Basic time-domain and spectral characteristics

**Tasks to do: **

**Computation of basic time-domain characteristics**- 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 - vykon.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

- 1st checked result : time-dependency of signal short-time energy for SA176S01.CS0. Compute it for legnths 30ms, 10ms, 5ms, 1ms.
- Repeat for the signal
*mc20bc116016.ils_a*- raw data, fs=44100 Hz. - 2nd checked result:waveform and time-dependency of energy in dBs in on-line recorded signal for the short-time frame length 32ms with 50% overlapping.

- Create MATLAB functions for the computation of basic time domain
characteristics in particular short-time frames. Inputs
for these functions should be:
**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. - 3rd checked result: wavefrom and spectrogram of signal SA176S01.CS0 for the short-time frame length 32ms with 50% overlapping WITHOUT preemphasis
- 4th checked result: wavefrom and spectrogram of signal SA176S01.CS0 for the short-time frame length 32ms with 50% overlapping WITH preemphasis
- Observe waveforms and spectrograms also for on-line recorded signals.

- Compute

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.

- Work with the
signal
**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.

- For signals