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Dynamic frequency warping, the dual of dynamic time warping
The Journal of the Acoustical Society of America, 1987Comparison of two tokens of the same utterance is central to many automatic speech recognition systems. Matching is usually done in the frequency-time domain; token matching is effectively spectrogram matching. Dynamic time warping (DTW) overcomes, to some extent, the temporal variability of speech tokens; spectrograms are time-aligned by calculating ...
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Constrained Sparse Dynamic Time Warping
2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs.
Youngha Hwang, Saul B. Gelfand
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Dynamic time warping improves sewer flow monitoring
Water Research, 2013Successful management and control of wastewater and storm water systems requires accurate sewer flow measurements. Unfortunately, the harsh sewer environment and insufficient flow meter calibration often lead to inaccurate and biased data. In this paper, we improve sewer flow monitoring by creating redundant information on sewer velocity from natural ...
Dürrenmatt David J. +2 more
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Alignment Using Variable Penalty Dynamic Time Warping
Analytical Chemistry, 2009In this article we highlight a novel variation on dynamic time warping (DTW) for aligning chromatogram signals. We are interested in sets of signals that can be aligned well locally, but not globally, by shifting individual signals in time. This kind of alignment is often sufficient for aligning gas chromatography data.
Clifford, David +7 more
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Segmented Dynamic Time Warping
2019Initially used in speech recognition, the dynamic time warping algorithm (DTW) has regained popularity with the widespread use of time series data. While demonstrating good performance, this elastic measure has two significant drawbacks: high computational costs and the possibility of pathological warping paths.
Ruizhe Ma +3 more
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Quaternion Dynamic Time Warping
IEEE Transactions on Signal Processing, 2012Dynamic time warping (DTW) is used for the comparison and processing of nonlinear signals and constitutes a widely researched field of study. The method has been initially designed for, and applied to, signals representing audio data. Afterwords it has been successfully modified and applied to many other fields of study.
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2017
Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs.
Youngha Hwang, Saul B. Gelfand
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Dynamic time warping (DTW) has been applied to a wide range of machine learning problems involving the comparison of time series. An important feature of such time series is that they can sometimes be sparse in the sense that the data takes zero value at many epochs.
Youngha Hwang, Saul B. Gelfand
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2009
This chapter contains sections titled: Introduction Dynamic Programming Dynamic Time Warping Applied to IWR DTW Applied to CSR Training Issues in DTW Algorithms Conclusions Problems ]]>
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This chapter contains sections titled: Introduction Dynamic Programming Dynamic Time Warping Applied to IWR DTW Applied to CSR Training Issues in DTW Algorithms Conclusions Problems ]]>
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Feature Based Dynamic Time Warping
2009Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns.
Ying Xie, Li Fangping
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SSDTW: Shape segment dynamic time warping
Expert Systems with Applications, 2020Abstract In order to increase the yield of a process, it is essential to establish a process control based on manufacturing data. Process management systems mainly consist of statistical process control (SPC), fault detection and classification (FDC), and advanced process control (APC), and are modeled using time series data.
Jae Yeol Hong +2 more
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