By Deniz Erdogmus, Umut Ozertem, Tian Lan (auth.), Bhanu Prasad, S. R. Mahadeva Prasanna (eds.)
Humans are striking in processing speech, audio, snapshot and a few biomedical signs. man made neural networks are proved to achieve success in appearing a number of cognitive, commercial and clinical projects. This peer reviewed ebook offers a few fresh advances and surveys at the functions of synthetic neural networks within the components of speech, audio, photograph and biomedical sign processing. It involves 18 chapters ready via a few reputed researchers and practitioners round the globe.
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People are amazing in processing speech, audio, picture and a few biomedical signs. man made neural networks are proved to achieve success in appearing a number of cognitive, commercial and medical initiatives. This peer reviewed e-book provides a few contemporary advances and surveys at the functions of man-made neural networks within the components of speech, audio, picture and biomedical sign processing.
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Additional resources for Speech, Audio, Image and Biomedical Signal Processing using Neural Networks
The ANN meant to achieve such task is ﬁrst adapted to the classiﬁcation task through a learning process using training data. Training is equivalent to ﬁnding the proper weight for all the connections in an ANN such that a desired output is generated for a corresponding input . Several neural network models have been developed and used for speech recognition. They include: (1) the Kohonen’s Self-organising Map (SOM) model, (2) the Hopeﬁeld Model, and (3) the multilayer perceptron . In the next subsection, we review the literature on the application of ANN to tone language speech recognition in general and tone recognition in particular.
Learned-Miller E G, Fisher J W (2003) ICA Using Spacings Estimates of Entropy, Journal of Machine Learning Research 4:1271–1295 31. Vasicek O, (1976) A Test for Normality Based on Sample Entropy, Journal of the Royal Statistical Society B 38:54–59 32. Hero A O, Ma B, Michel O J J, Gorman J (2002) Applications of Entropic Spanning Graphs, IEEE Signal Processing Magazine 19:85–95 33. Beirlant J, Dudewicz E J, Gyorﬁ L, Van Der Meulen E C (1997) Nonparametric Entropy Estimation: An Overview, International Journal of Mathematical and Statistical Sciences 6:17–39 34.
8, which creates an explicit memory of one time lag . By delaying the output, the network has access to both prior and following context for recognising each tone pattern. An added advantage of including the delay is that the recognition capability of the network is enhanced. This is because the number of hidden layers between any input features and its corresponding output tones is increased. The delay, however, has the disadvantage of making the training process slower requiring more iteration steps than is required in the standard MLP training.
Speech, Audio, Image and Biomedical Signal Processing using Neural Networks by Deniz Erdogmus, Umut Ozertem, Tian Lan (auth.), Bhanu Prasad, S. R. Mahadeva Prasanna (eds.)