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### BE2M31ZRE - seminar - TASK No. 2 Basic time-domain and spectral characteristics

• 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 - 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.
• Repeat for the signal mc20bc116016.ils_a - raw data, fs=44100 Hz.
• 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.