Metadata-Version: 2.1
Name: SpectroMap
Version: 1.0.0
Summary: SpectroMap is a peak detection algorithm that computes the constellation map for a given signal
Home-page: https://github.com/Aaron-AALG/SpectroMap
Download-URL: https://github.com/Aaron-AALG/SpectroMap/releases/tag/spectromap_1.0.0
Author: Aaron Lopez-Garcia
Author-email: aaron.lopez@uv.es
License: GPL-3.0
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
License-File: LICENSE
Requires-Dist: numpy (>=1.19)
Requires-Dist: scipy (>=1.6.3)

SpectroMap
======================

SpectroMap is a peak detection algorithm that computes the constellation map (or audio fingerprint) of a given signal.

Installation
======================

You can install the SpectroMap library from GitHub::

    git clone https://github.com/Aaron-AALG/spectromap.git
    python3 -m pip install -e spectromap


You can also install it directly from PyPI::

    pip install spectromap

Usage
======================

This packages contains the spectromap object that manages the full process of audio fingerprinting extraction. Given a signal Y, we just have to instantiate the class with Y and the corresponding kwargs (if needed).

spectrogram object
------------------

An example to apply SpectroMap over a signal is:

.. code:: python

    import numpy as np
    from spectromap.functions.spectromap import spectromap

    y = np.random.rand(44100)
    kwargs = {'fs': 22050, 'nfft': 512, 'noverlap':64}

    # Instantiate the SpectroMap object
    SMap = spectromap(y, **kwargs)

    # Get the spectrogram representation plus its time and frequency bands
    f, t, S = SMap.get_spectrogram()

    # Extract the topological prominent elements from the spectrogram, known as "Peak detection".
    # We get the coordinates (time, freq) of the peaks and the matrix with just these peaks.
    fraction = 0.15 # Fraction of spectrogram to compute local comparisons
    condition = 2   # Axis to analyze (0: Time, 1: Frequency, 2: Time+Frequency)
    id_peaks, peaks = SMap.peak_matrix(fraction, condition)

    # Get the peaks coordinates as as (s, Hz, dB)-array.
    extraction_t_f_dB = SMap.from_peaks_to_array()


peak_search function
---------------------

In case you desire to compute the spectrogram by yourself, then you can make use of the peak search function instead.

.. code:: python

    from spectromap.functions.spectromap import peak_search

    fraction = 0.05 # Fraction of spectrogram to compute local comparisons
    condition = 2   # Axis to analyze (0: Time, 1: Frequency, 2: Time+Frequency)
    id_peaks, peaks = peak_search(spectrogram, fraction, condition)

Cite this work
======================

If you use SpectroMap in your research I would appreciate a citation to the following paper:

.. code::

    @misc{https://doi.org/10.48550/arxiv.2211.00982,
    doi = {10.48550/ARXIV.2211.00982},
    url = {https://arxiv.org/abs/2211.00982},
    author = {López-García, Aarón},
    title = {SpectroMap: Peak detection algorithm for audio fingerprinting},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
    }

