Types of objects

Praat contains the following types of objects and Editors. For an introduction and tutorials, see Intro.

General purpose:

Matrix: a sampled real-valued function of two variables
PointProcess: a point process (PointEditor)
Sound: a sampled continuous process (SoundEditor, SoundRecorder, Sound files)
LongSound: a file-based version of a sound (LongSoundEditor)
Distributions, PairDistribution
Table, TableOfReal

Periodicity analysis:

• Tutorials:
    • Intro 4. Pitch analysis
    • Intro 6. Intensity analysis
    • Voice (jitter, shimmer, noise)
Pitch: articulatory fundamental frequency, acoustic periodicity, or perceptual pitch (PitchEditor)
Harmonicity: degree of periodicity
Intensity, IntensityTier: intensity contour

Spectral analysis:

• Tutorials:
    • Intro 3. Spectral analysis
    • Intro 5. Formant analysis
Spectrum: complex-valued equally spaced frequency spectrum (SpectrumEditor)
Ltas: long-term average spectrum
• Spectro-temporal: Spectrogram, BarkSpectrogram, MelSpectrogram
Formant: acoustic formant contours
LPC: coefficients of Linear Predictive Coding, as a function of time
Cepstrum, CC, LFCC, MFCC (cepstral coefficients)
Excitation: excitation pattern of basilar membrane
Excitations: an ensemble of Excitation objects
Cochleagram: excitation pattern as a function of time

Labelling and segmentation (see Intro 7. Annotation):

TextGrid (TextGridEditor)

Listening experiments:


Manipulation of sound:

• Tutorials:
    • Intro 8.1. Manipulation of pitch
    • Intro 8.2. Manipulation of duration
    • Intro 8.3. Manipulation of intensity
    • Filtering
    • Source-filter synthesis
PitchTier (PitchTierEditor)
Manipulation (ManipulationEditor): overlap-add

Articulatory synthesis (see the Articulatory synthesis tutorial):

Speaker: speaker characteristics of a woman, a man, or a child
Articulation: snapshot of articulatory specifications (muscle activities)
Artword: articulatory target specifications as functions of time
• (VocalTract: area function)

Neural net package:

FFNet: feed-forward neural net
Categories: for classification (CategoriesEditor)

Numerical and statistical analysis:

Eigen: eigenvectors and eigenvalues
Polynomial, Roots, ChebyshevSeries, LegendreSeries, ISpline, MSpline
Covariance: covariance matrix
Confusion: confusion matrix
Discriminant analysis: Discriminant
Principal component analysis: PCA
Correlation, ClassificationTable, SSCP
DTW: dynamic time warping

Multidimensional scaling:

Configuration (Salience)
Kruskal analysis: Dissimilarity (Weight), Similarity
INDSCAL analysis: Distance, ScalarProduct
Correspondence analysis: ContingencyTable

Optimality-theoretic learning (see the OT learning tutorial)

OTGrammar (OTGrammarEditor)


WordList, SpellingChecker

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© ppgb, November 9, 2014