tfr 0.2.4
Time-frequency reassigned spectrograms
License: MIT

tfr - time-frequency reassignment in Python
===========================================

|PyPI version| |Supported Python versions| |License|

Spectral audio feature extraction using `time-frequency
reassignment <https://en.wikipedia.org/wiki/Reassignment_method>`__.

Besides normal spectrograms it allows to compute reassigned
spectrograms, transform them (eg. to log-frequency scale) and requantize
them (eg. to musical pitch bins). This is useful to obtain good features
for audio analysis or machine learning on audio data.

A reassigned spectrogram often provides more precise localization of
energy in the time-frequency plane than a plain spectrogram. Roughly
said in the reassignment method we use the phase (which is normally
discarded) and move the samples on the time-frequency plane to a more
suitable place computed from derivatives of the phase.

This library supports reassignment in both frequency and time (both are
optional). As well it does requantization from the input overlapping
grid to an non-overlapping output grid.

It is a good building block to compute `chromagram
features <https://en.wikipedia.org/wiki/Chroma_feature>`__ (aka pitch
class profiles) where pitch is transformed into pitch class by ignoring
the octave. See also `harmonic pitch class
profiles <https://en.wikipedia.org/wiki/Harmonic_pitch_class_profiles>`__.

Installation
------------

::

    pip install tfr

Or for development (all code changes will be available):

::

    git clone https://github.com/bzamecnik/tfr.git
    pip install -e tfr

Usage
-----

Split audio signal to frames
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can read time-domain signal from an audio file (using the
``soundfile`` library) and split it into frames for spectral processing.

::

    import tfr
    signal_frames = tfr.SignalFrames('audio.flac')

``SignalFrames`` instance contains the signal split into frames and some
metadata useful for further processing.

The signal values are normalized to [0.0, 1.0] and the channels are
converted to mono.

It is possible to provide the signal a numpy array as well.

::

    import tfr
    x = np.sin(2 * np.pi * 10 * np.linspace(0, 1, 1000))
    signal_frames = tfr.SignalFrames(x)

Minimal example - pitchgram from audio file
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

::

    import tfr
    x_pitchgram = tfr.pitchgram(tfr.SignalFrames('audio.flac'))

From audio frames it computes a reassigned pitchgram of shape
``(frame_count, bin_count)`` with values being log-magnitudes in dBFS
``[-120.0, 0.0]``. Sensible parameters are used by default, but you can
change them if you wish.

Reassigned spectrogram
~~~~~~~~~~~~~~~~~~~~~~

Like normal one but sharper and requantized.

::

    import tfr
    x_spectrogram = tfr.reassigned_spectrogram(tfr.SignalFrames('audio.flac'))

Signal frames with specific parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  ``frame_size`` - affects the FFT size - trade-off between frequency
   and time resolution, good to use powers of two, eg. 4096
-  ``hop_size`` - affects the overlap between frames since a window
   edges fall to zero, eg. half of frame_size (2048)

::

    import tfr
    signal_frames = tfr.SignalFrames('audio.flac', frame_size=1024, hop_size=256)

General spectrogram API
~~~~~~~~~~~~~~~~~~~~~~~

The ``pitchgram`` and ``reassigned_spectrogram`` functions are just
syntax sugar for the ``Spectrogram`` class. You can use it directly to
gain more control.

General usage:

::

    x_spectrogram = tfr.Spectrogram(signal_frames).reassigned()

From one Spectrogram instance you can efficiently compute reassigned
spectrograms with various parameters.

::

    s = tfr.Spectrogram(signal_frames)
    x_spectrogram_tf = s.reassigned(output_frame_size=4096)
    x_spectrogram_f = s.reassigned(output_frame_size=512)

Different window function (by default we use Hann window):

::

    import scipy
    x_spectrogram = tfr.Spectrogram(signal_frames, window=scipy.blackman).reassigned()

Different output frame size (by default we make it the same as input hop
size):

::

    x_spectrogram = tfr.Spectrogram(signal_frames).reassigned(output_frame_size=512)

Disable reassignment of time and frequency separately:

::

    s = tfr.Spectrogram(signal_frames)
    x_spectrogram = s.reassigned(reassign_time=False, reassign_frequency=False)
    x_spectrogram_t = s.reassigned(reassign_frequency=False)
    x_spectrogram_f = s.reassigned(reassign_time=False)
    x_spectrogram_tf = s.reassigned()

Disable decibel transform of output values:

::

    x_spectrogram = tfr.Spectrogram(signal_frames).reassigned(magnitudes='power')

Magnitudes in the spectrogram can be transformed at the end in multiple
ways given by the ``magnitudes`` parameter:

-  ``linear`` - energy spectrum
-  ``power`` - power spectrum
-  ``power_db`` - power spectrum in decibels, range: [-120, 0]
-  ``power_db_normalized`` - power spectrum in decibels normalized to
   range: [0, 1]

   -  this is useful as a feature

Use some specific transformation of the output values.
``LinearTransform`` (default) is just for normal spectrogram,
``PitchTransform`` is for pitchgram. Or you can write your own.

