Acoustic Scene Classification’s documentation!¶

Contents:

Acoustic Scene Classification¶

Development
Last release
PyPI status

Acoustic Scene Auditory (ASC) using Convolutional Neural Network (CNN) is a project being part of the Machine Learning Nanodegreen program given by Udacity. For a description of the proposal, you can refer to its web version.

Dataset¶

The dataset can be downloaded on the Zenodo server.

Features¶

TODO

Credits¶

Project created by Matthieu Berjon and based on the work of Simone Battaglino, Ludovick Lepauloux and Nicholas Evans.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Installation¶

Stable release¶

To install Acoustic Scene Classification, run this command in your terminal:

$ pip install asc

This is the preferred method to install Acoustic Scene Classification, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

In order to properly work you need to ensure that libsndfile is installed such as on Debian:

$ sudo apt-get install libsndfile

From sources¶

The sources for Acoustic Scene Classification can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/mattberjon/asc

Or download the tarball:

$ curl  -OL https://github.com/mattberjon/asc/tarball/master

Once you have a copy of the source, you can install it with:

$ python setup.py install

Usage¶

To use Acoustic Scene Classification in a project:

import asc

You can as well use it through a CLI:

$ asc –help

Configuration¶

Parameters	Values	Description
audio.samplerate	(int) default=44100	Samplerate of the project in Hertz
path.tmp	(str) default=’/tmp’	Path to the temporary download folder
path.data	(str) default=’~/’	Path to the data folder
path.url_list	(str) no default value \| Path to the file containing the URLs of the data to download

Contributing¶

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions¶

Report Bugs¶

Report bugs at https://github.com/mattberjon/asc-cnn/issues.

If you are reporting a bug, please include:

Your operating system name and version.
Any details about your local setup that might be helpful in troubleshooting.
Detailed steps to reproduce the bug.

Fix Bugs¶

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features¶

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation¶

Acoustic Scene Classification could always use more documentation, whether as part of the official Acoustic Scene Classification docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback¶

The best way to send feedback is to file an issue at https://github.com/mattberjon/asc-cnn/issues.

If you are proposing a feature:

Explain in detail how it would work.
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!¶

Ready to contribute? Here’s how to set up asc for local development.

Fork the asc repo on GitHub.

Clone your fork locally:

$ git clone git@github.com:your_name_here/asc-cnn.git

Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
```
$ mkvirtualenv asc
$ cd asc/
$ python setup.py develop
```
Create a branch for local development:
```
$ git checkout -b name-of-your-bugfix-or-feature
```
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
```
$ flake8 asc tests
$ python setup.py test or py.test
$ tox
```
To get flake8 and tox, just pip install them into your virtualenv.

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst.
The pull request should work for Python 2.6, 2.7, 3.3, 3.4 and 3.5, and for PyPy. Check https://travis-ci.org/mattberjon/asc-cnn/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ py.test tests.test_asc

Credits¶

Development Lead¶

Matthieu Berjon <matthieu@berjon.net>

Contributors¶

None yet. Why not be the first?

History¶

0.3.1 (2017-08-22)¶

Added¶

Issue #8: config file feature
Issue #11: Samplerate setting up through CLI
Issue #10: Spectrogram calculation
Issue #24: Add a ‘config’ subcommand to the CLI
Issue #25: Read a value from the config file
Issue #21: Script to verify the samplerate sanity of the database
Issue #30: Function to transform milliseconds to samples

Changed¶

Issue #23: Update of the documentation for the installation process
Issue #26: Update of the CLI subcommand ‘getdata’
Issue #32: Exception handling in config reading

Deprecated¶

Nothing.

Removed¶

Nothing.

