Small Spectral Distortion Research Articles

Parallel voice conversion with limited training data using stochastic variational deep kernel learning

There are two types of voice conversion methods: statistical and deep learning-based. Although statistical methods can train with limited data, they face challenges, including spectral oversmoothing and time-domain discontinuity. On the other hand, extensively researched deep learning-based methods rely primarily on massive amounts of data, which limits their practical applicability. Given that voice conversion is an engineering problem with limited training data, it is crucial to develop techniques that can produce satisfactory results in terms of quality and similarity in the absence of a large amount of data.This paper proposes a voice conversion model based on stochastic variational deep kernel learning (SVDKL), which works with limited training data. The model allows the use of both the deep neural network’s expressive capability and the high flexibility of the Gaussian process, which is a Bayesian and non-parametric method. The model utilizes a cascade of a deep neural network and a conventional kernel as the covariance function, which enables it to estimate non-smooth and more complex functions. Furthermore, the model’s sparse variational Gaussian process solves the scalability problem of exact inference and enables the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which takes into account both data fitting and model complexity. Considering model complexity reduces the training data by increasing the robustness to overfitting. To evaluate the proposed scheme, we examined the model’s performance with as little as approximately 80 s of training data. The results indicated that our method obtains a higher mean opinion score, smaller spectral distortion, and better preference tests than the state-of-the-art limited data methods.

Design of a compact wide-spectrum double-channel prism imaging spectrometer with freeform surface.

A double-channel prism imaging spectrometer that includes visible-near-infrared (VNIR) and shortwave-infrared (SWIR) spectra is presented. Double slits divide a wide spectral band into two independent channels for VNIR and SWIR, guaranteeing their independent spectral resolution. A beam splitter splits the band into two, one detected by the SWIR and the other by the VNIR detector. The freeform surface, as a mirror, compensates for astigmatism across a wide spectral band. An imaging spectrometer with 30° field of view and a measured spectrum from 400nm to 2500nm is designed. This compact, cost-effective spectrometer with high performance and small spectral distortion has a huge potential for broadband space exploration.

On the radiative and thermodynamic properties of the cosmic radiations using COBE FIRAS instrument data: IV. Sunyaev-Zel’dovich ( μ $\mu$ -type) distortion effect

The Sunyaev-Zel’dovich (SZ) effect represents a small spectral distortion to the cosmic microwave background (CMB) radiation, caused by the Compton scattering of CMB photons by the hot gas of galaxy clusters. In an early stage of universe, the SZ effect generates $\mu$ -type of distortions for the CMB spectrum. A $\mu$ -type distortion is created between the double Compton scattering decoupling ( $z \sim 10^{6}$ ) and the thermalization decoupling by the Compton scattering ( $z \sim 10^{5}$ ). In this case, to describe the small spectral distortion of the CMB spectrum, we use the Bose-Einstein ( $\mu$ -type) distribution with a non-zero chemical potential. At present, it is interesting to investigate the effect of this spectral distortion on the integral characteristics of the Bose-Einstein ( $\mu$ -type) spectrum. The thermal radiative and thermodynamic functions are such integral characteristics. These functions are as follows: a) the total radiation power per unit area; b) total energy density; c) number density of photons; d) grand potential density; e) Helmholtz free energy density; f) entropy density; g) heat capacity at constant volume; h) enthalpy density; and i) pressure. Precise analytical expressions are obtained for the temperature dependences of these functions. Using the observational data obtained by the COBE FIRAS, PIXIE, PRISM, and Planck missions, the thermal radiative and thermodynamic functions are calculated. A comparative analysis of the results obtained with the results for the same functions of the CMB spectrum at $T = 2.72548~\mbox{K}$ is carried out. Very small distortions are observed for the thermal radiative and thermodynamic functions. In the redshift range $10^{5} < z < 3 \times10^{6}$ , these functions are calculated. The expressions are obtained for new astrophysical parameters, such as the entropy density/Boltzmann constant and number density, created by the Bose-Einstein ( $\mu$ -type) spectrum.

