Create MATLAB functions for the computation of basic time domain
characteristics in particular short-time frames. Inputs
for these functions should be:
- vector with analyzed signal
- sampling frequency of the signal
- length of short-time frame in miliseconds
- step (time movement) of short-time frame in miliseconds
Output parameters will be:
- 1st output parameter - column vector (matrix) with estimated
characteristics, where each row will contain characteristics for one
analyzed short-time frame,
- 2nd output parameter - column vector containing the time of each frame
beginning.
Use the following signal for the first
attempt SA176S01.CS0 - raw
data, fs=16000 Hz, for loading into MATLAB
use function loadbin.m
Possible structure for the computation of particular
characteristics - mypwr.m
Compute following parameters:
Energy
Power
Power in dB
RMS
Intensity
Peak-to-peak value
Zero-crossing-rate (function 'signum' is avaialble in MATLAB as sign)
Starting time of each short-time frame
Result : time-dependency of
signal short-time energy for
SA176S01.CS0. Compute it for legnths
30ms, 10ms, 5ms, 1ms.
Result:
- Display waveform and time-dependency of signal power and power in dBs in on-line
recorded utterance of the legnth 5s for the
short-time frame length 32ms with 50% overlapping. Signal
parameters: sampling frequency 16 kHz, 16 bit linear PCM, 1 channel
(mono signal).
- Estimate SNR from observed short-time power in dBs.
Computation of spectral charactersitics
Compute spectrogram for above mentioned signals with the
following setup of spectral analysis (observe and explain differences):
- short-time frame length 32 ms, frame overlapping 50%, Hamming window
- short-time frame length 5 ms, frame overlapping 50%, Hamming window
- short-time frame length 5 ms, frame overlapping 50%, Hamming
window, zero padding to NDFT 512
For above mentioned examples observe also short-time spectrum for
selected frame (voiced and unvoiced).
Observe the influence of preemphasis in speech spectrogram.
Result:
- Observe wavefrom and
spectrogram of for an on-line recorded utterance of the length 5 s using
short-time frame length 32ms, 50% overlapping, and WITHOUT and WITH
preemphasis application.
Further tasks for a homework:
Comparison of power analysis in MATLAB, Praat, and Wavesurfer
Work with the
signal mc20bc116016.ils_a
- raw data without header, fs=44100 Hz,
observe power in Praat (menu Intensity),
compute the power in MATLAB with the setup equivalent to Praat and
compare results,
observe power computation also within Wavesurfer (panel Power plot) and
set the parameters to obtain same results as in the preceeding two
cases in Praat and MATLAB.
Comparison of spectral analysis in MATLAB, Praat, and Wavesurfer
For signals SA176S01.CS0 (raw data, fs=16000 Hz) and mc20bc116016.ils_a
(raw data, fs=44100 Hz) observe spectrograms in
Praat and Wavesurfer.
Observe spectrograms for various setups, compare mainly the
differences for varying frame lengths and preemphasis setup.