Using Python for research and acoustic signal processing

Axel Plinge

Pattern Recognition, Computer Science XII,TU Dortmund University

October 2015

Bar-Ilan University

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Motivation (1)

Why use Python?

I it’s free

I cross platform (Windows, Linux, MacOS)I clear syntax

I code is easy to readI encapsulation of code in modules & packagesI alows for object oriented programing (OOP)

I powerful languageI functional programing: list comprehension, lambda, . . .I object oriented programingI iteration, slicing, . . .

I powerful libraries – maintained by PhDs :-)I vector and matrix operations (numpy)I MATLAB-like plotting (matplotlib)I probabilistic functions & models (scipy.stats)I machine learing (sklearn, . . . )I signal processing (scipy.signal)I . . .

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Contents

I MotivationI Installations

I WinPythonI Eclipse IDEI Audio

I LearningI Introductions & TutorialsI Documentation

I ExamplesI InteractiveI MatrixI FunctionsI ModuleI Audio PlaybackI Audio Loopback

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Motivation (2) Live Demo

live acoustic event detection with visualization coded in Python

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Installation: Python

I Python installation

+ pre-bulid scientific libraries

+ Qt GUI framework

Hints

1. Python 2.7 recommended, as still more libraries support 2.7 than 3.x

2. Download and unzip to Program Files/WinPython

3. Register using the control panel

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Installation: IDE

I Comfortable IDE

I Projects

I Debugging

I Version Control integration

Setup

1. Install Java Runtime (JRE) if necessary

2. Download and install Eclipse (current version is ‘Mars’)

3. Go to Help > Install new software and add http://pydev.org/updates

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Installation: Audio

/ Audio support from scipy is still rudimentary

, Several good projects exists..

Setup

1. open the WinPython console

2. pip install sounddevice --user

3. pip install pysoundfile --user

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Learning Python

Introductions

I Scipy Lectures introduction toI Python LanguageI Math with numpyI Plotting with matplotlibI Science with scipy

I Python for scientists

I MATLAB commands in Python

Tutorials

I The Python tutorial

I Quickstart numpy

I scipy tutorial

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Documentation

I Python 2.7

I numpy

I scipy

I matplotlib

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Interactive example>>> 1+12>>> import math>>> math.pi3.141592653589793>>> math.sin(math.pi/2.0)1.0>>> help(math)Help on built-in module math:

NAMEmath

FILE(built-in)

DESCRIPTIONThis module is always available. It provides access to themathematical functions defined by the C standard.

FUNCTIONSacos(...)

acos(x)Return the arc cosine (measured in radians) of x.

acosh(...)acosh(x)Return the inverse hyperbolic cosine of x.

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Matrix example

>>> import numpy as np>>> a=np.eye(3)>>> aarray([[ 1., 0., 0.],

[ 0., 1., 0.],[ 0., 0., 1.]])

>>> a[:,2]array([ 0., 0., 1.])>>> a[:,:2]array([[ 1., 0.],

[ 0., 1.],[ 0., 0.]])

>>> a[:2,:2]*5array([[ 5., 0.],

[ 0., 5.]])>>> b=np.array([[1,2,3]])>>> b.shape(1, 3)>>> a.shape(3, 3)>>> a*barray([[ 1., 0., 0.],

[ 0., 2., 0.],[ 0., 0., 3.]])

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Function example

>>> def doubleit(x):... return x*2...

>>> doubleit(5)10

>>> for i in range(3):... doubleit(i)...024

>>> [doubleit(i) for i in range(3)][0, 2, 4]

>>> a=[doubleit(i) for i in range(3)]>>> for ai in a:... doubleit(ai)...048

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Module example

A module is a file, it can contain variables, functions and classes

accumen.py’’’ variable is global within this module ’’’accumulator = 0

’’’ define function, global within this module ’’’def accum(x):

’’’ use the global variable ’’’global accumulator

accumulator += xprint accumulatorreturn accumulator

use it from another file or interpreterimport accumen as a

a.accum(1)print a.accumulator

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Playback example

import soundfileimport sounddevice as sd

data, fs = soundfile.read(’sentence.wav’)sd.play(data,fs)

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Loopback example

import numpy as npimport sounddevice as sdDURATION = 10 # secondsSAMPLERATE = 48000BLOCKSIZE = 4096

def callback(indata, outdata, frames, time, status):global theFilterif status:

print(status)filtered = theFilter.process(indata)outdata[:,1] = filteredoutdata[:,0] = filtered

ios = sd.Stream(samplerate=SAMPLERATE, channels=2, callback=callback,dtype=np.int16, blocksize=BLOCKSIZE)

ios.start()for i in xrange(10*DURATION):

sd.sleep(100)ios.stop()

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Overlapp-Add Filterimport numpy as npimport scipy

class OverlapAdd(object):def __init__(self,blocksize,gain):

self.blocksize= blocksizeself.gain = gainself.window = scipy.hanning(blocksize*2)self.lastin = np.zeros((blocksize),dtype=np.int16)self.overlap = np.zeros((blocksize),dtype=np.int16)self.lastout = np.zeros((blocksize),dtype=np.int16)self.inbuf = np.zeros((2*blocksize),dtype=np.int16)

def process(self,indata):self.inbuf[:self.blocksize] = self.lastinself.lastin = indata[:,0] * self.gainself.inbuf[self.blocksize:] = self.lastinself.inbuf = self.inbuf * self.windowself.filtered = scipy.fft(self.inbuf)self.filtered = self.stft_filter(self.filtered)self.filtered = scipy.real(scipy.ifft(self.filtered))self.filtered[:self.blocksize] += self.overlapself.overlap = self.filtered[self.blocksize:]self.lastout = self.filtered[:self.blocksize]return self.lastout