4,728 research outputs found
A parallel Heap-Cell Method for Eikonal equations
Numerous applications of Eikonal equations prompted the development of many
efficient numerical algorithms. The Heap-Cell Method (HCM) is a recent serial
two-scale technique that has been shown to have advantages over other serial
state-of-the-art solvers for a wide range of problems. This paper presents a
parallelization of HCM for a shared memory architecture. The numerical
experiments in show that the parallel HCM exhibits good algorithmic
behavior and scales well, resulting in a very fast and practical solver.
We further explore the influence on performance and scaling of data
precision, early termination criteria, and the hardware architecture. A shorter
version of this manuscript (omitting these more detailed tests) has been
submitted to SIAM Journal on Scientific Computing in 2012.Comment: (a minor update to address the reviewers' comments) 31 pages; 15
figures; this is an expanded version of a paper accepted by SIAM Journal on
Scientific Computin
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust
automatic speech recognition (ASR) front-end that operates on an ensemble of
the subband components of high-dimensional acoustic waveforms. The key issues
of selecting the appropriate SVM kernels for classification in frequency
subbands and the combination of individual subband classifiers using ensemble
methods are addressed. The proposed front-end is compared with state-of-the-art
ASR front-ends in terms of robustness to additive noise and linear filtering.
Experiments performed on the TIMIT phoneme classification task demonstrate the
benefits of the proposed subband based SVM front-end: it outperforms the
standard cepstral front-end in the presence of noise and linear filtering for
signal-to-noise ratio (SNR) below 12-dB. A combination of the proposed
front-end with a conventional front-end such as MFCC yields further
improvements over the individual front ends across the full range of noise
levels
Utilizing Domain Knowledge in End-to-End Audio Processing
End-to-end neural network based approaches to audio modelling are generally
outperformed by models trained on high-level data representations. In this
paper we present preliminary work that shows the feasibility of training the
first layers of a deep convolutional neural network (CNN) model to learn the
commonly-used log-scaled mel-spectrogram transformation. Secondly, we
demonstrate that upon initializing the first layers of an end-to-end CNN
classifier with the learned transformation, convergence and performance on the
ESC-50 environmental sound classification dataset are similar to a CNN-based
model trained on the highly pre-processed log-scaled mel-spectrogram features.Comment: Accepted at the ML4Audio workshop at the NIPS 201
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