A novel method based on cross correlation maximization, for pattern matching by means of a single parameter. Application to the human voice
| Autor: | Quiero, Felipe, Quintana, Fabian, Bennun, Leonardo |
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| Rok vydání: | 2015 |
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| Druh dokumentu: | Working Paper |
| Popis: | This work develops a cross correlation maximization technique, based on statistical concepts, for pattern matching purposes in time series. The technique analytically quantifies the extent of similitude between a known signal within a group of data, by means of a single parameter. Specifically, the method was applied to voice recognition problem, by selecting samples from a given individual recordings of the 5 vowels, in Spanish. The frequency of acquisition of the data was 11.250 Hz. A certain distinctive interval was established from each vowel time series as a representative test function and it was compared both to itself and to the rest of the vowels by means of an algorithm, for a subsequent graphic illustration of the results. We conclude that for a minimum distinctive length, the method meets resemblance between every vowel with itself, and also an irrefutable difference with the rest of the vowels for an estimate length of 30 points (~2 10-3 s). Comment: 13 pages, 11 figures |
| Databáze: | arXiv |
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