#!/usr/bin/env python
# coding: utf-8
# # Binaural Room Impulse Responses (BRIRs)
#
# [return to main page](index.ipynb)
#
# In this unit we will measure - with the help of our dummy head Ulf - binaural room impulse
# responses (BRIRs) of our seminar room.
#
# 
#
# We will be using two different methods:
#
# * First, we excite the room - like in the [previous unit](rir.ipynb) - by clapping two wooden boards together.
# But this time, instead of using a single microphone, we will record the room response with the dummy head.
# We'll use the free audio recording/editing software [Audacity](http://web.audacityteam.org/) again.
#
# * Afterwards, we use the slightly more modern *sweep method*.
# We excite the room with a sine sweep, which we reproduce by means of a loudspeaker.
# The actual impulse response will be calculated from the excitation signal and the signal recorded by the dummy head.
#
# Further information will be provided during the exercises.
#
# If you cannot be with us for the measurements, you can still try the following exercises with these files (from older measurements):
#
# * using the wooden boards: [data/brir_clap.wav](data/brir_clap.wav)
#
# * using the sweep method: [data/brir_sweep.mat](data/brir_sweep.mat)
# ## Loading the BRIRs
#
# We already know from the previous units how to load WAV files, so the first one should be easy.
# Note, however, that now we are dealing with a two-channel file (one channel for each ear).
# The resulting NumPy array will be two-dimensional and it will contain the channels along the columns.
#
# *Exercise:* Load the WAV file with the BRIRs.
# Use the `shape` property of the resulting array to check if the dimensions/sizes are as you expect them.
# How long (in seconds) are the impulse responses?
# In[ ]:
# The impulse responses obtained with the sweep method were created with Matlab®.
# Along with some additional information they are stored in MAT files.
# Luckily, SciPy [can load these kinds of files](https://docs.scipy.org/doc/scipy/reference/io.html) with the [scipy.io.loadmat()](https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat) function (as long as a certain MAT-file-version is used).
# In[ ]:
# Make sure to use the options `struct_as_record=False` and `squeeze_me=True` when loading the MAT file.
#
# *Exercise:* Load the MAT file with the other BRIRs.
# How long (in seconds) are the impulse responses?
# In[ ]:
# The object returned by `scipy.io.loadmat()` is a bit strange ...
#
# It is like a `dict` object which has variable names as keys.
# In our case, there is only one variable named `data`, which you can access with
#
# data = mat_contents['data']
#
# The `data` variable is a Matlab "structure" whose attributes you can access with the well-known dot-notation (but only if you used the argument `struct_as_record=False` as suggested above!).
# Use tab-completion (or `dir(data)`) to find out which attributes are available.
#
# For us, the most interesting attribute is `data.ir`, which holds the actual BRIR data as a two-dimensional NumPy array.
# ## Listening to the BRIRs
#
# As we saw (or rather *heard*) in the [previous unit](rir.ipynb), listening to the impulse responses directly doesn't tell us very much, but let's do it anyway!
#
# *Exercise:* Listen to the impulse responses.
# Do you hear a difference?
#
# You should use `tools.normalize()` (from [tools.py](tools.py)) on both IRs before playback to adjust their volume.
# In[ ]:
# To get a clearer picture of the data, let's convolve the IRs with some signals!
#
# Note that in contrast to the previous unit, we now have to deal with two-channel impulse responses.
#
# We might want to use [scipy.signal.fftconvolve()](http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.fftconvolve.html)
# which can handle convolution along a desired axes.
#
# *Exercise:* Load a mono signal (e.g. from [data/xmas.wav](data/xmas.wav)) and convolve it with both BRIRs.
# Do you hear a difference?
#
# Use `tools.normalize()` on the results to be able to compare them with appropriate levels.
# In[ ]:
# ## Headphone Compensation
#
# There should be a clearly audible difference between the two measured BRIRs, right?
#
# But we can still make it better.
# One thing that's still missing is *headphone compensation*.
# Load the impulse response stored in the file
# [data/THOMSON_HED415N_KEMAR_hcomp.wav](data/THOMSON_HED415N_KEMAR_hcomp.wav) and convolve it with the measured impulse responses to apply the headphone compensation filter.
