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complete report, add video
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ppwwyyxx committed Jan 3, 2014
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4 changes: 2 additions & 2 deletions doc/Final-Report-Complete/implementation.tex
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%File: implementation.tex
%Date: Fri Jan 03 21:09:50 2014 +0800
%Date: Fri Jan 03 21:51:56 2014 +0800
%Author: Yuxin Wu <ppwwyyxxc@gmail.com>

\section{Implementation}
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they suffer a common problem of inefficency.
For the consideration of speed, a C++ version of GMM with K-MeansII initialization and
concurrency support
was implemented and located in \verb|src/gmm/|. It requires \verb|g++ >= 4.7| to compile.
was implemented and located in \verb|src/gmm/|. It requires \verb|g++>=4.7| to compile.
This implementation of GMM also provides a python binding which have similar interface to the GMM in
scikit-learn.

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8 changes: 7 additions & 1 deletion doc/Final-Report-Complete/intro.tex
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%File: intro.tex
%Date: Fri Jan 03 17:03:58 2014 +0800
%Date: Fri Jan 03 22:40:59 2014 +0800
%Author: Yuxin Wu <ppwwyyxxc@gmail.com>


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In this task, we have built a proof-of-concept text-independent speaker recognition system with
GUI support. It is fast, accurate based on our tests on large corpus.
And the gui program only require very short utterance to quickly respond.
The whole system is fully described in this report.
This project is developed at Git9\footnote{Git hosting service of the department of CST, Tsinghua Univ., currently maintained by Yuxin Wu. See
\url{http://git.net9.org}},
and is also hosted on github\footnote{See \url{https://github.com/ppwwyyxx/speaker-recognition}}.
The repository contains the source code, all documents, experiment log, as well as a video demo.
The complete pack of this project also contains all the intermediate data, models, recordings, and 3rd party libraries.
4 changes: 2 additions & 2 deletions doc/Final-Report-Complete/report.tex
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%
% $File: report.tex
% $Date: Fri Jan 03 21:54:52 2014 +0800
% $Date: Fri Jan 03 21:58:13 2014 +0800
%

\documentclass{article}
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\newcommand{\cmd}[1]{{\it #1}}
\newcommand{\ccmd}[1]{\centerize{\cmd{#1}}}

\title{Digital Signal Processing: Speaker Recognition \\ Final Report \\ Complete Version}
\title{Digital Signal Processing: Speaker Recognition \\ Final Report \\ (Complete Version)}
\author{Xinyu Zhou, Yuxin Wu, and Tiezheng Li\\ Tsinghua University}
\date{}

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6 changes: 3 additions & 3 deletions log/final/main.py
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#!/usr/bin/python2
# -*- coding: utf-8 -*-
# $File: main.py
# $Date: Tue Dec 31 03:39:04 2013 +0800
# $Date: Fri Jan 03 22:01:46 2014 +0800
# $Author: Xinyu Zhou <zxytim[at]gmail[dot]com>

from dataextractor import DataExtractor as DE
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y_min = min(y_min, min(map(lambda p: p[0] - p[1], zip(y, yerr))))
# plt.errorbar(x, y, yerr, lw=2, label=ser['label'])
plt.plot(x, y, lw=2, label=ser['label'])
plt.xlabel(cfg['xlabel'], fontsize=25)
plt.ylabel(cfg['ylabel'], fontsize=20)
plt.xlabel(cfg['xlabel'], fontsize=13)
plt.ylabel(cfg['ylabel'], fontsize=13)
plt.yticks(fontsize=15)
if 'xlim' in cfg:
plt.xlim(cfg['xlim'])
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