docfix

diff --git a/README.rst b/README.rst
index e4a075a1bce0d12a3e858786b0dafe6fd561b77b..9878f274ebf241899fec8d0d9a0be33ea9d2fcaa 100644 (file)
--- a/README.rst
+++ b/README.rst
@@ -24,10 +24,10 @@ Goals
 We just **need** a python library to:
 
 * **Do** asynchronous and fast audio processing with Python,
-* **Decode** audio frames from ANY format into numpy arrays,
+* **Decode** audio frames from **any** audio or video media format into numpy arrays,
 * **Analyze** audio content with some state-of-the-art audio feature extraction libraries,
 * **Organize**, serialize and save analysis metadata through various formats,
-* **Draw** various fancy waveforms, spectrograms and other cool graphers,
+* **Draw** various fancy waveforms, spectrograms and other cool visualizers,
 * **Transcode** audio data in various media formats and stream them through web apps,
 * **Playback** and **interact** **on demand** through a smart high-level HTML5 extensible player,
 * **Index**, **tag** and **organize semantic metadata** (see `Telemeta <http://telemeta.org>`_ which embed TimeSide).
@@ -105,10 +105,10 @@ News
 
 0.5.4
 
- * Bugfix realease
  * Encoder : transcoded streams where broken. Now fixed with some smart thread controls.
  * Analyzer : update VAMP plugin example in sandbox
- * Analyzer : NEW experimental plugin : Limsi Speech Activity Detection Systems (limsi_sad)
+ * Analyzer : new experimental plugin : Limsi Speech Activity Detection Systems (limsi_sad)
+ * Decoder : process any media in streaming mode giving its URL 
  * Install : fix some setup requirements
  
 0.5.3
@@ -266,6 +266,9 @@ API / Documentation
 Install
 =======
 
+The TimeSide engine is intended to work on all Unix / Linux platforms.
+MacOS X and Windows versions will soon be explorated.
+
 TimeSide needs some other python modules to run. The following methods explain how to install all dependencies on various Linux based systems.
 
 On Debian, Ubuntu, etc:
@@ -287,7 +290,7 @@ On Fedora and Red-Hat:
 
  $ sudo pip install timeside
 
-Otherwise, you can also install all dependencies and then use pip::
+On other Linux platforms, you can also install all dependencies and then use pip::
 
  $ sudo pip install timeside
 
@@ -298,13 +301,6 @@ python (>=2.7), python-setuptools, python-gst0.10, gstreamer0.10-plugins-good, g
 gstreamer0.10-plugins-ugly, python-aubio, python-yaafe, python-simplejson, python-yaml, python-h5py,
 python-scipy, python-matplotlib, python-matplotlib
 
-Platforms
-==========
-
-The TimeSide engine is intended to work on all Unix / Linux platforms.
-MacOS X and Windows versions will soon be explorated.
-The player should work on any modern HTML5 enabled browser.
-Flash is needed for MP3 if the browser doesn't support it.
 
 Shell Interface
 ================
@@ -368,6 +364,9 @@ TODO list:
     * zoom
     * layers
 
+The player should work on any modern HTML5 enabled browser.
+Flash is needed for MP3 if the browser doesn't support it.
+
 Development
 ===========
 

diff --git a/timeside/analyzer/limsi_sad.py b/timeside/analyzer/limsi_sad.py
index b5033174bbd3b1ee31170c76eebeb01db80e8214..8e53992da45b1ba7cf724501deb71ea7b9c3cf67 100644 (file)
--- a/timeside/analyzer/limsi_sad.py
+++ b/timeside/analyzer/limsi_sad.py
@@ -29,6 +29,7 @@ import numpy as N
 import pickle
 import os.path
 
+
 class GMM:
 
     def __init__(self, weights, means, vars):
@@ -52,7 +53,8 @@ class LimsiSad(Analyzer):
     """
     Limsi Speech Activity Detection Systems
     LimsiSad performs frame level speech activity detection based on GMM models
-    For each frame, it computes the log likelihood difference between a speech model and a non speech model. The highest is the estimate, the largest is the probability that the frame corresponds to speech.
+    For each frame, it computes the log likelihood difference between a speech model and a non speech model. 
+    The highest is the estimate, the largest is the probability that the frame corresponds to speech.
     The initialization of the analyzer requires to chose a model between 'etape' and 'maya'
     'etape' models were obtained on data collected by LIMSI in the framework of ETAPE ANR project
     'maya' models were obtained on data collected by EREA – Centre Enseignement et Recherche en Ethnologie Amerindienne