding ocean environments. The frequency-difference autoproduct, a quadratic product of frequency-domain complex measured field values, is demonstrated to retain phase stability in the face of significant environmental uncertainty even when the related pressure field’s phase is as unstable as noise. This result demonstrates that a measured autoproduct (at difference frequencies less than 5 Hz) that is associated with a pressure field (measured in the hundreds of Hz) and which has propagated hundreds of kilometers in a deep ocean sound channel can be consistently cross-correlated with a calculated autoproduct. This cross-correlation is shown to give a cross-correlation coefficient that is more than 10 dB greater than the equivalent cross-correlation coefficient of the measured pressure field, demonstrating that the autoproduct is a stable alternative to the pressure field for array signal processing algorithms. The next major result demonstrates that the frequency-difference autoproduct can be used to passively localize remote unknown sound sources that broadcast sound hundreds of kilometers to a measuring device at hundreds of Hz frequencies. Because of the high frequency content of the measured pressure field, an equivalent conventional localization result is not possible using frequency-domain methods. These two primary contributions, recovery of frequency-domain phase stability and robust source localization, represent unique contributions to existing signal processing techniques. The second half of this thesis focuses on understanding electromagnetic wave propagation in a random medium composed of metallic scatterers placed within a background medium. This thesis focuses on developing new methods to compute the extinction and phase matrices, quantities related to Radiative Transfer theory, of a random medium composed of electrically large, interacting scatterers. A new method is proposed, based on using Monte Carlo simulation and full-wave computational electromagnetics methods simultaneously, to calculate the extinction coefficient and phase function of such a random medium. Another major result of this thesis demonstrates that the coherent portion of the field scattered by a configuration of the random medium is equivalent to the field scattered by a homogeneous dielectric that occupies the same volume as the configuration. This thesis also demonstrates that the incoherent portion of the field scattered by a configuration of the random medium, related to the phase function of the medium, can be calculated using buffer zone averaging. These methods are applied to model field propagation in a random medium, and propose an extension of single scattering theory that can be used to understand mean field propagation in relatively dense (tens of particles per cubic wavelength) random media composed of electrically large (up to 3 wavelengths long) conductors and incoherent field propagation in relatively dense (up to 5 particles per cubic wavelength) media composed of electrically large (up to two wavelengths) conductors. These results represent an important contribution to the field of incoherent, polarimetric remote sensing of the environment.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169886/1/geroskdj_1.pd

ADMM-Net for Communication Interference Removal in Stepped-Frequency Radar

Complex ADMM-Net, a complex-valued neural network architecture inspired by the alternating direction method of multipliers (ADMM), is designed for interference removal in super-resolution stepped frequency radar angle-range-doppler imaging. Tailored to an uncooperative scenario wherein a MIMO radar shares spectrum with communications, the ADMM-Net recovers the radar image---which is assumed to be sparse---and simultaneously removes the communication interference, which is modeled as sparse in the frequency domain owing to spectrum underutilization. The scenario motivates an $\ell_1$-minimization problem whose ADMM iteration, in turn, undergirds the neural network design, yielding a set of generalized ADMM iterations that have learnable hyperparameters and operations. To train the network we use random data generated according to the radar and communication signal models. In numerical experiments ADMM-Net exhibits markedly lower error and computational cost than ADMM and CVX

Inaudible acoustics: Techniques and applications

This dissertation is focused on developing a sub-area of acoustics that we call inaudible acoustics. We have developed two core capabilities, (1) BackDoor and (2) Ripple, and demonstrated their use in various mobile and IoT applications. In BackDoor, we synthesize ultrasound signals that are inaudible to humans yet naturally recordable by all microphones. Importantly, the microphone does not require any modification, enabling billions of microphone-enabled devices, including phones, laptops, voice assistants, and IoT devices, to leverage the capability. Example applications include acoustic data beacons, acoustic watermarking, and spy-microphone jamming. In Ripple, we develop modulation and sensing techniques for vibratory signals that traverse through solid surfaces, enabling a new form of secure proximal communication. Applications of the vibratory communication system include on-body communication through imperceptible physical vibrations and device-device secure data transfer through physical contacts. Our prototypes include an inaudible jammer that secures private conversations from electronic eavesdropping, acoustic beacons for location-based information sharing, and vibratory communication in a smart-ring sending password through a finger touch. Our research also uncovers new security threats to acoustic devices. While simple abuse of inaudible jammer can disable hearing aids and cell phones, our work shows that voice interfaces, such as Amazon Echo, Google Home, Siri, etc., can be compromised through carefully designed inaudible voice commands. The contributions of this dissertation can be summarized in three primitives: (1) exploiting inherent hardware nonlinearity for sensing out-of-band signals, (2) developing the vibratory communication system for secure touch-based data exchange, and (3) structured information reconstruction from noisy acoustic signals. In developing these primitives, we draw from principles in wireless networking, digital communications, signal processing, and embedded design and translate them to completely functional systems

