Machine Learning Source Separation Using Maximum a Posteriori Nonnegative Matrix Factorization

et al. 2016

IEEE Trans. Ind. Inf.

This paper proposes an unsupervised method for diagnosing and monitoring defects in inductive thermography imaging system. The proposed method is fully automated and does not require manual selection from the user of the specific thermal frame images for defect diagnosis. The core of the method is a hybrid of physics-based inductive thermal mechanism with signal processing-based pattern extraction algorithm using sparse greedy based Principal Component Analysis (SGPCA). An internal functionality is built into the proposed algorithm to control the sparsity of SGPCA. and to render better accuracy in sizing the defects. The proposed method is demonstrated on automatically diagnosing the defects on metals and the accuracy of sizing the defects. Experimental tests and comparisons with other methods have been conducted to verify the efficacy of the proposed method. Very promising results have been obtained where the performance of the proposed method is very near to human perception. Index Terms-Data analytics for diagnosis and monitoring, instrumentation, inductive thermal imaging, machine intelligence, non-destructive testing and evaluation. I. INTRODUCTION maging diagnostic system for defect detection is highly demanded in industry [1, 2]. This has been applied on inspection of electronic chips or dies in semiconductor production lines [3]. Acciani et al. extracted the features of the regions of interest in test images and then built multilayer neural networks for defect detection [4] on solder joints in surface mount technology of industry. Tsai et al. proposed defect inspection system of solar modules in electroluminescence (EL) images [5]. A. Picon et al. proposed fuzzy spectral and spatial feature integration method for classification of nonferrous materials in hyperspectral data [6]. All these methods recognize that image based defect diagnostic system is a wide group of analysis technique used in science and industry to evaluate the properties of material, component or system without causing damage [7-9]. Infrared thermography systems have reached a prominent status as a nondestructive testing and evaluation Manuscript received on

Section: Thermography Sparse Pattern Extraction 1) Observation Modelmentioning

confidence: 99%

Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System

Gao

et al. 2016

IEEE Trans. Ind. Inf.

“…Approximate sparsity is an important consideration as they represent important information. Many sparse solutions have been proposed in the last decade [19][20][21][22][23][24][25]. Nonetheless, the optimal sparse solution remains an open issue.…”

Section: Introductionmentioning

confidence: 99%

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Parathai

Tengtrairat

et al. 2020

Sensors

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.

“…A sparseness constraint can be added to the cost function [ 26 , 27 , 28 , 29 , 30 , 31 ], and this can be achieved by regularization using the L 1 -norm leading to Sparse NMF (SNMF). Here, “sparseness” refers to a representational scheme where only a few units (out of a large population) are effectively used to represent typical data vectors.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning for Monaural Source Separation Using Maximization–Minimization Algorithm with Time–Frequency Deconvolution †

Gao

Bouridane

et al. 2018

Sensors

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization–minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time–frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy.