scholarly journals Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis

2020 ◽  
pp. 53-61
Author(s):  
Paolo Graniero ◽  
Marco Gärtler
Keyword(s):  
Survival Analysis ◽  
Survival Data ◽  
Dynamic Time Warping ◽  
Computational Cost ◽  
Predictive Performance ◽  
Time Frame ◽  
Batch Processes ◽  
Time Warping ◽  
Dynamic Time ◽  
The One

AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant.

scholarly journals Addressing Text-Dependent Speaker Verification Using Singing Speech

2019 ◽  
Vol 9 (13) ◽  
pp. 2636 ◽  
Author(s):  
Yan Shi ◽  
Juanjuan Zhou ◽  
Yanhua Long ◽  
Yijie Li ◽  
Hongwei Mao
Keyword(s):  
Dynamic Time Warping ◽  
State Of The Art ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Time Warping ◽  
Normal Reading ◽  
Feature Spaces ◽  
Dynamic Time ◽  
The One ◽  
Normal Speech

The automatic speaker verification (ASV) has achieved significant progress in recent years. However, it is still very challenging to generalize the ASV technologies to new, unknown and spoofing conditions. Most previous studies focused on extracting the speaker information from natural speech. This paper attempts to address the speaker verification from another perspective. The speaker identity information was exploited from singing speech. We first designed and released a new corpus for speaker verification based on singing and normal reading speech. Then, the speaker discrimination was compared and analyzed between natural and singing speech in different feature spaces. Furthermore, the conventional Gaussian mixture model, the dynamic time warping and the state-of-the-art deep neural network were investigated. They were used to build text-dependent ASV systems with different training-test conditions. Experimental results show that the voiceprint information in the singing speech was more distinguishable than the one in the normal speech. More than relative 20% reduction of equal error rate was obtained on both the gender-dependent and independent 1 s-1 s evaluation tasks.

A computational analysis on similarities and dissimilarities of Acem Kurdi, Kurdi and Muhayyer Kurdi makams in Turkish music

2020 ◽  
Vol 8 (4) ◽  
pp. 761-773
Author(s):  
Naciye Hardalaç ◽  
Keyword(s):  
Dynamic Time Warping ◽  
Computational Analysis ◽  
Pattern Search ◽  
The Other ◽  
Time Dependent ◽  
Time Warping ◽  
Dynamic Time ◽  
The One ◽  
Distinguishing Features ◽  
Time Dependent Analysis

In Turkish music, it is possible to find different makams sharing the same core scale of notes. The subjects of this study are three such makams, namely Acem Küdri, Kürdi, Muhayyer Kürdi. We use computational analysis based on histograms, pattern search and dynamic time warping to reveal the similarities and dissimilarities of these three makams. On the one hand, our results show that a time independent histogram analysis is unable to properly highlight the differences between different makams. On the other hand, our study also reveals that a time dependent analysis is well suited for the identification of their distinguishing features. In particular, the application of a specialized dynamic time warping technique leads to the establishment of low correlation between these makams.

A Novel Weighted Dynamic Time Warping for Light Weight Speaker-Dependent Speech Recognition in Noisy and Bad Recording Conditions

2014 ◽  
Vol 490-491 ◽  
pp. 1347-1355
Author(s):  
Xiang Lilan Zhang ◽  
Ji Ping Sun ◽  
Xu Hui Huang ◽  
Zhi Gang Luo
Keyword(s):  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Time Frame ◽  
Training Data ◽  
Relative Reduction ◽  
Personal Privacy ◽  
Time Warping ◽  
Speech Data ◽  
Recording Conditions ◽  
Dynamic Time

Lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a promising solution for the problems of possibility of disclosing personal privacy and difficulty of obtaining training material for many seldom used English words and (often non-English) names. Dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small foot-print SD ASR applications, which have limited storage space and small vocabulary. In our previous work, we have successfully developed two fast and accurate DTW variations for clean speech data. However, speech recognition in adverse conditions is still a big challenge. In order to improve recognition accuracy in noisy and bad recording conditions, such as too high or low recording volume, we introduce a novel weighted DTW method. This method defines a feature index for each time frame of training data, and then applies it to the core DTW process to tune the final alignment score. With extensive experiments on one representative SD dataset of three speakers' recordings, our method achieves better accuracy than DTW, where 0.5% relative reduction of error rate (RRER) on clean speech data and 7.5% RRER on noisy and bad recording speech data. To the best of our knowledge, our new weighted DTW is the first weighted DTW method specially designed for speech data in noisy and bad recording conditions.

scholarly journals An empirical evaluation of kernels for time series

2021 ◽  
Author(s):  
Mourtadha Badiane ◽  
Pádraig Cunningham
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Dynamic Time Warping ◽  
Computational Cost ◽  
Empirical Evaluation ◽  
Distance Measures ◽  
Support Vector ◽  
Surprising Result ◽  
Time Warping ◽  
Dynamic Time

AbstractThere exist a variety of distance measures which operate on time series kernels. The objective of this article is to compare those distance measures in a support vector machine setting. A support vector machine is a state-of-the-art classifier for static (non-time series) datasets and usually outperforms k-Nearest Neighbour, however it is often noted that that 1-NN DTW is a robust baseline for time-series classification. Through a collection of experiments we determine that the most effective distance measure is Dynamic Time Warping and the most effective classifier is kNN. However, a surprising result is that the pairing of kNN and DTW is not the most effective model. Instead we have discovered via experimentation that Dynamic Time Warping paired with the Gaussian Support Vector Machine is the most accurate time series classifier. Finally, with good reason we recommend a slightly inferior (in terms of accuracy) model Time Warp Edit Distance paired with the Gaussian Support Vector Machine as it has a better theoretical basis. We also discuss the reduction in computational cost achieved by using a Support Vector Machine, finding that the Negative Kernel paired with the Dynamic Time Warping distance produces the greatest reduction in computational cost.

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