Enhancements to DTW and VQ decision algorithms for speaker recognition

The reciprocating compressor has widely been used in the industry because of low cost to mass production and high efficiency of performance. If an abnormal compressor is produced, high service cost occurs due to the increased service claim rate (SCR). A great deal of research has been conducted for real-time diagnosis of the compressor while it is being made in the manufacturing line. However, since the compressor structure is quite complicated, it is still difficult to find the cause of the compressor fault. Even with outstanding diagnostic tools, it is hard to screen an abnormal compressor at one time. We have developed a multi-stage diagnosis scheme to monitor the abnormal compressor. In this diagnosis scheme, the dynamic time warping (DTW) technique is employed to diagnose the fault compressor. The LAN based network is applied for connecting from first diagnosis PC to second diagnosis PC. The multi-stage diagnosis manufacturing line is established and the DTW technique has been successfully implemented in the diagnostic system. The multi-stage DTW system screens most of the abnormal compressor. The gage R&R is conducted to secure the reliability of the system.

Large scale optical character recognition (OCR) refers to or means the computerization of large amounts of documents such as news papers. Despite the diversity of commercial OCR products, this task still remains too far from the mature especially if the input documents are insufficient quality or cursive writing such as the Arabic documents (Vinciarelli, 2002). Indeed, in their project (Holley, 2009), the national library of Australia reports that existing OCR systems are, commonly, weak. Moreover, their conducted experiments on historical newspapers show that the corresponding accuracy raw varied from 71% to 98.02%. This is surely due to the weakness of the approaches and techniques used in these systems. Printed cursive written documents such as the Arabic one presents, in addition, other difficulties which are behind the weaknesses of the existing commercialized systems especially when the quality of the input binary image of the document is not good enough. The first difficulty encountered for such writing is the segmentation of any given input word or sub-word into isolated characters given that the size of each ofwhich is variable. In practice, if the segmentation process is conducted successfully, then it eases the recognition step to a large extent. That is why Latin printedOCR systems are, commonly, more powerful compared to those devoted to the cursive writing documents. Dynamic Time Warp (DTW) algorithm is a well known procedure especially in pattern recognition (Alves et al., 2002; Khemakhem et al., 2005; Philip, 1992; Vuori et al., 2001), (Khemakhem et al., 2009; Kumar et al., 2006; Tapia et al., 2007). The DTW algorithm is the result of the adaptation of dynamic programming to the field of pattern recognition. Printed cursive writing OCR by the DTW algorithm provides very interesting recognition rates without prior character segmentation (such as: the Arabic, Persian, Urdu, latin connected characters,...), (Khemakhem et al., 2005). The purpose of the DTW algorithm is to perform optimal time alignment between a reference pattern and an unknown pattern and evaluate their difference. Intensive experiments show that the recognition rate of the DTW algorithm remains acceptable compared to the existing commercialized systems even when the quality of the input documents is not good enough. Intensive tests on more than 100.000 connected characters (most of them are Arabic cursive and including some important noise) show that the segmentation average rate is greater than 98% and the recognition average rate is 5

The authors present an algorithm for isolated-word recognition that takes into consideration the duration variability of the different utterances of the same word. The algorithm is based on extracting acoustical features from the speech signal and using them as the input to a sequence of multilayer perceptron neural networks. The networks were implemented as predictors for the speech samples for a certain duration of time. The networks were trained by a combination of the back-propagation and the dynamic time warping (DTW) techniques. The DTW technique was implemented to normalize the duration variability. The networks were trained to recognize the correct words and to reject the wrong words. The training set consisted of ten words, each uttered seven times by three different speakers. The test set consisted of three utterances of each of the ten words. The results show that all these words could be recognized.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A time series analysis method for spacecraft telemetry data is presented in this paper. For spacecraft testing and on-orbit flight, this method can monitor the changes of telemetry data automatically and identify the failure modes of spacecraft. Using dynamic time warping (DTW) algorithm, combining historical data samples as well as fault cases with this method analyzes the similarity of telemetry data transformed into time series. By comparing the results of analysis with the results of DTW distance calculation, the relative deviation of data is measured and the abnormal data in fault mode is identified. The results show that the telemetry data analysis method based on DTW algorithm can effectively detect data anomalies and realize fault identification, which has a certain application prospect.

DTW algorithm compares the parameters of an unknown spoken word with the parameters of one or more reference templates. The more reference templates are used for the same word, the higher is the recognition rate. But increasing the number of reference templates for the same word to recognize, leads to an increase in memory resources and computing time. The proposed algorithm is used in the learning phase and combines the advantages of DTW and Vector Quantization (VQ); instead of storing multiple reference templates, it stores only one reference model for each word and that reference is based on classes (like in the vector quantization method), each class is represented by a centroid (or codeword). In the recognition phase, the parameters of the unknown utterance are compared to the centroids of the reference model. This solution increases the speed of calculation in the recognition phase and reduces the quantity of used memory.

