Estimation of window coefficients for dynamic feature extraction for HMM-based speech synthesis

Multilayer perceptron (MLP) type neural networks and dynamic feature extraction techniques, namely linear prediction coding (LPC) and LPC cepstrum, are used to classify leakage type and to predict leakage flowrate magnitude in an electrohydraulic cylinder drive. Both single-leakage and multiple-leakage type faults are considered. A novel feature is that only pressure transient responses are employed as information. In addition, the feature extraction technique used to detect faults can result in a large data dimensionality reduction. The performance of two MLP models, namely serial and parallel, are studied to reflect the importance of the way data are presented to the MLP.

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Parallel and distributed methods for image retrieval with dynamic feature extraction on cluster architectures

12th International Workshop on Database and Expert Systems Applications ◽

10.1109/dexa.2001.953049 ◽

2002 ◽

Cited By ~ 4

Author(s):

O. Kao

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Dynamic Feature

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A Feature Extraction Method of Hybrid Gram for Malicious Behavior Based on Machine Learning

Security and Communication Networks ◽

10.1155/2019/2674684 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Yuntao Zhao ◽

Bo Bo ◽

Yongxin Feng ◽

ChunYu Xu ◽

Bo Yu

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Dimensional Space ◽

Semantic Segmentation ◽

Dynamic Feature ◽

Feature Extraction Method ◽

Malicious Behavior ◽

Internet Users ◽

Performance Indexes ◽

N Gram

With explosive growth of malware, Internet users face enormous threats from Cyberspace, known as “fifth dimensional space.” Meanwhile, the continuous sophisticated metamorphism of malware such as polymorphism and obfuscation makes it more difficult to detect malicious behavior. In the paper, based on the dynamic feature analysis of malware, a novel feature extraction method of hybrid gram (H-gram) with cross entropy of continuous overlapping subsequences is proposed, which implements semantic segmentation of a sequence of API calls or instructions. The experimental results show the H-gram method can distinguish malicious behaviors and is more effective than the fixed-length n-gram in all four performance indexes of the classification algorithms such as ID3, Random Forest, AdboostM1, and Bagging.

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A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

The Canadian Journal of Chemical Engineering ◽

10.1002/cjce.23740 ◽

2020 ◽

Vol 98 (6) ◽

pp. 1280-1292 ◽

Cited By ~ 1

Author(s):

Xinrui Gao ◽

Fan Yang ◽

Enbo Feng

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Time Scale ◽

Dynamic Feature ◽

Diagnosis Method ◽

Process Fault Diagnosis

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The Feature Extraction Algorithm for the Production of Emotions in Text-to-Speech (TTS) System for an Indian Regional Language

Pervasive Computing for Business ◽

10.4018/978-1-60566-996-0.ch003 ◽

2010 ◽

pp. 17-30

Author(s):

Jagadish S Kallimani ◽

V. K Ananthashayana ◽

Debjani Goswami

Keyword(s):

Feature Extraction ◽

Language Processing ◽

Speech Synthesis ◽

Text To Speech ◽

Everyday Objects ◽

Regional Language ◽

Extraction Algorithm ◽

Processing Signal ◽

Emotional Aspects ◽

Text To Speech Synthesis

Text-to-speech synthesis is a complex combination of language processing, signal processing and computer science. Ubiquitous computing (ubicomp) is a post-desktop model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. Speech synthesis is the generation of synthesized speech from text. This chapter deals with the development of a Text to Speech (TTS) Synthesis system for an Indian regional language by considering Bengali as the language. This chapter highlights various methods which may be used for speech synthesis and also it provides an overview on the problems and difficulties in Bengali text to speech conversion. Variations in the prosody (speech parameters – volume, pitch, intonation, amplitude) of the speech yields the emotional aspects (anger, happy, normal), which are applied to our developed TTS system.

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