Metadata-Version: 1.1
Name: spectromap
Version: 0.1.1
Summary: SpectroMap is a peak detection algorithm that computes the constellation map for a given signal
Home-page: https://github.com/Aaron-AALG/spectromap
Author: Aaron Lopez-Garcia
Author-email: aaron.lopez@uv.es
License: GPL-3.0
Download-URL: https://github.com/Aaron-AALG/spectromap/releases/tag/spectromap_0.1.1
Description: 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 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 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)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
