By Patrick C. K. Hung (eds.)
This ebook offers varied use circumstances in huge information purposes and similar functional stories. Many companies at the present time are more and more attracted to using great info applied sciences for helping their enterprise intelligence in order that it really is changing into progressively more vital to appreciate many of the useful matters from assorted functional use instances. This e-book offers transparent facts that giant info applied sciences are enjoying an ever expanding vital and important function in a brand new cross-discipline examine among desktop technological know-how and enterprise.
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Extra info for Big Data Applications and Use Cases
J. Saunders, “Real-time discrimination of broadcast speech/music,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), pp. 993–996, May 1996 4. E. Scheirer, M. Slaney, Construction and evaluation of a robust multi-feature speech/music discriminator, in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP), pp. 1331–1334, Apr 1997 Automatic Speech and Singing Discrimination for Audio Data Indexing 47 5. G. Williams and D. Ellis, “Speech/music discrimination based on posterior probability features,” in Proc.
31] developed an answer summary system to replace existing lists of similar queries. Zhang et al.  had suggested a mobile multimedia functionality that could be used in CQA websites, and the authors had claimed that supported by identifying mobile screenshots, matching these instances and retrieving candidate answers, the question asking process could be effective facilitated. In terms of the non-functionalities of CQA websites, the characteristic of communities of CQA websites had attracted intense research attentions.
In the testing phase, the system evaluates the timbre and pitch features of an unknown sound recording by matching them with the parametric models generated in the training phase. The resulting likelihoods are then combined to form a basis for decision. Specifically, the unknown sound recording is decided as either speech or singing according to Eq. (2) À Á À Á Pr XΛSpeech Pr YλSpeech αlog À Singing Á þ ð1 À αÞlog À Singing Á Pr XΛ Pr Yλ Speech > 0; ð2Þ Singing where Pr(X|ΛSpeech) and Pr(X|ΛSinging) are the likelihoods of the extracted timbre-based feature X matching speech and singing models ΛSpeech and ΛSinging; Pr(Y|λSpeech) and Pr(Y|λSinging) are the likelihoods of the extracted pitch-based feature Y matching speech and singing models λSpeech and λSinging; and α is a tunable weight.
Big Data Applications and Use Cases by Patrick C. K. Hung (eds.)