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rPraat and mPraat: Connect Praat, Matlab, and R

rPraat, mPraat

Read, write, create, and manipulate Sound, TextGrid, PitchTier and Pitch files from Praat in R and Matlab.


Abstract: The paper presents the rPraat package for R / mPraat toolbox for Matlab which constitutes an interface between the most popular software for phonetic analyses, Praat, and the two more general programmes. The package adds on to the functionality of Praat, it is shown to be superior in terms of processing speed to other tools, while maintaining the interconnection with the data structure of R and Matlab, which provides a wide range of subsequent processing possibilities. The use of the proposed tool is demonstrated on a comparison of real speech data with synthetic speech generated by means of dynamic unit selection.


ACR method - Power spectral density approach to Granger causality in frequency domain in MATLAB (source code)

ACR - Autoregressive causal relation

ACR - Autoregressive causal relation: Digital filtering approach to causality measures in frequency domain

By Tomáš Bořil, Pavel Sovka. Full text of the paper in pdf: Elsevier, Digital Signal Processing, 2013

Full text of the paper in pdf (preprint draft version): boril_sovka_2013_autoregressive_causal_relation.pdf

Download the source code of the ACR method and the experiment in Matlab/Octave for free

Please cite this article as


Abstract: A novel measure of the Autoregressive Causal Relation based on a multivariate autoregressive model is proposed. It reveals the strength of the connections among a simultaneous time series and also the direction of the information flow. It is defined in the frequency domain, similar to the formerly published methods such as: Directed Transfer Function, Direct Directed Transfer Function, Partial Directed Coherence, and Generalized Partial Directed Coherence. Compared to the Granger causality concept, frequency decomposition extends the possibility to reveal the frequency rhythms participating on the information flow in causal relations.

The Autoregressive Causal Relation decomposes diagonal elements of a spectral matrix and enables a user to distinguish between direct and indirect causal relations. The main advantage lies in its definition using power spectral densities, thus allowing for a clear interpretation of strength of causal relation in meaningful physical terms.

The causal measures can be used in neuroscience applications like the analysis of underlying structures of brain connectivity in neural multichannel time series during different tasks measured via electroencephalography or functional magnetic resonance imaging, or other areas using the multivariate autoregressive models for causality modeling like econometrics or atmospheric physics but this paper is focused on theoretical aspects and model data examples in order to illustrate a behavior of methods in known situations.


Speech - collaboration with Hynek Bořil

We have developed H&T Recorder under .Net platform and DirectX. It is used for recording the Czech Lombard Speech Database.

H&T Recorder Screenshot