Fixed¶

Issue #9: trailing slash on data path
Issue #13: Update of the config file parameters
Issue #15: Crash when setting up the samplerate
Issue #22: Update of the dependency list
Issue #25: Update of the config file
Update of docstrings
Update of the documentation

Security¶

Update of the Coveralls library from 1.1 to 1.2.0

0.2.3 (2017-08-07)¶

Added¶

Travis config file
pytest suite
CLI tests
issue #5: Package coverage for the development setup
issue #6: adding of a clear_zip() to clean the archive files

Changed¶

Python 3.3 testing removed

Deprecated¶

Nothing

Removed¶

Nothing

Fixed¶

Issue #4: invalid functools dependency
Issue #7: update of docstring unzip_data()

Security¶

Nothing

0.2.2 (2017-07-31)¶

Added¶

nothing

Changed¶

Updated of the ChangeLog (HISTORY.rst)

Deprecated¶

nothing

Removed¶

nothing

Fixed¶

nothing

Security¶

nothing

0.2.1 (2017-07-31)¶

Added¶

nothing

Changed¶

nothing

Deprecated¶

nothing

Removed¶

nothing

Fixed¶

unzip_data() url list issue
download of temporary files in the right directory

Security¶

nothing

0.2.0 (2017-07-31)¶

Added¶

Adding of a documentation (with docstrings)
CLI command to download and unzip data automatically
creation of a python package
configuration of Tox
download() method in data class

Changed¶

Use of RST instead of markdown for all the documentation
development packages are now in requirements_dev.txt

Deprecated¶

nothing

Removed¶

nothing

Fixed¶

source files satisfy PEP8
bug fix on getdata cli

Security¶

Update of all packages to their latest versions

0.1.0 (2017-07-25)¶

First release as a package.

asc package¶

Submodules¶

asc.asc module¶

asc.cli module¶

asc.data module¶

Data module

This module lets the program to download the data from the server.

class asc.data.Data[source]¶

Bases: object

Data collection.

clear_zip(url_list, tmp_dir)[source]¶

Clear the archives

Delete the downloaded archives.

Args:: url_list (str): list of strings containing the urls tmp_dir (str): path where are store the archives

download(url_list, dest_dir)[source]¶

Download data from a list of URLs

Download data from a list of URLs and display a progress according to the size of the file.

Args:: url_list(list): list containing URLs of files to download. dest_dir (str): path to where download the data.

file_to_list(filename)[source]¶

Parse a file and transform it into a list

Parse a file line by line to make a list with the urls contained inside.

Args:: filename (str): path to the file containing the URLs.

unzip_data(url_list, origin_dir, dest_dir)[source]¶

Unzip data files

Unzip data files given a list of file names, the path where they are store and where they will be unzipped.

Args:: url_list (str): list of strings containing the urls origin_dir (str): directory where are stored the files dest_dir (str): directory where the files will be extracted

asc.utils module¶

asc.utils.conf_param_extract(parameter)[source]¶

Extract the section and option given the parameter.

Extract the section and option given the parameter that is in the specific format section.option

Args:: parameter (str): parameter with the format ‘section.option’
Returns:: The section and option

asc.utils.ms2smp(ms, sample_rate)[source]¶

Milliseconds to samples converter.

Simple converter in order to compute the number of samples for a given time frame in milliseconds and the sampling rate.

Args:

ms (int): Number of milliseconds. sample_rate (int): Sampling rate in Hertz.

Returns:: Return the number of sample (forced as an int).

asc.utils.read_config(section, option, config_obj)[source]¶

Look for a given option in a config file.

If exists, return the value in a config file according to the section and option.

Args:

section (str): section related to the option looked for. option (str): option related to the value looked for. config_obj (obj): configparser object.

Returns:

value given for a specific tuple section/option.

Todo:

Be able to cast the data into the right type.

asc.utils.write_config(section, option, data, config_obj, config_file)[source]¶

Write/Update the configuration file

Write or update the configuration file according to the section or option provided.

Args:: section (str): Name of the section option (str): Name of the option data (str): data related to the option to store config_obj (str): instance of the configuration object) config_file (str): instance of the configuration file where to save

the data
Returns:: None
Todo:: Need to cast the object to string before saving the data.

Module contents¶

Top-level package for Acoustic Scene Classification.

Acoustic Scene Classification’s documentation!¶