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A preliminary study on extracting speaker related features from audio files using linear predictive coding.

Home Page: https://t3000.henryschuler.de

License: MIT License

TeX 45.96% Python 45.16% Jupyter Notebook 8.88%
feature-extraction linear-predictive-coding speaker

analysis-of-audio-signals-using-linear-predictive-coding-t3000's Introduction

๐Ÿ™‹๐Ÿปโ€โ™‚๏ธ About me

Hi, I am Henry Schuler ๐Ÿ‘‹

I am a master student at the University Ulm since 2023.

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User authentication using voice recognition

A student research project concerning the topic of user authentication using voice recognition.


Python Tensorflow LaTeX

Analysis of audio signals using linear predictive coding (LPC)

A preliminary study for the student research project on extracting parameters from audio files using linear predictive coding.


Python Tensorflow LaTeX

Know Where You Go

A progressive web application (PWA) using location based services and reverse geocoding.

JB LB DF FG FH PP BS

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analysis-of-audio-signals-using-linear-predictive-coding-t3000's Issues

Create statistic

  • perform speaker identification on basic neural network
  • safe results for different speakers (using unknown samples)
  • evaluate results (text)
    • Provide required info for comparing with MFCC for the study

lpc and lpcc feature extraction

Create a feature extraction class + methods for lpc and lpcc coefficients.

  • finalize functions and classes
  • integrate in the study

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