We will mainly use three libraries for audio acquisition and playback: IPython.display.Audio
, essentia.standard
, and librosa
.
Introduced in IPython 2.0, IPython.display.Audio
lets you play audio directly in an IPython notebook.
Essentia is an open-source library for audio analysis and music information retrieval from the Music Technology Group at Universitat Pompeu Fabra. Although Essentia is written in C++, we will use the Python bindings for Essentia.
librosa
is a Python package for music and audio processing by Brian McFee. A large portion was ported from Dan Ellis's Matlab audio processing examples.
To download a file onto your local machine (or Vagrant box) in Python, you can use urllib.urlretrieve
:
import urllib
urllib.urlretrieve('https://ccrma.stanford.edu/workshops/mir2014/audio/simpleLoop.wav', filename='simpleLoop.wav')
('simpleLoop.wav', <httplib.HTTPMessage instance at 0x3629b48>)
To check that the file downloaded successfully, list the files in the working directory:
%ls *.wav
default.wav out.wav simpleLoop.wav
Visit https://ccrma.stanford.edu/workshops/mir2014/audio/ for more audio files.
If you only want to listen to, and not manipulate, a remote audio file, use IPython.display.Audio
instead. (See Playing Audio.)
essentia.standard.Monoloader
¶MonoLoader
reads (and downmixes, if necessary) an audio file into a single channel (as will often be the case during this workshop). MonoLoader
also resamples the audio to a sampling frequency of your choice (default = 44100 Hz):
from essentia.standard import MonoLoader
audio = MonoLoader(filename='simpleLoop.wav')()
audio.shape
(132300,)
N = len(audio)
t = arange(0, N)/44100.0
plot(t, audio)
xlabel('Time (seconds)')
<matplotlib.text.Text at 0x40f4bd0>
For more control over the audio acquisition process, you may want to use AudioLoader
instead.
librosa.load
¶import librosa
x, fs = librosa.load('simpleLoop.wav')
print x.shape
print fs
(66150,) 22050
IPython.display.Audio
¶Using IPython.display.Audio
, you can play a local audio file or a remote audio file:
from IPython.display import Audio
Audio('https://ccrma.stanford.edu/workshops/mir2014/audio/CongaGroove-mono.wav') # remote WAV file
Audio('simpleLoop.wav') # local WAV file
Audio
can also accept a NumPy array:
fs = 44100 # sampling frequency
T = 1.5 # seconds
t = numpy.linspace(0, T, int(T*fs), endpoint=False) # time variable
x = numpy.sin(2*numpy.pi*440*t) # pure sine wave at 440 Hz
Audio(x, rate=fs)
To play audio from the command line, we recommend SoX (included in the stanford-mir
Vagrant box).
$ play simpleLoop.wav
plot
is the simplest way to plot time-domain signals:
T = 0.001 # seconds
fs = 44100 # sampling frequency
t = numpy.linspace(0, T, int(T*fs), endpoint=False) # time variable
x = numpy.sin(2*numpy.pi*3000*t)
plot(t, x)
xlabel('Time (seconds)')
<matplotlib.text.Text at 0x5810cd0>
specgram
is a Matplotlib tool for computing and displaying spectrograms.
S, freqs, bins, im = specgram(x, NFFT=1024, Fs=fs, noverlap=512)
xlabel('Time')
ylabel('Frequency')
<matplotlib.text.Text at 0x5f4f4d0>
essentia.standard.MonoWriter
¶MonoWriter
can write a NumPy array to a WAV file. Note: the array must have type int
, single
, or complex64
.
from essentia.standard import MonoWriter
noise = 0.1*randn(44100)
MonoWriter(filename='noise1.wav')(single(noise))
%ls *.wav
default.wav noise1.wav out.wav simpleLoop.wav
librosa.output.write_wav
¶librosa.output.write_wav
also saves a NumPy array to a WAV file. This is a bit easier to use.
import librosa
noise = 0.1*randn(44100)
librosa.output.write_wav('noise2.wav', noise, 44100)
%ls *.wav
default.wav noise1.wav noise2.wav out.wav simpleLoop.wav