On the principles of fuzzy classification

Amo, Ana del; Montero, Javier; Cutello, Vincenzo

doi:10.1109/nafips.1999.781779

Cited by 14 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has been discussed, for example, by Zimmermann (1997), Del Amo et al (1999, and Meier et al (2008). A fuzzy classification is achieved by a membership function, : ⟶ [0,1], that indicates the degree to which an individual is a member of a fuzzy class, , given the corresponding fuzzy propositional function, .…”

Section: ∶= ( ∈ )mentioning

confidence: 99%

“…In data analysis, or "the search for structure in data" (Zimmermann H. J., 1997), fuzzy classification is a method for gradation in data consolidation, as presented by Meier, Schindler, andWerro (2008) andDel Amo, Montero, andCutello (1999). The application of fuzzy classification to marketing analytics (Spais & Veloutsou, 2005) has the advantage of precisiation (sic; Zadeh, 2008) of fuzzy concepts in the context of decision support for direct customer contact, as proposed by Werro (2008).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Inductive Fuzzy Classification in Marketing Analytics

Kaufmann¹

2014

Fuzzy Management Methods

View full text Add to dashboard Cite

Section: ∶= ( ∈ )mentioning

confidence: 99%

mentioning

confidence: 99%

Inductive Fuzzy Classification in Marketing Analytics

Kaufmann¹

2014

Fuzzy Management Methods

View full text Add to dashboard Cite

“…Therefore, each spectral bin should be allowed to belong to all of the classes simultaneously, with a certain degree of membership for each class. This kind of approach is known as fuzzy classification [30,31]. To this end, in [32], a continuous measure denoted as tonalness was proposed.…”

Section: Introductionmentioning

confidence: 99%

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Damskägg

Välimäki

2017

Applied Sciences

View full text Add to dashboard Cite

A novel method for audio time stretching has been developed. In time stretching, the audio signal's duration is expanded, whereas its frequency content remains unchanged. The proposed time stretching method employs the new concept of fuzzy classification of time-frequency points, or bins, in the spectrogram of the signal. Each time-frequency bin is assigned, using a continuous membership function, to three signal classes: tonalness, noisiness, and transientness. The method does not require the signal to be explicitly decomposed into different components, but instead, the computing of phase propagation, which is required for time stretching, is handled differently in each time-frequency point according to the fuzzy membership values. The new method is compared with three previous time-stretching methods by means of a listening test. The test results show that the proposed method yields slightly better sound quality for large stretching factors as compared to a state-of-the-art algorithm, and practically the same quality as a commercial algorithm. The sound quality of all tested methods is dependent on the audio signal type. According to this study, the proposed method performs well on music signals consisting of mixed tonal, noisy, and transient components, such as singing, techno music, and a jazz recording containing vocals. It performs less well on music containing only noisy and transient sounds, such as a drum solo. The proposed method is applicable to the high-quality time stretching of a wide variety of music signals.

show abstract