Learning Stochastic Edit Distances from Structured Data: Application in Music Retrieval

author: José Oncina, University of Alicante

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*Slides*
0:00	Learning Stochastic Edit Distance from Structured Data: Application in Music Retrieval
0:01	Outline - 1
0:08	Outline - 2
0:14	Outline - 3
0:22	Outline - 4
0:27	Outline - 5
0:30	1. Tree Edit Distance
0:32	Tree Edit Distance - 1
0:47	Tree Edit Distance - 2
0:50	Tree Edit Distance - 3
1:12	Tree Edit Distance - 4
1:54	Tree Edit Distance - 5
2:45	The Edit Distance - 1
2:59	The Edit Distance - 2
3:21	The Edit Distance - 3
3:31	The Edit Distance - 4
3:54	The Edit Distance - 5
4:48	2. Stochastic Extension
4:55	The Stochatic Edit Distance - 1
5:56	The Stochatic Edit Distance - 2
6:13	The Stochatic Edit Distance - 3
7:00	Normalization - 1
7:42	Normalization - 2
8:00	Normalization - 3
8:46	Normalization - 4
10:17	Which edition weights should we use? - 1
10:38	Which edition weights should we use? - 2
10:46	Which edition weights should we use? - 3
11:01	Which edition weights should we use? - 4
11:30	Which edition weights should we use? - 5
11:35	EM Algorithm-based Approach - 1
11:57	EM Algorithm-based Approach - 2
12:12	EM Algorithm-based Approach - 3
12:36	3. Music Database
12:43	Application: Music Recognition - 1
13:16	Application: Music Recognition - 2
13:31	Application: Music Recognition - 3
14:22	Application: Music Recognition - 4
14:25	Application: Music Recognition - 5
14:35	Application: Music Recognition - 6
14:42	Application: Music Recognition - 7
14:59	Application: Music Recognition - Objective
15:19	Music Representation - 1
16:27	Music Representation - 2
17:59	4. Experiments
18:02	Experimental Setup
19:19	Results
19:37	5. Future work
19:41	Future work - 1
19:49	Future work - 2
20:10	Future work - 3
20:32	Future work - 4
20:39	Future work - 5
20:47	Future work - 6
21:11	Future work - 7
21:44	Future work - 8
21:51	Future work - 9
23:04	- Questions

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