Partial matching and search space reduction for QbE-STD

A comprehensive understanding of temporal variability of subsurface soil moisture (SM) is paramount in hydrological and agricultural applications such as rainfed farming and irrigation. Since the SMOS (Soil Moisture and Ocean Salinity) mission was launched in 2009, globally available satellite SM retrievals have been used to investigate SM dynamics, based on the fact that useful information about subsurface SM is contained in their time series. SM along the depth profile is influenced by atmospheric forcing and local SM properties. Until now, subsurface SM was estimated by weighting preceding information of remotely sensed surface SM time series according to an optimized depth-specific characteristic time length. However, especially in regions with extreme SM conditions, the response time is supposed to be seasonally variable and depends on related processes occurring at different timescales. Aim of this study was to quantify the response time by means of the time lag between the trend series of satellite and in-situ SM observations using a Dynamic Time Warping (DTW) technique. DTW was applied to the SMOS satellite SM L4 product at 1 km resolution developed by the Barcelona Expert Center (BEC), and in-situ near-surface and root-zone SM of four representative stations at multiple depths, located in the Soil Moisture Measurements Station Network of the University of Salamanca (REMEDHUS) in Western Spain. DTW was customized to control the rate of accumulation and reduction of time lag during wetting and drying conditions and to consider the onset dates of pronounced precipitation events to increase sensitivity to prominent features of the input series. The temporal variability of climate factors in combination with crop growing seasons were used to indicate prevailing SM-related processes. Hereby, a comparison of long-term precipitation recordings and estimations of potential evapotranspiration (PET) allowed us to estimate SM seasons. The spatial heterogeneity of land use was analyzed by means of high-resolution images of Normalized Difference Vegetation Index (NDVI) from Sentinel-2 to provide information about the level of spatial representativeness of SMOS observations to each in-situ station. Results of the spatio-temporal analysis of the study were then evaluated to understand seasonally and spatially changing patterns in time lag. The time lag evolution describes a variable characteristic time length by considering the relevant processes which link SMOS and in-situ SM observation, which is an important step to accurately infer subsurface SM from satellite time series. At a further stage, the approach needs to be applied to different SM networks to understand the seasonal, climate- and site-specific characteristic behaviour of time lag and to decide, whether general conclusions can be drawn.

This paper describes the implementation of a speaker identification system with reference to Assamese language. The database consists of speech samples that were collected from 15 (fifteen) speakers for ten Assamese words representing the Assamese digits from 0 (shounyo) to 9 (no). Mel Frequency Cepstral Coefficients (MFCC) are used as features for this study. Two independent speaker identification systems have been built in this paper using Vector Quantization (VQ) and I-vector technique. The system built using the I-vector technique obtains comparatively better identification accuracy for speakers when compared with the system developed using VQ technique. Three different systems have been built for both the techniques based on variable feature size. A maximum accuracy of 92.38% is achieved using I-vector technique with 39 MFCC features.

Insects play significant role in the human life. And insects pollinate major food crops consumed in the world. Insect pests consume and destroy major crops in the world. Hence to have control over the disease and pests, researches are going on in the area of entomology using chemical, biological and mechanical approaches. The data relevant to the flying insects often changes over time, and classification of such data is a central issue. And such time series mining tasks along with classification is critical nowadays. Most time series data mining algorithms use similarity search and hence time taken for similarity search is the bottleneck and it does not produce accurate results and also produces very poor performance. In this paper, a novel classification method that is based on the dynamic time warping (DTW) algorithm is proposed. The dynamic time warping algorithm is deterministic and lacks in modeling stochastic signals. The dynamic time warping (DTW) algorithm is improved by implementing a nonlinear median filtering (NMF). Recognition accuracy of conventional DTW algorithms is less than that of the hidden Markov model (HMM) by same voice activity detection (VAD) and noise-reduction. With running spectrum filtering (RSF) and dynamic range adjustment (DRA). NMF seek the median distance of every reference of time series data and the recognition accuracy is much improved. In this research work, optical sensors are used to record the sound of insect flight, with invariance to interference from ambient sounds. The implementation of our tool includes two parts, an optical sensor to record the "sound" of insect flight, and a software that leverages on the sensor information, to automatically detect and identify flying insects.

The author reviews major methods of applying the vector quantization (VQ) technique to speech and speaker recognition. These include speech recognition based on the combination of VQ and the DTW/HMM (dynamic time warping/hidden Markov model) technique. VQ-distortion-based recognition, learning VQ algorithms, speaker adaptation by VQ-codebook mapping, and VQ-distortion-based speaker recognition. It is concluded that not only has the VQ technique reduced the amount of computation and storage, but it has also created new ideas for solving various problems in speech/speaker recognition. >

