8 research outputs found
Histogram Transform-based Speaker Identification
A novel text-independent speaker identification (SI) method is proposed. This
method uses the Mel-frequency Cepstral coefficients (MFCCs) and the dynamic
information among adjacent frames as feature sets to capture speaker's
characteristics. In order to utilize dynamic information, we design super-MFCCs
features by cascading three neighboring MFCCs frames together. The probability
density function (PDF) of these super-MFCCs features is estimated by the
recently proposed histogram transform~(HT) method, which generates more
training data by random transforms to realize the histogram PDF estimation and
recedes the commonly occurred discontinuity problem in multivariate histograms
computing. Compared to the conventional PDF estimation methods, such as
Gaussian mixture models, the HT model shows promising improvement in the SI
performance.Comment: Technical Repor
Deep Neural Network for Analysis of DNA Methylation Data
Many researches demonstrated that the DNA methylation, which occurs in the
context of a CpG, has strong correlation with diseases, including cancer. There
is a strong interest in analyzing the DNA methylation data to find how to
distinguish different subtypes of the tumor. However, the conventional
statistical methods are not suitable for analyzing the highly dimensional DNA
methylation data with bounded support. In order to explicitly capture the
properties of the data, we design a deep neural network, which composes of
several stacked binary restricted Boltzmann machines, to learn the low
dimensional deep features of the DNA methylation data. Experiments show these
features perform best in breast cancer DNA methylation data cluster analysis,
comparing with some state-of-the-art methods.Comment: Techinical Repor
Language Identification with Deep Bottleneck Features
In this paper we proposed an end-to-end short utterances speech language
identification(SLD) approach based on a Long Short Term Memory (LSTM) neural
network which is special suitable for SLD application in intelligent vehicles.
Features used for LSTM learning are generated by a transfer learning method.
Bottle-neck features of a deep neural network (DNN) which are trained for
mandarin acoustic-phonetic classification are used for LSTM training. In order
to improve the SLD accuracy of short utterances a phase vocoder based
time-scale modification(TSM) method is used to reduce and increase speech rated
of the test utterance. By splicing the normal, speech rate reduced and
increased utterances, we can extend length of test utterances so as to improved
improved the performance of the SLD system. The experimental results on
AP17-OLR database shows that the proposed methods can improve the performance
of SLD, especially on short utterance with 1s and 3s durations.Comment: Preliminary work repor
Impacts of Weather Conditions on District Heat System
Using artificial neural network for the prediction of heat demand has
attracted more and more attention. Weather conditions, such as ambient
temperature, wind speed and direct solar irradiance, have been identified as
key input parameters. In order to further improve the model accuracy, it is of
great importance to understand the influence of different parameters. Based on
an Elman neural network (ENN), this paper investigates the impact of direct
solar irradiance and wind speed on predicting the heat demand of a district
heating network. Results show that including wind speed can generally result in
a lower overall mean absolute percentage error (MAPE) (6.43%) than including
direct solar irradiance (6.47%); while including direct solar irradiance can
achieve a lower maximum absolute deviation (71.8%) than including wind speed
(81.53%). In addition, even though including both wind speed and direct solar
irradiance shows the best overall performance (MAPE=6.35%).Comment: Technical Repor
Statistical Speech Model Description with VMF Mixture Model
In this paper, we present the LSF parameters by a unit vector form, which has
directional characteristics. The underlying distribution of this unit vector
variable is modeled by a von Mises-Fisher mixture model (VMM). With the high
rate theory, the optimal inter-component bit allocation strategy is proposed
and the distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ).
Experimental results show that the VVQ outperforms our recently introduced DVQ
and the conventional GVQ.Comment: Technical Repor
Vehicular Edge Computing via Deep Reinforcement Learning
The smart vehicles construct Vehicle of Internet which can execute various
intelligent services. Although the computation capability of the vehicle is
limited, multi-type of edge computing nodes provide heterogeneous resources for
vehicular services.When offloading the complicated service to the vehicular
edge computing node, the decision should consider numerous factors.The
offloading decision work mostly formulate the decision to a resource scheduling
problem with single or multiple objective function and some constraints, and
explore customized heuristics algorithms. However, offloading multiple data
dependency tasks in a service is a difficult decision, as an optimal solution
must understand the resource requirement, the access network, the user
mobility, and importantly the data dependency. Inspired by recent advances in
machine learning, we propose a knowledge driven (KD) service offloading
decision framework for Vehicle of Internet, which provides the optimal policy
directly from the environment. We formulate the offloading decision of
multi-task in a service as a long-term planning problem, and explores the
recent deep reinforcement learning to obtain the optimal solution. It considers
the future data dependency of the following tasks when making decision for a
current task from the learned offloading knowledge. Moreover, the framework
supports the pre-training at the powerful edge computing node and continually
online learning when the vehicular service is executed, so that it can adapt
the environment changes and learns policy that are sensible in hindsight. The
simulation results show that KD service offloading decision converges quickly,
adapts to different conditions, and outperforms the greedy offloading decision
algorithm.Comment: Preliminary report of ongoing wor
Classification of EEG Signal based on non-Gaussian Neutral Vector
In the design of brain-computer interface systems, classification of
Electroencephalogram (EEG) signals is the essential part and a challenging
task. Recently, as the marginalized discrete wavelet transform (mDWT)
representations can reveal features related to the transient nature of the EEG
signals, the mDWT coefficients have been frequently used in EEG signal
classification. In our previous work, we have proposed a super-Dirichlet
distribution-based classifier, which utilized the nonnegative and sum-to-one
properties of the mDWT coefficients. The proposed classifier performed better
than the state-of-the-art support vector machine-based classifier. In this
paper, we further study the neutrality of the mDWT coefficients. Assuming the
mDWT vector coefficients to be a neutral vector, we transform them non-linearly
into a set of independent scalar coefficients. Feature selection strategy is
proposed on the transformed feature domain. Experimental results show that the
feature selection strategy helps improving the classification accuracy.Comment: Technical Repor
Infinite Mixture of Inverted Dirichlet Distributions
In this work, we develop a novel Bayesian estimation method for the Dirichlet
process (DP) mixture of the inverted Dirichlet distributions, which has been
shown to be very flexible for modeling vectors with positive elements. The
recently proposed extended variational inference (EVI) framework is adopted to
derive an analytically tractable solution. The convergency of the proposed
algorithm is theoretically guaranteed by introducing single lower bound
approximation to the original objective function in the VI framework. In
principle, the proposed model can be viewed as an infinite inverted Dirichelt
mixture model (InIDMM) that allows the automatic determination of the number of
mixture components from data. Therefore, the problem of pre-determining the
optimal number of mixing components has been overcome. Moreover, the problems
of over-fitting and under-fitting are avoided by the Bayesian estimation
approach. Comparing with several recently proposed DP-related methods, the good
performance and effectiveness of the proposed method have been demonstrated
with both synthesized data and real data evaluations.Comment: Technical Report of ongoing wor