Difference between revisions of "Resources and papers on Audio, Music and Speech"

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image restoration, z-transform, FIR filters, adaptive filters,
image restoration, z-transform, FIR filters, adaptive filters,
wavelets, and filterbanks.
wavelets, and filterbanks.
[http://videos-phone.net/ Video to Phone]<br/>
You can find all kinds of most popular phone in this site, for example:iPhone,BlackBerry,Gphone,Motorola,Nokia,Samsung,etc, It also provides video to iPhone, video to BlackBerry,  video to 3GP, video to Gphone, video to Motorola, video to Nokia, video to Samsung, etc, I believe that you will find what you like!  and it  gives your life more fun, Good luck to you !
[http://cnx.org/content/col10252/latest/ Methods for Voice Conversion]<br/>
[http://cnx.org/content/col10252/latest/ Methods for Voice Conversion]<br/>

Latest revision as of 05:30, 9 April 2010

Frequency and Music
An overview of frequency, harmonic (Fourier) series, and their relationship to music.

Audio Localization
This course has been created as an introduction to audio localization, and how beamforming can be applied in a real-time environment.

Fundamentals of Digital Signal Processing Lab
The purpose of this lab is to familiarize students with the DSP development workstation in the signal processing lab by examining sampling, analysis, and reconstruction of continuous-time signals. Specifically, we will first look at sampling/reconstruction of continuous-time signals. We will then examine time- and frequency-domain displays. Finally, we will examine the importance of sampling frequency and its effects on aliasing.

Intro to Digital Signal Processing
The course provides an introduction to the concepts of digital signal processing (DSP). Some of the main topics covered include DSP systems, image restoration, z-transform, FIR filters, adaptive filters, wavelets, and filterbanks.

Methods for Voice Conversion
This course explores methods in signal processing to perform voice conversion: producing the words from one speaker in the voice of another. This is the Elec 301 project of Justin Chen, Matthew Hutchinson, Gina Upperman, and Brian VanOsdol.

Musical Instrument Recognition
To detect the pitch and instrument of a monophonic signal. To decompose polyphonic signals into their component pitches and instruments by analyzing the waveform and spectra of each instrument. Elec 301 Project Fall 2005.