Using Python for research and acoustic signal processing

Transcription

1 processing Axel Plinge Pattern Recognition, Computer Science XII, TU Dortmund University October 2015 Bar-Ilan University

2 Motivation (1) processing 1/15 Why use Python? it s free cross platform (Windows, Linux, MacOS) clear syntax code is easy to read encapsulation of code in modules & packages alows for object oriented programing (OOP) powerful language functional programing: list comprehension, lambda,... object oriented programing iteration, slicing,... powerful libraries maintained by PhDs :-) vector and matrix operations (numpy) MATLAB-like plotting (matplotlib) probabilistic functions & models (scipy.stats) machine learing (sklearn,... ) signal processing (scipy.signal)...

3 Contents processing 2/15 Motivation Installations WinPython Eclipse IDE Audio Learning Introductions & Tutorials Documentation Examples Interactive Matrix Functions Module Audio Playback Audio Loopback

4 Axel Plinge processing 3/15 Motivation (2) Live Demo live acoustic event detection with visualization coded in Python

5 Installation: Python processing 4/15 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

6 Installation: IDE processing 5/15 Comfortable IDE Projects Debugging 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

7 Installation: Audio processing 6/15 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

8 Learning Python processing 7/15 Introductions Scipy Lectures introduction to Python Language Math with numpy Plotting with matplotlib Science with scipy Python for scientists MATLAB commands in Python Tutorials The Python tutorial Quickstart numpy scipy tutorial

9 Documentation processing 8/15 Python 2.7 numpy scipy matplotlib

10 acosh(...) Axel Plinge Interactive example >>> >>> import math >>> math.pi >>> math.sin(math.pi/2.0) 1.0 >>> help(math) Help on built-in module math: NAME math FILE (built-in) processing 9/15 DESCRIPTION This module is always available. It provides access to the mathematical functions defined by the C standard. FUNCTIONS acos(...) acos(x) Return the arc cosine (measured in radians) of x.

11 >>> import numpy as np >>> a=np.eye(3) >>> a array([[ 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]*5 array([[ 5., 0.], [ 0., 5.]]) >>> b=np.array([[1,2,3]]) >>> b.shape (1, 3) >>> a.shape (3, 3) >>> a*b array([[ 1., 0., 0.], [ 0., 2., 0.], [ 0., 0., 3.]]) Axel Plinge Matrix example processing 10/15

12 >>> def doubleit(x):... return x*2... Axel Plinge Function example processing 11/15 >>> doubleit(5) 10 >>> for i in range(3):... doubleit(i) >>> [doubleit(i) for i in range(3)] [0, 2, 4] >>> a=[doubleit(i) for i in range(3)] >>> for ai in a:... doubleit(ai)

13 Module example processing 12/15 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 += x print accumulator return accumulator use it from another file or interpreter import accumen as a a.accum(1) print a.accumulator

14 Playback example processing 13/15 import soundfile import sounddevice as sd data, fs = soundfile.read( sentence.wav ) sd.play(data,fs)

15 Loopback example processing 14/15 import numpy as np import sounddevice as sd DURATION = 10 # seconds SAMPLERATE = BLOCKSIZE = 4096 def callback(indata, outdata, frames, time, status): global thefilter if status: print(status) filtered = thefilter.process(indata) outdata[:,1] = filtered outdata[:,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()

16 import numpy as np import scipy Axel Plinge Overlapp-Add Filter processing 15/15 class OverlapAdd(object): def init (self,blocksize,gain): self.blocksize= blocksize self.gain = gain self.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.lastin self.lastin = indata[:,0] * self.gain self.inbuf[self.blocksize:] = self.lastin self.inbuf = self.inbuf * self.window self.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.overlap self.overlap = self.filtered[self.blocksize:] self.lastout = self.filtered[:self.blocksize] return self.lastout