AudioSignal Basics¶

The nussl.AudioSignal object is the main container for all things related to your audio data. It provides a lot of helpful utilities to make it easy to manipulate your audio. Because it is at the heart of all of the source separation algorithms in nussl, it is crucial to understand how it works. Here we provide a brief introduction to many common tasks.

Initialization from a file¶

It is easy to initialize an AudioSignal object by loading an audio file from a path

>>> from __future__ import division
>>> import nussl
>>> input_file_path = 'path/to/input.wav'
>>> signal1 = nussl.AudioSignal(input_file_path)


Now the AudioSignal object is ready with all of the information about our the signal.

>>> print("{} Hz".format(signal1.sample_rate))  # This is determined by our input file
44100 Hz
>>> signal1.num_channels
2
>>> print("{} seconds".format(signal1.signal_duration))
2 seconds
>>> print("{} samples".format(signal1.signal_length))  # Two seconds at 44.1 kHz means...
88200 samples
>>> signal1.file_name  # just get the file name
'input.wav'
>>> signal1.path_to_input_file  # this is the full path
'path/to/input.wav'


The actual signal data is in signal1.audio_data. It’s just a numpy array, so we can use it as such:

>>> signal1.audio_data
array([[  0.00000000e+00,   0.00000000e+00,   6.10351562e-05, ...,
2.47192383e-03,   1.04370117e-02,   1.83410645e-02],
[  0.00000000e+00,   0.00000000e+00,   6.10351562e-05, ...,
2.47192383e-03,   1.04370117e-02,   1.83410645e-02]], dtype=float32)
>>> signal1.audio_data.shape
(2, 88200)


A few things to note here:

1. When AudioSignal loads a file, it converts the data to floats between [-1, 1]
2. The number of channels is the first dimension, the number of samples is the second.

Initialization from a numpy array¶

Another common way to initialize an AudioSignal object is by passing in a numpy array. Let’s first make a single channel signal within a numpy array.

>>> sample_rate = 44100  # Hz
>>> dt = 1.0 / sample_rate
>>> dur = 2.0  # seconds
>>> freq = 5000  # Hz
>>> x = np.arange(0.0, dur, dt)
>>> x = np.sin(2 * np.pi * freq * x)


Cool! Now let’s put this into a new AudioSignal object.

>>> signal2 = nussl.AudioSignal(audio_data_array=x)
>>> len(x) == signal2.signal_length == len(signal2)  # These are all the same thing, right?
True
>>> signal2.rms()  # Root-mean-square of audio_data in signal2
0.70710678118654524
>>> signal2.time_vector  # This is a vector with timestamps at each sample
array([  0.00000000e+00,   2.26759941e-05,   4.53519881e-05, ...,
1.99995465e+00,   1.99997732e+00,   2.00000000e+00])
>>> signal2.audio_data_as_ints()  # Get signal2.audio_data as ints
array([[     0,  21418,  32419, ..., -27651, -32419, -21418]], dtype=int16)


Alright, alright, alright [1].

Other basic manipulations¶

If we want to add the audio data in these two signals, it’s simple. But there are some gotchas:

>>> signal3 = signal1 + signal2
Exception: Cannot add with two signals that have a different number of channels!


Uh oh! I guess it doesn’t make sense to add a stereo signal (signal1) and mono signal (signal2). But if we really want to add these two signals, we have a few options.

First, we can just get one of the channels like this:

>>> signal1.get_channel(0)


Another option we have is to we can make signal1 mono. nussl does this by simply averaging the two channels at every sample. We have to explicitly tell nussl that we are okay with to_mono() changing audio_data. We do that like this:

>>> signal1.to_mono(overwrite=True)


If we hadn’t set overwrite=True then to_mono() would just return a np array of mono-ed audio_data and not change the representation of signal1.audio_data. You will see this pattern come up again. In certain places, AudioSignal:’s default behavior is to overwrite its internal data, and in other places the default is to not overwrite data. See the reference pages for more info.

Now, we can add the two signals as before:

>>> signal3 = signal1 + signal2


No exceptions this time! Great! signal3 is now a new AudioSignal: object. We can similarly subtract two signals.

Let’s write this to a file:

>>> signal3.write_audio_to_file('path/to/output.wav')


Awesome! Now lets see how we can manipulate the audio in the frequency domain…

Footnotes

 [1] Here, signal2 has no value for file_name or path_to_input_file. They are None.