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.. |logo| image:: logo_lium.png


Welcome to SIDEKIT for diarization documentation!
==================================================

| **SIDEKIT for diarization** (**s4d** as short name) is an open source package  extension of **SIDEKIT** for Speaker diarization .
| The aim of **S4D** is to provide an educational and efficient toolkit 
| for speaker diarization including the whole chain of treatment 
| that goes from the audio data to the analysis of the system performance.


:Authors: 
    Sylvain Meignier \&
    Anthony Larcher

:Version: 0.1.0 of 2015/11/15 

Implementation
--------------

| **s4d** has been designed and written in `Python <https://www.python.org>`_ and released under LGPL :ref:`license`
| to allow a wider usage of the code that, we hope, could be beneficial to the community.
| The structure of the core package makes use of a limited number of classes in order
| to facilitate the readability and reusability of the code.
| **s4d** has been tested under Python 2.7 and Python 3.4 for both Linux and MacOS.


Citation
--------

When using **s4d** for research, please cite:

Authors, **Title of the paper to come**, in, issue, year, pages...


What for
--------

| **s4d** aims at providing the whole chain of tools required to perform speaker diarization.
| **s4d** extends **SIDEKIT** and the main tools available include:

   * Acoustic features extraction off **SIDEKIT**

      - Linear-Frequency Cepstral Coefficients (LFCC)
      - Mel-Frequency Cepstral Coefficients (MFCC)
      - RASTA filtering
      - Energy-based Voice Activity Detection (VAD)
      - normalization (CMS, CMVN, Short Term Gaussianization)

   * Modeling and classification
       - from *SIDEKIT*
             - Gaussian Mixture Models (GMM)
             - *i* - vectors
             - Probabilistic Linear Discriminant Analysis (PLDA)
             - Joint Factor Analysis (JFA)
             - Support Vector Machine (SVM)
       - from *S4D*
           - Mono gaussian model with full covariance matrix
           - BIC segmentation and
           - BIC Hierarchical Agglomerative Clustering (HAC) with gaussian models
           - Cross-Likelihood Ratio HAC with MAP-GMM models (CLR-HAC)
           - i-vector base clustering: HAC, graph based clustering and Integer Linear Programing clustering (ILP)
           - Presentation of the results
             - DER scoring for single and cross-show diarization
             - Segmentation output viewer




Contents
========

.. toctree::
   :maxdepth: 3
   :titlesonly:

   aboutS4D
   howto
   s4d
   tutorial

Additional material
===================

.. toctree::
   :maxdepth: 2

   links
   references
   known_errors


Sponsors
========

|logo|

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`