Formant estimation of speech signals using subspace-based spectral analysis

The objective of this paper is to propose a signal processing scheme that employs subspace-based spectral analysis for the purpose of formant estimation of speech signals. Specifically, the scheme is based on decimative spectral estimation that uses Eigenanalysis and SVD (Singular Value Decomposition). The underlying model assumes a decomposition of the processed signal into complex damped sinusoids. In the case of formant tracking, the algorithm is applied on a small amount of the autocorrelation coefficients of a speech frame. The proposed scheme is evaluated on both artificial and real speech utterances from the TIMIT database. For the first case, comparative results to standard methods are provided which indicate that the proposed methodology successfully estimates formant trajectories.

Noncatalytic chemical vapor deposition of graphene on high-temperature substrates for transparent electrodes

A noncatalytic chemical vapor deposition mechanism is proposed, where high precursor concentration, long deposition time, high temperature, and flat substrate are needed to grow large-area nanocrystalline graphene using hydrocarbon pyrolysis. The graphene is scalable, uniform, and with controlled thickness. It can be deposited on virtually any nonmetallic substrate that withstands ∼1000 °C. For typical examples, graphene grown directly on quartz and sapphire shows transmittance and conductivity similar to exfoliated or metal-catalyzed graphene, as evidenced by transmission spectroscopy and transport measurements. Raman spectroscopy confirms the sp 2-C structure. The model and results demonstrate a promising transfer-free technique for transparent electrode production. © 2012 American Institute of Physics.