Improved Convolutive and Under-Determined Blind Audio Source Separation with MRF Smoothing

Convolutive and under-determined blind audio source separation from noisy recordings is a challenging problem. Several computational strategies have been proposed to address this problem. This study is concerned with several modifications to the expectation-minimization-based algorithm, which iteratively estimates the mixing and source parameters. This strategy assumes that any entry in each source spectrogram is modeled using superimposed Gaussian components, which are mutually and individually independent across frequency and time bins. In our approach, we resolve this issue by considering a locally smooth temporal and frequency structure in the power source spectrograms. Local smoothness is enforced by incorporating a Gibbs prior in the complete data likelihood function, which models the interactions between neighboring spectrogram bins using a Markov random field. Simulations using audio files derived from stereo audio source separation evaluation campaign 2008 demonstrate high efficiency with the proposed improvement

Over-Determined Source Separation and Localization Using Distributed Microphones

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Online source separation in reverberant environments exploiting known speaker locations

This thesis concerns blind source separation techniques using second order statistics and higher order statistics for reverberant environments. A focus of the thesis is algorithmic simplicity with a view to the algorithms being implemented in their online forms. The main challenge of blind source separation applications is to handle reverberant acoustic environments; a further complication is changes in the acoustic environment such as when human speakers physically move. A novel time-domain method which utilises a pair of finite impulse response filters is proposed. The method of principle angles is defined which exploits a singular value decomposition for their design. The pair of filters are implemented within a generalised sidelobe canceller structure, thus the method can be considered as a beamforming method which cancels one source. An adaptive filtering stage is then employed to recover the remaining source, by exploiting the output of the beamforming stage as a noise reference. A common approach to blind source separation is to use methods that use higher order statistics such as independent component analysis. When dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source vector prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. The final algorithm is realised as a real-time embedded application on a floating point Texas Instruments digital signal processor platform. Moving sources, along with reverberant environments, cause significant problems in realistic source separation systems as mixing filters become time variant. A method which employs the pair of cancellation filters, is proposed to cancel one source coupled with an online natural gradient independent vector analysis technique to improve average separation performance in the context of step-wise moving sources. This addresses `dips' in performance when sources move. Results show the average convergence time of the performance parameters is improved. Online methods introduced in thesis are tested using impulse responses measured in reverberant environments, demonstrating their robustness and are shown to perform better than established methods in a variety of situations

산업용 로봇 고장 진단을 위한 암묵신호 분리 기반 다축 간섭 최소화 기법

학위논문(석사)--서울대학교 대학원 :공과대학 기계항공공학부,2019. 8. 윤병동.As smart factory is becoming popular, industrial robots are highly demanding in many manufacturing fields for factory automation. Unpredictable faults in the industrial robot could bring about interruptions in the whole manufacturing process. Therefore, many methods have been developed for fault detection of the industrial robots. Because gearboxes are the main parts in the power transmission system of industrial robots, fault detection of the gearboxes has been widely investigated. Especially, vibration analysis is a well-established technique for fault detection of the industrial robot gearbox. However, the vibration signals from the gearboxes are mixed convolutively and linearly at each axes, which makes it difficult to locate a damaged gearbox, and reduce fault detection performance. Thus, this paper develops a vibration signal separation technique for fault detection of industrial robot gearboxes under multi-axis interference. The developed method includes two steps, frequency domain independent component analysis (ICA-FD) and time domain independent component analysis (ICA-TD). ICA-FD is aimed at separating convolutive mixture of signals, and ICA-TD is aimed at eliminating the residual mixed components. The experiment is performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method could successfully separate the mixed signals by obtaining vibration signals from each gearbox, and enhance fault detection performance for the industrial robot gearboxes.Chapter 1. Introduction 1 1.1 Background and Motivation . 1 1.2 Scope of Research 1 1.3 Structure of the Thesis . 5 Chapter 2. Structure of Industrial Robot . 6 2.1 Structure of Experimental Robot 6 2.2 Problem in Industrial Robot Fault Detection . 8 Chapter 3. Methodology 10 3.1. Time Domain Independent Component Analysis (ICA-TD) . 10 3.2. Frequency Domain Independent Component Analysis (ICA-FD) 12 3.2.1 Separation 12 3.2.2 Permutation . 14 3.2.3 Scaling . 17 3.3. Multi-stage Independent Component Analysis (MSICA) . 17 Chapter 4. Experiment Evaluation . 19 4.1 Experiment with MSICA 19 4.1.1 Experiment Process . 19 4.1.2 Result Analysis 28 4.2 Comparison Experiment Using Basic ICA Method . 33 4.3 Comparison Experiment Using ICA-FD Method . 38 Chapter 5. Discussion and Conclusion . 45 5.1 Conclusions and Contributions 45 5.2 Future Work 46 Bibliography . 47Maste