Two-stage cascaded classification approach based on genetic fuzzy learning for speech/music discrimination

Ruiz-Reyes, N.; García-Galán, S.; Muñoz, Javier

doi:10.1016/j.engappai.2009.06.006

Cited by 9 publications

(1 citation statement)

References 26 publications

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“…By employing fuzzy logic as a nonlinear mapping F, it is possible to describe the degree to which an event belongs to one class or the other. In order to solve a fuzzy classification problem, it is necessary to fuzzify inputs (Fuzzification), determine all IF-THEN rules (rule base), process them (Interference engine) and provide result in a usable and understandable form (Defuzzification) [7] [8].…”

Section: Fuzzy Classifiermentioning

confidence: 99%

Seismic Signal Discrimination between Earthquakes and Quarry Blasts Using Fuzzy Logic Approach

Laasri

Akhouayri

Agliz

et al. 2012

Lecture Notes in Computer Science

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Seismic analysts identify earthquakes signals from those of explosions by visual inspection and calculating some characteristics of seismogram. Such work supposes a great deal of workload for seismic analysts. Therefore, an automatic classification tool reduces dramatically this arduous task, turns classification reliable, removes errors associated to tedious evaluations and changing of personnel. In this present paper we are interested in transforming the analysts' knowledge of classifying seismograms into an automated soft classification system based on fuzzy logic. This is primarily due to its capability of modelling human reasoning and decision-making, managing complexity and controlling computational cost. These capabilities are essential for manipulating high dimensionality and complexity of seismic signal. To conduct effective discrimination, relevant seismogram characteristics are extracted based on human experience. Using these characteristics, a fuzzy classifier is built and tested with real seismic data. The results are found to be encouraging.

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Section: Fuzzy Classifiermentioning

confidence: 99%