Nonstationary signal extraction based on BatOMP sparse decomposition technique

Sparse decomposition technique is a new method for nonstationary signal extraction in a noise background. To solve the problem of accuracy and efficiency exclusive in sparse decomposition, the bat algorithm combined with Orthogonal Matching Pursuits (BatOMP) was proposed to improve sparse decomposition, which can realize adaptive recognition and extraction of nonstationary signal containing random noise. Two general atoms were designed for typical signals, and dictionary training method based on correlation detection and Hilbert transform was developed. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with several relevant methods, it was indicated that BatOMP can improve convergence speed and extraction accuracy efficiently as well as decrease the hardware requirement, which is cost effective and helps broadening the applications.

Nonstationary Signal Extraction Based on Bat Algorithm Sparse Decomposition Technique

For nonstationary time series i.e. natural electromagnetic field and acoustical signal, effective signal extraction always requires prior knowledge or hypothesis, and hardly do without artificial judgment. We proposed bat algorithm sparse decomposition (BASD) to realize adaptive recognition and extraction of nonstationary signal in a noisy background. We designed two general atomics for typical signals, and developed dictionary training method based on correlation detection and Hilbert transform. The sparse decomposition was turned into an optimizing problem by introducing bat algorithm with optimized fitness function. By contrast with variational modal decomposition, it was indicated that BASD can effectively extract short time target without inducing global aliasing of local feature, and no preset mode number and late screening were needed.

Applying adaptive recognition of the learner’s vowel space to English pronunciation training of native speakers of Japanese

When native speakers of Japanese are taught English as a second language, there are difficulties with their training in pronunciation of American English vowels that can be ameliorated though adaptive recognition of the learner’s vowel space. This paper reports on the development of an online Computer-Assisted Language Learning (CALL) environment that provides Japanese learners with customized target utterances of 12 single-syllable words that are synthesized according to an adaptive recognition of the learner’s vowel space. These customized target utterances provide each learner with examples of each of 12 American English monophthongs in consonant-vowel-consonant (CVC) context in order to sound as if they had been uttered by the learners themselves. This adaptive process was incorporated into a successfully developed tool for Computer-Assisted Pronunciation Training (CAPT) which gave more appropriate pronunciation targets to each learner, rather than forcing the learners to attempt to match the formant frequencies of their own utterances to those of the target utterances as produced by a speaker exhibiting a different vowel space (i.e., a speaker with a different vocal tract length).