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Highlights from Dynamic Time Warping

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Dynamic Time Warping

Timothy Felty (view profile)

08 Dec 2004 (Updated )

Dynamic time warping program. Calculates the similarity between 2 vectors.

File Information
Description

If you pass in 2 vectors it returns the unnormalized distance between the vectors, the accumulated distance between them, the length of the warping path (the normalizing factor), and the warping path points.

To compare 2 vectors A and B call:
[Dist,D,k,w]=dtw(A,B)
Dist is the unnormalized distance
D is the accumulated distance
k is the length of the warping path
(the normalizing factor)
w is a matrix containing the points along the warping path

Acknowledgements

This file inspired Constrained Dynamic Time Warping Distance Measure and Continuous Dynamic Time Warping.

MATLAB release MATLAB 6.1 (R12.1)
11 Dec 2015 Megamind

Megamind (view profile)

Hi Grace Pan
Could you explain what your issue was?

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26 Nov 2015 Grace Pan

Grace Pan (view profile)

I have fixed my issue!

26 Nov 2015 Grace Pan

Grace Pan (view profile)

Hi,Timothy Felty
Could i ask you a question? The code works perfectly when two input data have the same length. But if two input data have different length, such as [10 12 14 18 14 12 10 10 12 14 18 14 12 10] and [NaN 12 14 18 14 12 10 10 12 14 18 NaN NaN NaN], the output is NaN. Could you think how to deal with it?

04 Aug 2015 Shuangjun

Shuangjun (view profile)

Good frame work, however ignoring the continuous condition.

04 Aug 2015 Shuangjun

Shuangjun (view profile)

Good frame work, however ignoring the continuous condition.

23 Jan 2015 Edgar dominguez

Edgar dominguez (view profile)

in this code, how do i know if the two vectors are a good match or not..

13 Dec 2014 arezoo

arezoo (view profile)

I ran this function for some series of data but all the time, the answer is the same... I mean for different data I get a fix D and Dist and etc.
am I wrong? or is there a problem with the function?

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13 Dec 2014 arezoo

arezoo (view profile)

anybody pleas answer and help me. I'm in a hurry

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28 May 2014 Ansuman Mahapatra

02 May 2014 Qing

15 Apr 2014 Han

Han (view profile)

26 May 2013 sai sahiti

sai sahiti (view profile)

12 Jan 2013 Andrea Rollo

Andrea Rollo (view profile)

Hi everyone!!
I'm using this software, and I think that it's really useful.
But I've a question: how to use the normalization factor?
Thanks a lot.

15 Sep 2012 Tanmoy

Tanmoy (view profile)

13 Jan 2012 Akhil Sudhakar

Akhil Sudhakar (view profile)

please send complete code for melodic contour matching....

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24 Nov 2011 Vicky

10 Aug 2011 He

He (view profile)

01 Aug 2011 Mohammed

Mohammed (view profile)

At least answer the previous questions!

14 Nov 2010 Lars

Lars (view profile)

Maybe add code to ensure proper vectors, e.g. t=t(:)'; and r=r(:)'; in this case ;)

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17 Sep 2010 abdur

abdur (view profile)

im trying to compare two vectors of audio samples but the matrix dimensions dont match n d code gives error. plz assist

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13 Aug 2010 Nandha

Nandha (view profile)

Can I know how can I implement lower bounding (LB_keogh) and global constraint to this algorithm ?

Also, how to use obtain normalized distance using the normalizing factor ?

30 Mar 2010 Mano Samuel

Mano Samuel (view profile)

Hi Timothy,
could you let me know which article or book you used as a reference to code this ? . Thank you.
Mano

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13 Apr 2009 Wen

Wen (view profile)

should not be, d(n,m)=sqrt(sum((t(:,n)-r(:,m)).^2)); instead of d(n,m)=(t(n)-r(m))^2;
t, r are sequences of rows-dim vectors

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07 Apr 2009 Tony Mei

Tony Mei (view profile)

Can anyone explain how to use the normalizing factor? Thanks a lot.

14 Oct 2008 Zhuo Feibao
25 Mar 2008 Ahmed Bderhman

I think this is a good working and had help me more.

I have this question: i like to change euclidean distance{d=(repmat(Test',1,N)-repmat(Ref,M,1)).^2;} with mahalanobis distance is it possipole? if yes how can i do that?

because i like to test the efficiency of my system when i use euclidean distance and when i used mahalnobis distance.

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26 Feb 2008 Sai Man

Great code thanks for writing. But, I noticed a small discrepancy with my C code. In the .m file line 45:
min([D(n-1,m),D(n,m-1),D(n-1,m-1)]);

it would be much better to have the D(n-1,m-1) element in the first argument like this:
min([D(n-1,m-1),D(n,m-1),D(n-1,m)]);
and also swap the cases on the following lines.

This is because we are trying to find the minimum path length, and in the case where D(n-1,m-1)=D(n,m-1) or D(n-1,m-1)=D(n-1,m) then it would make sense to preference the diagonal element over the offdiagonal.

28 Jan 2008 twinss ahmed
07 Jun 2007 Pau Micó

I think it should be better to replace the sentence: w=cat(1,w,[n,m]); by: w=[n m; w];
In this way we get the w indexes in ascending order and it is easier to get t_warped and r_warped as: t_warped=t(w(:,1)); and r_warped=t(w(:,2));. In this way we can directly compare the warped sequences with the original ones without any index inversion.

20 Feb 2007 weera kompreyaray
24 Jan 2007 Sven Mensing

Good function
Another 10-fold Speed-up can be achived if you use a double-MIN construction for the Distance matrix

D(n,m)=d(n,m)+min(D(n-1,m),min(D(n-1,m-1),D(n,m-1)));

08 Jan 2007 Boat MiMi

...

18 Dec 2006 Yuzeng Lv
16 Oct 2006 reza rezaww
08 Sep 2006 Rogelio Ramos
20 Feb 2006 Natan K

Can you tell me about speaker verification using mfcc and dtw. Please let me know.

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04 May 2005 jamal habibi
29 Apr 2005 Ivo Ihrke

The loop

for m=2:m
D(1,m)=d(1,m)+D(1,m-1);
end

contains an error ( for statement ), should be:

for m=2:M
D(1,m)=d(1,m)+D(1,m-1);
end

If you want to compare vectors, where the vectors themselves are unknown, but their distance matrix is known, you can use your distance matrix instead of "d"

06 Apr 2005 Georg Schmitz

The routine can be made 10 times faster by doing the d(n,m) calculation by

d=(repmat(t(:),1,M)-repmat(r(:)',N,1)).^2;