A New Approximate Matching Algorithm and its Application in Internet Music Search by Humming

2012 ◽  
Vol 433-440 ◽  
pp. 3662-3668
Author(s):  
Yun Feng Dong ◽  
Bei Qi
Keyword(s):  
Dynamic Time Warping ◽  
Large Scale ◽  
Time Warping ◽  
Similarity Matching ◽  
Matching Algorithm ◽  
Approximate Matching ◽  
Query By Humming ◽  
Dynamic Time

This paper has proposed a new approximate matching algorithm—similarity matching, and use the characteristics of algorithm to establish a system of internet music search by humming. The author compared the similarity matching algorithm and dynamic time warping (DTW) algorithm, which is most commonly used to query by humming, by the system of internet music search by humming. On the two standard of the query hit ratio and query speed, we got the result that similarity matching algorithm's comprehensive efficiency is superior, is one of QBH (query by humming) algorithm, which is applicable to the large-scale music library such as internet music search.

scholarly journals Time-weighted dynamic time warping analysis for mapping interannual cropping practices changes in large-scale agro-industrial farms in Brazilian Cerrado

2021 ◽  
Vol 3 ◽  
pp. 100021
Author(s):  
Michel E.D. Chaves ◽  
Marcelo de C. Alves ◽  
Thelma Sáfadi ◽  
Marcelo S. de Oliveira ◽  
Michelle C.A. Picoli ◽  
...  
Keyword(s):  
Dynamic Time Warping ◽  
Large Scale ◽  
Brazilian Cerrado ◽  
Time Warping ◽  
Cropping Practices ◽  
Dynamic Time

Study on Rapid Archival Technology of Bullets Based on Graph Convolutional Neural Network

2021 ◽  
Author(s):  
Shi-bo Pan ◽  
Di-lin Pan ◽  
Nan Pan ◽  
Xiao Ye ◽  
Miaohan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Dynamic Time Warping ◽  
Large Scale ◽  
Line Graph ◽  
High Accuracy ◽  
Time Warping ◽  
Large Numbers ◽  
Dynamic Time

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

scholarly journals Use of Dynamic Time Warping for Object Shape Classification through Signature

1970 ◽  
Vol 6 (1) ◽  
pp. 33-49 ◽  
Author(s):  
KC Santosh
Keyword(s):  
Template Matching ◽  
Dynamic Time Warping ◽  
Center Of Gravity ◽  
Shape Classification ◽  
Time Warping ◽  
Similarity Matching ◽  
Object Shape ◽  
Boundary Points ◽  
Engineering And Technology ◽  
Dynamic Time

This paper expresses an application of similarity matching of the signatures through DTW.Fundamental aspect of classification is template matching. The classification is robust tonoise, scaling, and rotation. Feature includes radius plus angle along the boundary points withrespect to center of gravity. The classification automatically and confidently discloses theshape of every object at once throughout page from top to bottom. The paper expresses itspromising results within an average of a few seconds (cheaper classification) for an object. Aseries of tests is done with all possible configurations of geometrical shapes.Keywords: Signature; Dynamic Time Warping; Uniform ScalingDOI: 10.3126/kuset.v6i1.3308Kathmandu University Journal of Science, Engineering and Technology Vol.6(1) 2010, pp33-49

Follow That Tune-Adaptive Approach to DTW-based Query-by-Humming System

2015 ◽  
Vol 39 (4) ◽  
pp. 467-476 ◽  
Author(s):  
Bartłomiej Stasiak
Keyword(s):  
Dynamic Time Warping ◽  
Experimental Validation ◽  
Recognition Rate ◽  
Multimedia Databases ◽  
Time Warping ◽  
Query By Humming ◽  
Human Ability ◽  
Alignment Problem ◽  
Computationally Expensive ◽  
Dynamic Time

Abstract Dynamic Time Warping is a standard algorithm used for matching time series irrespective of local tempo variations. Its application in the context of Query-by-Humming interface to multimedia databases requires providing the transposition independence, which involves some additional, sometimes computationally expensive processing and may not guarantee the success, e.g., in the presence of a pitch trend or accidental key changes. The method of tune following, proposed in this paper, enables solving the pitch alignment problem in an adaptive way inspired by the human ability of ignoring typical errors occurring in sung melodies. The experimental validation performed on the database containing 4431 queries and over 5000 templates confirmed the enhancement introduced by the proposed algorithm in terms of the global recognition rate.

scholarly journals Energy Disaggregation Using Elastic Matching Algorithms

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 71 ◽  
Author(s):  
Pascal A. Schirmer ◽  
Iosif Mporas ◽  
Michael Paraskevas
Keyword(s):  
Energy Consumption ◽  
Template Matching ◽  
Dynamic Time Warping ◽  
Minimum Variance ◽  
Time Warping ◽  
Matching Algorithm ◽  
Energy Disaggregation ◽  
Model Training ◽  
Dynamic Time ◽  
Incoming Energy

In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.

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