Comparative Study between the Discrete-Frequency Kalman Filtering and the Discrete-Time Kalman Filtering with Application in Noise Reduction in Speech Signals

This article aims to carry out a comparative study between discrete-time and discrete-frequency Kalman filters. In order to assess the performance of both methods for speech reconstruction, we measured the output segmental signal-to-noise ratio and the Itakura-Saito distance provided by each algorithm over 25 different voice signals. The results show that although the two algorithms performed very similarly regarding noise reduction, the discrete-time Kalman filter produced smaller spectral distortion on the estimated signals when compared with the discrete-frequency Kalman filter.

Analytical design and implementation of an imaging spectrometer

We present the design and practical implementation of a visible/near-infrared imaging spectrometer. The design of the spectrometer is based on an Offner relay. The approach presented allows an effective design for the Offner type imaging spectrometer to achieve a large flat field and to have minimum astigmatism, which is the dominant residual aberration over the full interested wavelength range. The system works at the wavelength range from 400 to 1000 nm with a high spectral resolution of 0.6 nm/pixel, a relatively low f-number of f/2.3, and a large flat field of 12 mm. The performance measurement of the achieved prototype shows that only a low smile distortion (<0.1 pixel) and a small spectral keystone distortion (<0.07 pixel) are present. The instrument has the advantages of a compact configuration and a light weight. We present some actual images at different working wavelengths.

Cross-Calibration for Data Fusion of EO-1/Hyperion and Terra/ASTER

The data fusion of low spatial-resolution hyperspectral and high spatial-resolution multispectral images enables the production of high spatial-resolution hyperspectral data with small spectral distortion. EO-1/Hyperion is the world's first hyperspectral sensor. It was launched in 2001 and has a similar orbit to Terra/ASTER. In this work, we apply hyperspectral and multispectral data fusion to EO-1/Hyperion and Terra/ASTER datasets by the preprocessing of datasets and the onboard cross-calibration of sensor characteristics. The relationship of the spectral response function is determined by convex optimization by comparing hyperspectral and multispectral images over the same spectral range. After accurate image registration, the relationship of the point spread function is obtained by estimating a matrix that acts as Gaussian blur filter between two images. Two pansharpening-based methods and one unmixing-based method are adopted for hyperspectral and multispectral data fusion and their properties are investigated.

The evolution of CMB spectral distortions in the early Universe

The energy spectrum of the cosmic microwave background (CMB) allows constraining episodes of energy release in the early Universe. In this paper we revisit and refine the computations of the cosmological thermalization problem. For this purpose a new code, called CosmoTherm, was developed that allows solving the coupled photon-electron Boltzmann equation in the expanding, isotropic Universe for small spectral distortion in the CMB. We explicitly compute the shape of the spectral distortions caused by energy release due to (i) annihilating dark matter; (ii) decaying relict particles; (iii) dissipation of acoustic waves; and (iv) quasi-instantaneous heating. We also demonstrate that (v) the continuous interaction of CMB photons with adiabatically cooling non-relativistic electrons and baryons causes a negative mu-type CMB spectral distortion of DI_nu/I_nu ~ 10^{-8} in the GHz spectral band. We solve the thermalization problem including improved approximations for the double Compton and Bremsstrahlung emissivities, as well as the latest treatment of the cosmological recombination process. At redshifts z <~ 10^3 the matter starts to cool significantly below the temperature of the CMB so that at very low frequencies free-free absorption alters the shape of primordial distortions significantly. In addition, the cooling electrons down-scatter CMB photons introducing a small late negative y-type distortion at high frequencies. We also discuss our results in the light of the recently proposed CMB experiment Pixie, for which CosmoTherm should allow detailed forecasting. Our current computations show that for energy injection because of (ii) and (iv) Pixie should allow to improve existing limits, while the CMB distortions caused by the other processes seem to remain unobservable with the currently proposed sensitivities and spectral bands of Pixie.

A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method