::

    x_spectrogram = tfr.Spectrogram(signal_frames).reassigned(transform=LinearTransform())

::

    x_pitchgram = tfr.Spectrogram(signal_frames).reassigned(transform=PitchTransform())

::

    class LogTransform():
      def __init__(self, bin_count=100)
        self.bin_count = bin_count

      def transform_freqs(self, X_inst_freqs, sample_rate):
          X_y = np.log10(np.maximum(sample_rate * X_inst_freqs, eps))
          bin_range = (0, np.log10(sample_rate))
          return X_y, self.bin_count, bin_range

    x_log_spectrogram = tfr.Spectrogram(signal_frames).reassigned(transform=LogTransform())

Pitchgram parameters
~~~~~~~~~~~~~~~~~~~~

In pitchgram the frequencies are transformed into pitches in some tuning
and then quantized to bins. You can specify the tuning range of pitch
bins and their subdivision.

-  ``tuning`` - instance of ``Tuning`` class, transforms between pitch
   and frequency
-  ``bin_range`` is in pitches where 0 = 440 Hz (A4), 12 is A5, -12 is
   A3, etc.
-  ``bin_division`` - bins per each pitch

Extract features via CLI
~~~~~~~~~~~~~~~~~~~~~~~~

::

    # basic STFT spectrogram
    python -m tfr.spectrogram_features audio.flac spectrogram.npz
    # reassigned STFT spectrogram
    python -m tfr.spectrogram_features audio.flac -t reassigned reassigned_spectrogram.npz
    # reassigned pitchgram
    python -m tfr.spectrogram_features audio.flac -t pitchgram pitchgram.npz

Look for other options:

::

    python -m tfr.spectrogram_features --help

scikit-learn transformer
~~~~~~~~~~~~~~~~~~~~~~~~

In order to extract pitchgram features within a sklearn pipeline, we can
use ``PitchgramTransformer``:

::

    import soundfile as sf
    x, fs = sf.read('audio.flac')

    from tfr.signal import to_mono
    from tfr.sklearn import PitchgramTransformer
    ct = PitchgramTransformer(sample_rate=fs)
    x_pitchgram = ct.transform(x)

    # output:
    #  - shape: (frame_count, bin_count)
    #   - values in dBFB normalized to [0.0, 1.0]

Status
------

Currently it’s alpha. I’m happy to extract it from some other project
into a separate repo and package it. However, the API must be completely
redone to be more practical and obvious.

About
-----

-  Author: Bohumír Zámečník ([@bzamecnik](http://twitter.com/bzamecnik))
-  License: MIT

Support the project
~~~~~~~~~~~~~~~~~~~

Need some consulting or coding work regarding audio processing, machine
learning or big data? Drop me a message via
`email <mailto:bohumir.zamecnik@gmail.com?subject=Work+inquiry+-+based+on+tfr>`__
or `LinkedIn <https://www.linkedin.com/in/bohumirzamecnik>`__. Or just
say hello :).

Literature
----------

-  `A Unified Theory of Time-Frequency
   Reassignment <https://arxiv.org/abs/0903.3080>`__ - Kelly R. Fitz,
   Sean A. Fulop, Digital Signal Processing 30 September 2005
-  `Algorithms for computing the time-corrected instantaneous frequency
   (reassigned) spectrogram, with
   applications <http://acousticslab.org/learnmoresra/files/fulopfitz2006jasa119.pdf>`__
   - Sean A. Fulop, Kelly Fitz, Journal of Acoustical Society of
   America, Jan 2006
-  `Time Frequency Reassignment: A Review and
   Analysis <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.4.1053&rep=rep1&type=pdf>`__
   - Stephen W. Hainsworth, Malcolm D. Macleod, Technical Report,
   Cambridge University Engineering Dept.
-  `Improving the Readability of Time-Frequency and Time-Scale
   Representations by the Reassignment
   Method <http://perso.ens-lyon.fr/patrick.flandrin/IEEE_SP1995.pdf>`__
   - Francois Auger, Patrick Flandrin, IEEE Transactions on Signal
   Processing, vol. 43, no. 5, May 1995
-  `Time–frequency reassignment: from principles to
   algorithms <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.331.5416&rep=rep1&type=pdf>`__
   - P. Flandrin, F. Auger, E. Chassande-Mottin, CRC Press 2003
-  `Time-frequency toolbox for Matlab, user’s guide and reference
   guide <http://iut-saint-nazaire.univ-nantes.fr/~auger/tftb.html>`__ -
   F.Auger, P.Flandrin, P.Goncalves, O.Lemoine

.. |PyPI version| image:: https://img.shields.io/pypi/v/tfr.svg
   :target: https://pypi.python.org/pypi/tfr
.. |Supported Python versions| image:: https://img.shields.io/pypi/pyversions/tfr.svg
.. |License| image:: https://img.shields.io/pypi/l/tfr.svg