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Tasks to do:

• Estimation of fundamental frequency (pitch) for one signal frame.
  Estimate the fundamental frequency (pitch of the voice) for the following voiced frame of speech signal muz1-AA-frame.CS0 (raw data without header, fs=16000 Hz, for loading into MATLAB use function loadbin.m). Make the estimation in the following steps:
  • compute and observe autocorrelation of the frame (biased estimation using xcorr)
  • on the basis of maximum location compute fundamental period in samples (L_0), fundamental period in seconds (T_0) and the value of pitch, i.e. the fundamental frequency in Hz (f_0),
  • fcn max in MATLAB returns maximum and also its position as the second output parameter,
  • restrict the looking for the maximum accroding to the typical range of human voice pitch which is 60-260 Hz.
  • Result: pro for one voiced frame muz1-AA-frame.CS0 observe:
    - time waveform fo the singal commonly with the computed autocorrelation function
    - boundaries for the possible maximum location in computed autocorrelation function,
    - on the basis of detected maximum and its position, compute the values of L_0, T_0 a f_0.

  • Repeat the same procedure and observe the results also for analogous voiced frames of other speakers, observe the signal variability same as the differences in estimated pitch values and maxima positions. Use the following signals:
    - muz2-AA-frame.CS0, muz3-AA-frame.CS0, muz4-AA-frame.CS0, zena1-AA-frame.CS0, zena2-AA-frame.CS0, zena3-AA-frame.CS0
  • Repeat also for different voiced frames of one speaker (one male and one female speaker) and observe the variability and stability of f0 estimation fro particular speakers:
    - muz3-AA-frame.CS0, muz3-EE-frame.CS0, muz3-II-frame.CS0, muz3-OO-frame.CS0, muz3-UU-frame.CS0
    - zena3-AA-frame.CS0, zena3-EE-frame.CS0, zena3-II-frame.CS0, zena3-OO-frame.CS0, zena3-UU-frame.CS0
  • Repeat also for one unvoiced speech frame muz1-SS-frame.CS0, zena3-SS-frame.CS0 and observe mainly the differences in time waveform same as in the estimation of autocorrealtion function for unvoiced frame with noise character.

• Pitch estimation in longer utternace
  • Implement estimation of fundamental frequency within particular short-time frames for the whole utterance (similarly as for the power computation realized within Task No.2). Compute pitch for the each short-time frame of the analyzed signal.
  • The length of the frame should be 32 ms, the frame step should be 16 ms (i.e. work with 50% overlapping of analyzed short-time frames).
  • Result: Signal waveform and computed value of the pitch (f_0) for all available frames in the whole utterance SA176S01.CS0 (raw data, fs=16000 Hz) same as for your own on-line recorded signal.

• The detection of voiced frames during the pitch estimation
  • Try to detect unvoice frames on the basis of ZCR. Fundamental frequency for uvoiced frame should be set to f_0 = 10 Hz.
  • Detect also speech pause frames on the basis of energy computation (for the frames with low energy). Set the value of f_0 = 0 Hz for non-speech frame.
  • Result: Signal waveform and computed f_0 for voiced frames only in on-line recorded signal (or for analyzed utterance SA176S01.CS0).
  • POSSIBLE IMPROVEMENT - think about the smooting of pitch estimation using median filtering (function med.m) or other postprocessing.

• Further signals for processing
  • mc20bc116016.ils_a - raw data without header, fs=44100 Hz,
  • a30650b1.wav - wav format, fs=8000 Hz
  • your own utternaces from database zreratdb available as *.CS0 files (resampled to fs = 16000 Hz), for efficient download are your utterances available in the following archive zrerat_blocken_2024_cs0.zip
  • NOTE - Use function loadbin.m to load raw-data (without header) to MATLAB, to load the data in wav-format use standard MATLAB function audioread.

• Pitch of your voice.
  • Result: Compute the average value of your voice pitch (f_0) computed from voiced frames only.

• Try to estimate the pitch also in Praat, and Wavesurfer.