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

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.

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The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

Energies ◽

10.3390/en14134024 ◽

2021 ◽

Vol 14 (13) ◽

pp. 4024

Author(s):

Krzysztof Dmytrów ◽

Joanna Landmesser ◽

Beata Bieszk-Stolorz

Keyword(s):

Time Series ◽

Palm Oil ◽

Dynamic Time Warping ◽

Global Economy ◽

Price Change ◽

Commodity Prices ◽

The Other ◽

Time Warping ◽

Heating Oil ◽

Dynamic Time

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

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Continuous Motion Classification and Segmentation Based on Improved Dynamic Time Warping Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418500027 ◽

2017 ◽

Vol 32 (02) ◽

pp. 1850002 ◽

Cited By ~ 3

Author(s):

Mingqin Liu ◽

Xiaoguang Zhang ◽

Guiyun Xu

Keyword(s):

Action Recognition ◽

Dynamic Time Warping ◽

The Other ◽

Continuous Image ◽

Single Image ◽

Time Warping ◽

Continuous Action ◽

Template Sequence ◽

Dynamic Time

The continuous image sequence recognition is more difficult than the single image recognition because the classification of continuous image sequences and the image edge recognition must be very accurate. Hence, a method based on sequence alignment for action segmentation and classification is proposed to reconstruct a template sequence by estimating the mean action of a class category, which calculates the distance between a single image and a template sequence by sparse coding in Dynamic Time Warping. The proposed method, the methods of Kulkarni et al. [Continuous action recognition based on sequence alignment, Int. J. Comput. Vis. pp. 1–26.] and Hoai et al. [Joint segmentation and classification of human actions in video, IEEE Conf. Computer Vision and Pattern Recognition, 2008, pp. 108–119.] are compared on the recognition accuracy of the continuous recognition and isolated recognition, which clearly shows that the proposed method outperforms the other methods. When applied to continuous gesture classification, it not only can recognize the gesture categories more quickly and accurately, but is more realistic in solving continuous action recognition problems in a video than the other existing methods.

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Addressing Text-Dependent Speaker Verification Using Singing Speech

Applied Sciences ◽

10.3390/app9132636 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2636 ◽

Cited By ~ 2

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.

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

Machine Learning for Cyber Physical Systems - Technologien für die intelligente Automation ◽

10.1007/978-3-662-62746-4_6 ◽

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.

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