#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy, scipy, matplotlib.pyplot as plt, pandas, librosa


# # Using Audio in IPython

# ## Audio Libraries

# We will mainly use three libraries for audio acquisition and playback: 

# ### 1. IPython.display.Audio
# ### 2. librosa
# ### 3. essentia.standard

# Introduced in IPython 2.0, [`IPython.display.Audio`](http://ipython.org/ipython-doc/stable/api/generated/IPython.display.html#IPython.display.Audio) lets you play audio directly in an IPython notebook.

# [`librosa`](http://bmcfee.github.io/librosa/) is a Python package for music and audio processing by [Brian McFee](http://cosmal.ucsd.edu/~bmcfee/). A large portion was ported from [Dan Ellis's Matlab audio processing examples](http://www.ee.columbia.edu/%7Edpwe/resources/matlab/).
# 
# - [Documentation Home](http://bmcfee.github.io/librosa/)
# - [Demo: Getting Started](http://nbviewer.ipython.org/github/bmcfee/librosa/blob/master/examples/LibROSA%20demo.ipynb)
# - [librosa on Github](https://github.com/bmcfee/librosa/)

# [Essentia](http://essentia.upf.edu) is an open-source library for audio analysis and music information retrieval 
# from the [Music Technology Group at Universitat Pompeu Fabra](http://mtg.upf.edu/home). 
# Although Essentia is written in C++, we will use the Python bindings for Essentia.
# 
# - [Documentation Home](http://essentia.upf.edu/documentation/)
# - [Python Tutorial](http://essentia.upf.edu/documentation/python_tutorial.html)
# - [Essentia on GitHub](https://github.com/MTG/essentia)

# ## Retrieving Audio

# To download a file onto your local machine (or Vagrant box) in Python, you can use `urllib.urlretrieve`:

# In[2]:


import urllib
urllib.urlretrieve(
    'http://audio.musicinformationretrieval.com/simpleLoop.wav', 
    filename='simpleLoop.wav'
)


# To check that the file downloaded successfully, list the files in the working directory:

# In[3]:


get_ipython().run_line_magic('ls', '*.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](#Playing-Audio).)

# ## Reading Audio

# ### `librosa.load`

# In[4]:


x, fs = librosa.load('simpleLoop.wav')
print x.shape
print fs


# In[5]:


plt.plot(x)


# ### `essentia.standard.Monoloader`

# [`MonoLoader`](http://essentia.upf.edu/documentation/reference/std_MonoLoader.html) 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):

# In[6]:


from essentia.standard import MonoLoader
audio = MonoLoader(filename='simpleLoop.wav')()
audio.shape


# In[7]:


N = len(audio)
t = numpy.arange(0, N)/44100.0
plt.plot(t, audio)
plt.xlabel('Time (seconds)')


# For more control over the audio acquisition process, you may want to use [`AudioLoader`](http://essentia.upf.edu/documentation/reference/std_AudioLoader.html) instead.

# ## Playing Audio

# ### `IPython.display.Audio`

# Using [`IPython.display.Audio`](http://ipython.org/ipython-doc/2/api/generated/IPython.lib.display.html#IPython.lib.display.Audio), you can play a local audio file or a remote audio file:

# In[8]:


from IPython.display import Audio
# load a remote WAV file
Audio('https://ccrma.stanford.edu/workshops/mir2014/audio/CongaGroove-mono.wav')


# In[9]:


# load a local WAV file
Audio('simpleLoop.wav')  


# `Audio` can also accept a NumPy array:

# In[10]:


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

# load a NumPy array
Audio(x, rate=fs)


# ### SoX

# To play or record audio from the command line, we recommend SoX (included in the `stanford-mir` Vagrant box).

#     $ rec test.wav
#     
#     $ play test.wav

# ## Visualizing Audio

# `plot` is the simplest way to plot time-domain signals:

# In[11]:


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 a sine wave
plt.plot(t, x)
plt.xlabel('Time (seconds)')


# `specgram` is a Matplotlib tool for computing and displaying spectrograms.

# In[12]:


S, freqs, bins, im = plt.specgram(x, NFFT=1024, Fs=fs, noverlap=512)

# Plot a spectrogram
plt.xlabel('Time')
plt.ylabel('Frequency')


# ## Writing Audio

# ### `librosa.output.write_wav`

# `librosa.output.write_wav` also saves a NumPy array to a WAV file. This is a bit easier to use.

# In[13]:


noise = 0.1*scipy.randn(44100)

# Write an array to a wav file
librosa.output.write_wav('noise2.wav', noise, 44100)
get_ipython().run_line_magic('ls', '*.wav')