A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.

Student Engagement in the School: Interpersonal and Inter-Center Differences

Traducido al castellano e inglés

Intensive Program ERASMUS: TOPCART. Geometric Documentation of the Heritage (administrative and academic documentation)

[EN] This academic activity has been the origin of other work that are also located in this repository. The first one is the dataset of information about the geometry of the Monastery recorded during the two years of fieldwork, then some bachelor thesis and papers are listed:

Enhancing the Digital Student Experience postcards

The original ‘Enhancing the digital student experience’ cards have been updated to incorporate the findings from the HE Digital Student consultation and also from the emerging findings from the FE Digital Student project. These ‘Digital Student’ cards are designed to support conversations about students’ digital experience

Student research into the use of technology in higher and further education

Jisc conducted a study into the use of technology in higher and further education to mark the launch of the Jisc Digital Festival.

Summer of Student Innovation 2015

Marketing materials

HE in FE projects: summary report

This report provides a summary overview of the projects and findings from nine projects that explored the use of technology-enhanced learning delivered in Higher Education (HE) courses delivered by Further Education (FE) colleges.