Contribution to dimensionality reduction of digital predistorter behavioral models for RF power amplifier linearization

The power efficiency and linearity of radio frequency (RF) power amplifiers (PAs) are critical in wireless communication systems. The main scope of PA designers is to build the RF PAs capable to maintain high efficiency and linearity figures simultaneously. However, these figures are inherently conflicted to each other and system-level solutions based on linearization techniques are required. Digital predistortion (DPD) linearization has become the most widely used solution to mitigate the efficiency versus linearity trade-off. The dimensionality of the DPD model depends on the complexity of the system. It increases significantly in high efficient amplification architectures when considering current wideband and spectrally efficient technologies. Overparametrization may lead to an ill-conditioned least squares (LS) estimation of the DPD coefficients, which is usually solved by employing regularization techniques. However, in order to both reduce the computational complexity and avoid ill-conditioning problems derived from overparametrization, several efforts have been dedicated to investigate dimensionality reduction techniques to reduce the order of the DPD model. This dissertation contributes to the dimensionality reduction of DPD linearizers for RF PAs with emphasis on the identification and adaptation subsystem. In particular, several dynamic model order reduction approaches based on feature extraction techniques are proposed. Thus, the minimum number of relevant DPD coefficients are dynamically selected and estimated in the DPD adaptation subsystem. The number of DPD coefficients is reduced, ensuring a well-conditioned LS estimation while demanding minimum hardware resources. The presented dynamic linearization approaches are evaluated and compared through experimental validation with an envelope tracking PA and a class-J PA The experimental results show similar linearization performance than the conventional LS solution but at lower computational cost.La eficiencia energetica y la linealidad de los amplificadores de potencia (PA) de radiofrecuencia (RF) son fundamentales en los sistemas de comunicacion inalambrica. El principal objetivo a alcanzar en el diserio de amplificadores de radiofrecuencia es lograr simultaneamente elevadas cifras de eficiencia y de linealidad. Sin embargo, estas cifras estan inherentemente en conflicto entre si, y se requieren soluciones a nivel de sistema basadas en tecnicas de linealizacion. La linealizacion mediante predistorsion digital (DPD) se ha convertido en la solucion mas utilizada para mitigar el compromise entre eficiencia y linealidad. La dimension del modelo del predistorsionador DPD depende de la complejidad del sistema, y aumenta significativamente en las arquitecturas de amplificacion de alta eficiencia cuando se consideran los actuales anchos de banda y las tecnologfas espectralmente eficientes. El exceso de parametrizacion puede conducir a una estimacion de los coeficientes DPD, mediante minimos cuadrados (LS), mal condicionada, lo cual generalmente se resuelve empleando tecnicas de regularizacion. Sin embargo, con el fin de reducir la complejidad computacional y evitar dichos problemas de mal acondicionamiento derivados de la sobreparametrizacion, se han dedicado varies esfuerzos para investigar tecnicas de reduccion de dimensionalidad que permitan reducir el orden del modelo del DPD. Esta tesis doctoral contribuye a aportar soluciones para la reduccion de la dimension de los linealizadores DPD para RF PA, centrandose en el subsistema de identificacion y adaptacion. En concrete, se proponen varies enfoques de reduccion de orden del modelo dinamico, basados en tecnicas de extraccion de caracteristicas. El numero minimo de coeficientes DPD relevantes se seleccionan y estiman dinamicamente en el subsistema de adaptacion del DPD, y de este modo la cantidad de coeficientes DPD se reduce, lo cual ademas garantiza una estimacion de LS bien condicionada al tiempo que exige menos recursos de hardware. Las propuestas de linealizacion dinamica presentados en esta tesis se evaluan y comparan mediante validacion experimental con un PA de seguimiento de envolvente y un PA tipo clase J. Los resultados experimentales muestran unos resultados de linealizacion de los PA similares a los obtenidos cuando se em plea la solucion LS convencional, pero con un coste computacional mas reducido.Postprint (published version