tfr 0.2.4

Description:

tfr 0.2.4

Spectral audio feature extraction using time-frequency
reassignment.
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 (aka pitch
class profiles) where pitch is transformed into pitch class by ignoring
the octave. See also 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
or LinkedIn. Or just
say hello :).



Literature

A Unified Theory of Time-Frequency
Reassignment - Kelly R. Fitz,
Sean A. Fulop, Digital Signal Processing 30 September 2005
Algorithms for computing the time-corrected instantaneous frequency
(reassigned) spectrogram, with
applications
- Sean A. Fulop, Kelly Fitz, Journal of Acoustical Society of
America, Jan 2006
Time Frequency Reassignment: A Review and
Analysis
- 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
- Francois Auger, Patrick Flandrin, IEEE Transactions on Signal
Processing, vol. 43, no. 5, May 1995
Time–frequency reassignment: from principles to
algorithms
- P. Flandrin, F. Auger, E. Chassande-Mottin, CRC Press 2003
Time-frequency toolbox for Matlab, user’s guide and reference
guide -
F.Auger, P.Flandrin, P.Goncalves, O.Lemoine