The AFSSI-C is a spectral imager that generates spectral classification directly, in fewer measurements than are required by traditional systems that measure the spectral datacube (which is later interpreted to make material classification). By utilizing adaptive features to constantly update conditional probabilities for the different hypotheses, the AFSSI-C avoids the overhead of directly mea...

This paper presents a method to quantify the spectral characteristics of reduction in speech. Hämäläinen et al. (2009) proposes a measure of spectral reduction which is able to predict a substantial amount of the variation in duration that linguistically motivated variables do not account for. In this paper, we continue studying acoustic reduction in speech by developing a new acoustic measure ...

where eg,H is the spectral measure associated with g and H , i.e., the finite positive measure on [−1, 1] such that 〈g,H g〉μ = ∫ 1 −1 x eg,H(dx) for all i ∈ N. For nonnegative definite transition 15 kernels H the spectral measure, and the integrals, are over the narrower range [0, 1]. Denote by H2,α the transition kernel of the pseudo-marginal algorithm with proposal kernel q, target marginal d...

Alterations in speech motor control in depressed individuals have been found to manifest as a reduction in spectral variability. In this paper we present a novel method for measuring acoustic volume a model-based measure that is reflective of this decrease in spectral variability and assess the ability of features resulting from this measure for indexing a speaker’s level of depression. A Monte...

We present a new classification scheme, dubbed spectral classification, which uses the spectral characteristics of the image blocks to classify them into one of a finite number of classes. A vector quantizer with an appropriate distortion measure is designed to perform the classification operation. The application of the proposed spectral classification scheme is then demonstrated in the contex...

Analysis of the seismic risk to a structure requires assessment of both the rate of occurrence of future earthquake ground motions hazard and the effect of these ground motions on the structure response . These two pieces are often linked using an intensity measure such as spectral acceleration. However, earth scientists typically use the geometric mean of the spectral accelerations of the two ...

Under noisy conditions, due to the redundancy of speech signal, there are some spectral bands (Reliable Bands) whose local SNR’s are high enough to be used effectively by a recognizer. A novel, phonetically motivated Reliable Bands Guided similarity measure (RBG measure) is proposed in this study. It has the following features. Firstly, for reference spectrum, frequency bands which have larger ...

We present a general adaptive approach for discrete spectral modeling by minimizing different spectral distances in an adaptive filtering context. By comparing the steady-state error for several spectral distance measures for real speech, we study the performance of these important distance measures. We also present a fast converging algorithm for the COSH distance that is shown to yield a more...

Spectral clustering is a method of subspace clustering which is suitable for the data of any shape and converges to global optimal solution. By combining concepts of shared nearest neighbors and geodesic distance with spectral clustering, a self-adaptive spectral clustering based on geodesic distance and shared nearest neighbors was proposed. Experiments show that the improved spectral clusteri...

We propose new audio features that can be extracted from bass lines. Most previous studies on content-based music information retrieval (MIR) used low-level features such as the mel-frequency cepstral coefficients and spectral centroid. Musical similarity based on these features works well to some extent but has a limit to capture fine musical characteristics. Because bass lines play important ...