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By Patrick C. K. Hung (eds.)
This booklet provides diversified use instances in colossal info purposes and comparable useful reviews. Many companies this present day are more and more attracted to using monstrous facts applied sciences for helping their company intelligence in order that it really is turning into progressively more vital to appreciate many of the sensible matters from various useful use circumstances. This ebook presents transparent evidence that enormous facts applied sciences are enjoying an ever expanding vital and demanding function in a brand new cross-discipline examine among machine technology and enterprise.
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Additional resources for Big Data Applications and Use Cases
The recording conditions were the same as those in DB-1-Singing. The resulting 300 recordings were denoted by DB-2-Singing. -H. -H. Ma 10 participants was asked to read the lyrics of what they have sung at a normal speed. The resulting 300 recordings were denoted as DB-2-Speech. The recordings in DB-2-Singing and DB-2-Speech were used to evaluate the performance of our speech/singing discrimination system. 1 Experiment Results Evaluation of the Speech-Recognition-Based Discrimination System Our experiments were first conducted to examine the performance of the intuitive speech/singing discrimination system described in Sect.
To characterize the timbre differences between speech and singing voices, we propose building a Gaussian mixture classifier by its success in many pattern recognition problems. The classifier operates in two phases: training and testing. During the training phase, two Gaussian mixture models (GMMs), ΛSpeech and ΛSinging, are created using the available speech data and singing data, respectively. It is known that GMMs provide good approximations of arbitrarily shaped densities of a spectrum over a long span of time , and hence can reflect the collective vocal tract configurations in speech or singing voices.
3 %. 6 % shown in Table 4a, it is clear that the proposed speech/singing discrimination system is superior to the intuitive speech-recognition-based discriminating system. Table 4 lists the accuracies of discriminating each individual subject’s singing and speech recordings using the proposed system. 7 performs poorly in discriminating subject 2’s voice, compared to the cases of other subjects. -H. -H. Ma recordings as speech. The major reason is that subject 2 is not good at singing, which does not know how to modify their voices properly to make the singing melodious.