semvec/pypi-packages huggingface dataset:
remedian 0.1.2
Remedian: robust averaging of large data sets
License: MIT
Requires Python: >=2.7
Keywords: remedian median memory efficient big data

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# remedian
The  Remedian:  A  Robust  Averaging  Method  for  Large  Data  Sets - Python
implementation

This algorithm is used to approximate the median of several data chunks if
these data chunks cannot (or should not) be loaded into memory at once.

Given a data chunk of size `obs_size`, and `t` data chunks overall, the
Remedian class sets up a number `k_arrs` of arrays of length `n_obs`.

The median of the `t` data chunks of size `obs_size` is then approximated
as follows: One data chunk after another is fed into the `n_obs` positions
of the first array. When the first array is full, its median is calculated
and stored in the first position of the second array. After this, the first
array is re-used to fill the second position of the second array, etc.
When the second array is full, the median of its values is stored in the
first position of the third array, and so on.

The final "Remedian" is the median of the last array, after all `t` data
chunks have been fed into the object.

# Installation

`pip install remedian`

The dependencies should be installed automatically by pip.

# Installation of most recent version

1. activate your python environment
2. `git clone https://www.github.com/sappelhoff/remedian`
3. `cd remedian`
5. `pip install -e .`
6. then you should be able to `from remedian.remedian import Remedian`

# Usage

See the [example in the docs](https://remedian.readthedocs.io/en/latest/examples.html).

# References

> P.J. Rousseeuw, G.W. Bassett Jr., "The remedian: A robust averaging method
  for large data sets", Journal of the American Statistical Association, vol.
  85 (1990), pp. 97-104

> M. Chao, G. Lin, "The asymptotic distributions of the remedians", Journal of
  Statistical Planning and Inference, vol. 37 (1993), pp. 1-11

> Domenico Cantone, Micha Hofri, "Further analysis of the remedian algorithm",
  Theoretical Computer Science, vol. 495 (2013), pp. 1-16
my-voice-analysis 0.7
the analysis of voice (simultaneous speech) without the need of a transcription
License: MIT
Keywords: praat speech signal processing phonetics

## the new revision has got a new script and bugs fixed ## 

My-Voice-Analysis is a Python library for the analysis of voice (simultaneous speech, high entropy) 
without the need of a transcription. It breaks utterances and detects syllable boundaries, fundamental 
frequency contours, and formants. Its built-in functions recognize and measures:

1.	gender recognition, 
2.	speech mood (semantic analysis), 
3.	pronunciation posterior score 
4.	articulation-rate, 
5.	speech rate,
6.	filler words, 
7.	f0 statistics.

The library was developed based upon the idea introduced by Nivja DeJong and Ton Wempe [1], 
Paul Boersma and David Weenink [2], Carlo Gussenhoven [3], S.M Witt and S.J. Young [4] and 
Yannick Jadoul [5].

Peaks in intensity (dB) that are preceded and followed by dips in intensity are considered 
as potential syllable cores. 

My-Voice Analysis is unique in its aim to provide a complete quantitative and analytical way 
to study acoustic features of a speech. Moreover, those features could be analysed further 
by employing Python’s functionality to provide more fascinating insights into speech patterns. 

This library is for Linguists, scientists, developers, speech and language therapy clinics and
researchers. Please note that My-Voice Analysis is currently in initial state though in active 
development. While the amount of functionality that is currently present is not huge, more will 
be added over the next few months.

=============
Installation
=============
my-voice-analysis can be installed like any other Python library, using (a recent version of) 
the Python package manager pip, on Linux, macOS, and Windows:

------------- pip install my-voice-analysis
------------------------------
or, to update your installed version to the latest release:
-------------    pip install -u my-voice-analysis
---------------------------------
NOTE: 
After installing My-Voice-Analysis, copy the file 
-----------------myspsolution.praat from--------------
---------- https://github.com/Shahabks/my-voice-analysis ---- 
and save in the directory where you will save audio files for analysis.

Audio files must be in *.wav format, recorded at 44 kHz sample frame and 16 bits of resolution.

To check how the my-voice-analysis functions behave, please check 
---------------- EXAMPLES.docx on --------
------------- https://github.com/Shahabks/my-voice-analysis.-----

My-Voice-Analysis was developed by MYOLUTION Lab in Japan. It is part of New Generation of Voice
Recognition and Analysis Project in MYSOLUTION Lab. That is planned to rich the functionality of 
My-Voice Analysis by adding more advanced functions.

---------https://shahabks.github.io/Mysolution-Lab-AI/