Psychopathological disorders are increasingly conceptualized as complex dynamic systems, which can be represented as networks of interconnected symptoms. These dynamic networks are often constructed using Multilevel Vector AutoRegression (mlVAR) models. However, psychological processes frequently violate these assumptions. An alternative approach for examining temporal relationships between variables is Dynamic Time Warping (DTW). This paper evaluates the potential applications, advantages, and disadvantages of DTW and mlVAR. As part of the Netherlands Study of Depression and Anxiety, an Ecological Momentary Assessment module was administered five times daily for 2 weeks, to 376 participants (Mean age 49.3 years, 64.4% women). We created item networks based on 20 of the mood and physical condition items from this module using the mlVAR and DTW techniques, and repeated these analyses using simulated data to explore violations of mlVAR assumptions, including various lagged relationships and the presence of collider variables. Analysis of simulated datasets revealed that mlVAR networks were more susceptible to spurious connections, while DTW produces more reliable networks under these conditions. While mlVAR better reveals causal relationships when assumptions are met, DTW provides a robust method for examining co-occurrence, synchrony, and the directionality of lagged connections in real-world psychological data.

Croplands are commonly mapped using time series of remotely sensed images. The dynamic time warping (DTW) algorithm is an effective method for realizing this. However, DTW algorithm faces the challenge of capturing complete and accurate representative cropland time series on a national scale, especially in Asian countries where climatic and topographic conditions, cropland types, and crop growth patterns vary significantly. This study proposes an automatic cropland extraction method based on the DTW algorithm and density-based spatial clustering of applications with noise (DBSCAN), hereinafter referred to as ACE-DTW, to map croplands in Pakistan in 2015. First, 422 frames of multispectral Landsat-8 satellite images were selected from the Google Earth Engine to construct monthly normalized difference vegetation index (NDVI) time series. Next, a total of 2409 training samples of six land cover types were generated randomly and explained visually using high-resolution remotely sensed images. Then, a multi-layer DBSCAN was used to classify NDVI time series of training samples into different categories automatically based on their pairwise DTW distances, and the mean NDVI time series of each category was used as the standard time series to represent the characteristics of that category. These standard time series attempted to represent cropland information and maximally distinguished croplands from other possible interference land cover types. Finally, image pixels were classified as cropland or non-cropland based on their DTW distances to the standard time series of the six land cover types. The overall cropland extraction accuracy of ACE-DTW was 89.7%, which exceeded those of other supervised classifiers (classification and regression trees: 78.2%; support vector machines: 78.8%) and existing global cropland datasets (Finer Resolution Observation and Monitoring of Global Land Cover: 87.1%; Global Food Security Support Analysis Data: 83.1%). Further, ACE-DTW could produce relatively complete time series of variable cropland types, and thereby provide a significant advantage in mountain regions with small, fragmented croplands and plain regions with large, high-density patches of croplands.

Fuel cell (FC) is a promising solution in order to tackle global warming problems. Though, efforts are needed for the development of reliable tools to monitor the FC state of health and to extract useful information in order to detect possible malfunctioning. The present paper propose an original method based on the Dynamic Time Warping (DTW) technique in order to process and analyze electrochemical impedance spectroscopy data. The proposed method extracts information on the similarities between 2 EIS curves. This method is evaluated on data from start-up and shut-down experimental campaign on a high temperature PEM-FC stack. Several hundreds of EIS curves are processed over 5 different conditions. The proposed method reaches 92% of correct unsupervised classifications. From the different classes identified, the ohmic resistance is extracted in order to study the impact of 2 different start-up and shut-down protocols on the FC performance.

The introduction of Gaussian mixture models (GMMs) in the field of speaker verification has led to very good results. This paper illustrates an evolution in state-of-the-art speaker verification by highlighting the contribution of recently established information theoretic based vector quantization technique. We explore the novel application of three different vector quantization algorithms, namely K-means, Linde-Buzo-Gray (LBG) and information theoretic vector quantization (ITVQ) for efficient speaker verification. The expectation maximization (EM) algorithm used by GMM requires a prohibitive amount of iterations to converge. In this paper, comparable alternatives to EM including K-means, LBG and ITVQ algorithm were tested. The GMM-ITVQ algorithm was found to be the most efficient alternative for the GMM-EM. It gives correct classification rates at a similar level to that of GMM-EM. Finally, representative performance benchmarks and system behaviour experiments on NIST SRE corpora are presented.

The Dynamic Time Warping (DTW) algorithm is widely used in finding the global alignment of time series. Many time series data mining and analytical problems can be solved by the DTW algorithm. However, using the DTW algorithm to find similar subsequences is computationally expensive or unable to perform accurate analysis. Hence, in the literature, the parallelisation technique is used to speed up the DTW algorithm. However, due to the nature of DTW algorithm, parallelizing this algorithm remains an open challenge. In this paper, we first propose a novel method that finds the similar local subsequence. Our algorithm first searches for the possible start positions of subsequence, and then finds the best-matching alignment from these positions. Moreover, we parallelize the proposed algorithm on GPUs using CUDA and further propose an optimization technique to improve the performance of our parallelization implementation on GPU. We conducted the extensive experiments to evaluate the proposed method. Experimental results demonstrate that the proposed algorithm is able to discover time series subsequences efficiently and that the proposed GPU-based parallelization technique can further speedup the processing.