Metadata-Version: 1.1
Name: lws
Version: 1.1
Summary: Fast spectrogram phase reconstruction using Local Weighted Sums
Home-page: https://github.com/Jonathan-LeRoux/lws
Author: Jonathan Le Roux
Author-email: leroux@merl.com
License: Apache 2.0
Download-URL: https://github.com/Jonathan-LeRoux/lws/archive/1.1.tar.gz
Description: LWS
        ===
        
        **Fast spectrogram phase recovery using Local Weighted Sums (LWS)**
        
        Author: Jonathan Le Roux -- 2008-2017
        
        .. image:: https://badge.fury.io/py/lws.svg
            :target: https://badge.fury.io/py/lws
        
        | LWS is a C/C++ library for which this package is a Python wrapper.
        | A Matlab/Mex wrapper is also available.
        
        License
        -------
        
        | Copyright (C) 2008-2017 Jonathan Le Roux
        | Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
        
        Reference
        ---------
        
        If you use this code, please cite the following papers.
        
        Batch LWS
        ~~~~~~~~~
        
        | Jonathan Le Roux, Hirokazu Kameoka, Nobutaka Ono, Shigeki Sagayama, 
        | "Fast Signal Reconstruction from Magnitude STFT Spectrogram Based on Spectrogram Consistency," 
        | in Proc. International Conference on Digital Audio Effects (DAFx), pp. 397--403, Sep. 2010.
        
        .. code::
        
            @InProceedings{LeRoux2010DAFx09,
              author =	 {Jonathan {Le Roux} and Hirokazu Kameoka and Nobutaka Ono and Shigeki Sagayama},
              title =	 {Fast Signal Reconstruction from Magnitude {STFT} Spectrogram Based on Spectrogram Consistency},
              booktitle =	 {Proc. International Conference on Digital Audio Effects (DAFx)},
              year =	 2010,
              pages =	 {397--403},
              month =	 sep
            }
            
        
        
        Online LWS, "No future" LWS
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        | Jonathan Le Roux, Hirokazu Kameoka, Nobutaka Ono, Shigeki Sagayama, 
        | "Phase initialization schemes for faster spectrogram-consistency-based signal reconstruction," 
        | in Proc. of ASJ Autumn Meeting, 3-10-3, Sep. 2010.
        
        .. code::
        
            @InProceedings{LeRoux2010ASJ09,
              author =	 {Jonathan {Le Roux} and Hirokazu Kameoka and Nobutaka Ono and Shigeki Sagayama},
              title =	 {Phase Initialization Schemes for Faster Spectrogram-Consistency-Based Signal Reconstruction},
              year =	 2010,
              booktitle =	 {Proceedings of the Acoustical Society of Japan Autumn Meeting (ASJ)},
              number =	 {3-10-3},
              month =	 mar
            }
            
        Installation
        ------------
        
        1. The easiest way to install `lws` is via `pip`:  
        
                .. code-block:: bash
        
                    pip install lws
        
        2. To compile from source using cython (required if one modifies the code):  
        
                .. code-block:: bash
        
                    cd python
                    LWS_USE_CYTHON=1 make 
        
        3. To compile from source using the pre-generated c source file (which was obtained with cython): 
        
                .. code-block:: bash
        
                    cd python
                    make
        
        4. Alternatively, one can first use cython to create a tarball, which can then be installed by pip:  
        
                .. code-block:: bash
        
                    cd python
                    make sdist
                    pip install dist/lws-1.0.tar.gz
        
        
        Usage
        -----
        .. code:: python
        
            import lws
            import numpy as np
            
            lws_processor=lws.lws(512,128, mode="speech") # 512: window length; 128: window shift
            X = lws_processor.stft(x) # where x is a single-channel waveform
            X0 = np.abs(X) # Magnitude spectrogram
            print('{:6}: {:5.2f} dB'.format('Abs(X)', lws_processor.get_consistency(X0))
            X1 = lws_processor.run_lws(X0) # reconstruction from magnitude (in general, one can reconstruct from an initial complex spectrogram)
            print('{:6}: {:5.2f} dB'.format('LWS', lws_processor.get_consistency(X1)))
        
        Options
        -------
        
        .. code:: python
        
            lws_processor=lws.lws(awin_or_fsize, fshift, L = 5, swin = None, look_ahead = 3,
                      nofuture_iterations = 0, nofuture_alpha = 1, nofuture_beta = 0.1, nofuture_gamma = 1,
                      online_iterations = 0, online_alpha = 1, online_beta = 0.1, online_gamma = 1,
                      batch_iterations = 100, batch_alpha = 100, batch_beta = 0.1, batch_gamma = 1,
                      symmetric_win = True, mode= None, fftsize=None, perfectrec=True)
        
        * ``awin_or_fsize``: either the analysis window, or a window length (in which case the sqrt(hann) window is used); the analysis window should be symmetric for the computations to be correct.
        * ``fshift``: window shift
        * ``L``: approximation order in the phase reconstruction algorithm, 5 should be good.
        * ``swin``: synthesis window (if None, it gets computed from the analysis window for perfect reconstruction)
        * ``look_ahead``: number of look-ahead frames in RTISI-LA-like algorithm, 3 should be good.
        * ``xxx_iterations``, ``xxx_alpha``, ``xxx_beta``, ``xxx_gamma``: number of iterations of algorithm xxx (where xxx is one of ``nofuture``, ``online``, or ``batch``), and parameters alpha/beta/gamma of the decreasing sparsity curve that is used to determine which bins get updated at each iteration. Any bin with magnitude larger than a given threshold is updated, others are ignored (``thresholds = alpha * np.exp(- beta * np.arange(iterations)**gamma)``)
        * ``symmetric_win``: determines whether to use a symmetric hann window or not
        * ``mode``: ``None``, ``'speech'``, or ``'music'``. This sets default numbers of iterations of each algorithm that seem to be good for speech and music signals. Disclaimer: your mileage may vary.
        * ``fftsize``: can be set longer than frame size to do 0-padding in the FFT. Note that 0-padding will be done symmetrically on the left and right of the window to enforce symmetry in the analysis window.
        * ``perfectrec``: whether to pad with zeros on each side to ensure perfect reconstruction at the boundaries too. 
        
        Three steps are implemented, and they can be turned on/off independently by appropriately setting the corresponding number of iterations:
        
        * "no future" LWS: phase initialization using LWS updates that only involve past frames
        * online LWS: phase estimation using online LWS updates, corresponding to a fast time-frequency domain version of RTISI-LA
        * LWS: phase estimation using batch LWS updates on the whole spectrogram
        
        
        Remarks
        -------
        
        1) The .cpp files are actually C code with some C99 style comments, but the .cpp extension is needed on Windows for mex to acknowledge the c99 flag (with .c, it is discarded, and -ansi used instead, leading to compilation errors)
        
        2) Because the module is a C extension, it cannot be reloaded (see <http://bugs.python.org/issue1144263>). In Jupyter Notebooks, in particular, autoreload will not work, and the kernel has to be restarted.
        
        Acknowledgements
        ----------------
        
        The recipe to wrap the LWS C code as a python module was largely inspired by the following post by Martin Sosic: <http://martinsosic.com/development/2016/02/08/wrapping-c-library-as-python-module.html>
        
Keywords: phase,reconstruction,stft,short-term Fourier Transform,spectrogram
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
