Titlebook: Speech, Audio, Image and Biomedical Signal Processing using Neural Networks; Bhanu Prasad,S. R. Mahadeva Prasanna Book 2008 Springer-Verla

Endearing

Audio Signal Processing, audio signals are discussed followed by a description of time-frequency representations for audio. Features useful for classification are reviewed. In addition, a discussion on prominent examples of audio classification systems with particular emphasis on feature extraction is provided.

Fibrinogen

Book 2008rming several cognitive, industrial and scientific tasks. This peer reviewed book presents some recent advances and surveys on the applications of artificial neural networks in the areas of speech, audio, image and biomedical signal processing. It consists of 18 chapters prepared by some reputed researchers and practitioners around the globe. .

控制

Information Theoretic Feature Selection and Projection,optimal exploitation of the information content in the feature vector. Dimensionality reduction eliminates redundant dimensions that do not convey reliable statistical information for classification, determines a manifold on which projections of the original high- dimensional feature vector exhibit

separate

CHANT

委派

Modeling Supra-Segmental Features of Syllables Using Neural Networks, These supra-segmental features are also termed as prosodic features, and hence the corresponding models are known as prosody models. Neural networks are used to capture the implicit duration and intonation knowledge in the sequence of syllables of an utterance. A four layer feedforward neural netwo

transplantation

钩针织物

A Novel Approach to Language Identification Using Modified Polynomial Networks, The novelty of the proposed method consists of building up language models for each language using the normalized mean of the training feature vectors for all the speakers in particular language class with discriminatively trained polynomial network having based on Mean-Square Error (MSE) learning

心胸狭窄

Speech/Non-Speech Classification in Hearing Aids Driven by Tailored Neural Networks, in hearing aids. These classes have been preliminary selected aiming at focusing on speech intelligibility and user‘s comfort. Hearing aids in the market have important constraints in terms of computational complexity and battery life, and thus a set of trade-offs have to be considered. Tailoring t

surrogate