Time-frequency spectral estimation of speech

In recent years there has been a growing interest amongst the speech research community into the use of spectral estimators which circumvent the traditional quasi-stationary assumption and provide greater time-frequency (t-f) resolution than conventional spectral estimators, such as the short time Fourier power spectrum (STFPS). One distribution in particular, the Wigner distribution (WD), has attracted considerable interest. However, experimental studies have indicated that, despite its improved t-f resolution, employing the WD as the front end of speech recognition system actually reduces recognition performance; only by explicitly re-introducing t-f smoothing into the WD are recognition rates improved. In this paper we provide an explanation for these findings. By treating the spectral estimation problem as one of optimization of a bias variance trade off, we show why additional t-f smoothing improves recognition rates, despite reducing the t-f resolution of the spectral estimator. A practical adaptive smoothing algorithm is presented, whicy attempts to match the degree of smoothing introduced into the WD with the time varying quasi-stationary regions within the speech waveform. The recognition performance of the resulting adaptively smoothed estimator is found to be comparable to that of conventional filterbank estimators, yet the average temporal sampling rate of the resulting spectral vectors is reduced by around a factor of 10. © 1992.

Frequency-based interpolation of sampled signals with applications in audio restoration

A new interpolation technique has been developed for replacing missing samples in a sampled waveform drawn from a stationary stochastic process, given the power spectrum for the process. The method works with a finite block of data and is based on the assumption that components of the block DFT are Gaussian zero-mean independent random variables with variance proportional to the power spectrum at each frequency value. These assumptions make the interpolator particularly suitable for signals with a sharply-defined harmonic structure, such as audio waveforms recorded from music or voiced speech. Some results are presented and comparisons are made with existing techniques.

Directly and parametrically excited bi-stable vibration energy harvester for broadband operation

Despite many recent advances, the wide-spread adoption of vibrational energy harvesting has been limited by the low levels of generated output power and confined operational frequency band. Recent work by the authors on parametrically excited harvesters has demonstrated over an order of magnitude power improvement. This paper presents an investigation into the simultaneous employment of both direct and parametric resonance, as well as the incorporation of bi-stability, in an attempt to further improve the mechanical-to-electrical energy conversion efficiency by broadening the output power spectrum. Multiple direct and parametric resonant peaks from a multi-degree-of-freedom system were observed and an accumulative ∼10 Hz half-power bandwidth was recorded for the first 40 Hz. Real vibration data was also employed to analysis the rms power response effectiveness of the proposed system. © 2013 IEEE.

Raman Spectrum of silicon nanowires

We measure the effects of phonon confinement on the Raman spectra of silicon nanowires (SiNWs). We show how previous reports of phonon confinement in SiNWs and nanostructures are actually inconsistent with phonon confinement, but are due to the intense local heating caused by the laser power used for Raman measurements. This is peculiar to nanostructures, and would require orders of magnitude higher power in bulk Si. By varying the temperature, power and excitation energy, we identify the contributions of pure confinement, heating and carrier photo-excitation. After eliminating laser-related effects, the Raman spectra show confinement signatures typical of quantum wires. © 2003 Elsevier B.V. All rights reserved.