#
# *Exercise:* Listen to the BRIRs with and without headphone compensation (after convolving some input signal with them).
#
# In[ ]:
# ## Plotting the BRIRs
#
# *Exercise:* Plot all impulse responses which were used up to now.
# In[ ]:
# *Exercise:* Estimate the time-of-flight from $t = 0$ until the direct sound hits the ears.
# Which distance in meters does this correspond to?
# Is this consistent with the actual measurement setup?
# In[ ]:
# *Exercise:* Roughly estimate the signal-to-noise ratio of the measured impulse responses.
# Does it differ for the different measurement methods?
# In[ ]:
# *Exercise:* Plot the frequency response of the different measurements.
# Plot the frequency logarithmically on the x-axis and the magnitude in dB on the y-axis.
# In[ ]:
# ## Multiple Head Orientations
#
# At the beginning of this unit, we measured impulse responses for different head orientations.
#
# If you couldn't be with us for the measurements, you can use these example files:
#
# [data/brir_sweep-80.mat](data/brir_sweep-80.mat)
# [data/brir_sweep-40.mat](data/brir_sweep-40.mat)
# [data/brir_sweep.mat](data/brir_sweep.mat) (0 degree, same file as we used above)
# [data/brir_sweep+40.mat](data/brir_sweep+40.mat)
# [data/brir_sweep+80.mat](data/brir_sweep+80.mat)
#
# *Exercise:* Load all files, extract the BRIRs, convolve them with a mono signal and listen to the results.
# In[ ]:
# *Exercise:* Select either the left or the right ear and plot its impulse responses for each
# measured orientation.
# To do that, create a 2-dimensional array containing one impulse response per column.
# In[ ]:
# *Exercise:* Do the same thing with the magnitude spectra.
#
# Note: Try to add a legend to see which line corresponds to which measurement.
# In[ ]:
# ## Let's Watch a Video!
#
# This video is from this page: [how to create animations with matplotlib](http://nbviewer.jupyter.org/github/mgeier/python-audio/blob/master/plotting/matplotlib-animation.ipynb).
#
#
#
# *Exercise:* Try to understand what's shown there.
# What's the meaning of the angle on the right side?
#
# Note that the impulse responses in the video were measured in an anechoic
# chamber, therefore they look quite different compared to our measured BRIRs.
# ## Plotting Many Head Orientations
#
# First, have a quick look at this [public dataset](https://zenodo.org/record/55418#.YmK9actBxhE), of most interest might be the [pdf documentation](https://zenodo.org/record/55418/files/wierstorf2011_QU_KEMAR_anechoic.pdf).
#
# Then, download [QU_KEMAR_anechoic_2m.mat](https://zenodo.org/record/4459911/files/QU_KEMAR_anechoic_2m.mat).
#
# *Exercise:* Load the file and extract the variable called `irs`.
# In[ ]:
# *Exercise:* Plot all data from `irs.left` (or, if you prefer, `irs.right`) using [matplotlib.pyplot.pcolormesh()](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html) or [matplotlib.pyplot.imshow()](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html).
#
# You might see more if you first convert the data to dB.
#
# What can you recognize on the plot?
# Use the zoom feature to enlarge certain areas.
#
# Note that also in this case the data was measured in an anechoic chamber.
# In[ ]:
# *Exercise:* Try to find out what the axes show and label them accordingly.
# In[ ]:
# ## Solutions
#
# If you had problems solving some of the exercises, don't despair!
# Have a look at the [example solutions](brir-solutions.ipynb).
#
#
#
# 
#
#
# To the extent possible under law,
# the person who associated CC0
# with this work has waived all copyright and related or neighboring
# rights to this work.
#