rpi-audio-levels

Description

Python binding allowing to retrieve audio levels by frequency bands given audio samples, ona raspberry pi (aka power spectrum).It uses the GPU FFT lib from Andrew Holme (see http://www.aholme.co.uk/GPU_FFT/Main.htm).

I compared implementations using cython and ctypes, and the ctypes solution was slower(overhead due to the input data transformation for the C call).

In my case it's 7 times faster than using pure python and Numpy.

Dependencies

The GPU FFT lib sources which can be found here http://www.aholme.co.uk/GPU_FFT/Main.htm.It is also directly in raspbian.You will also need Cython.

Installation

$ python setup.py build_ext --inplace

This generates the rpi_audio_levels.so, be sure to add its directory to the PYTHONPATH(or install it using sudo python setup.py install instead of the above command).

Usage

from rpi_audio_levels import AudioLevels# We will give chunks of 2**11 audio samplesDATA_SIZE = 11# We want to extract levels for 6 frequency bandsBANDS_COUNT = 6# Preliminary call to prepare thingsaudio_levels = AudioLevels(DATA_SIZE, BANDS_COUNT)# Example of 6 arbitrary frequency bands. Indexes must be between 0 and 2**(DATA_SIZE - 1).bands_indexes = [[0,100], [100,200], [200,600], [600,700], [700,800], [800,1024]]# (Here the indexes come from nowhere, but you could have some algorithm to convert# start/end frequencies of each band you want to their corresponding indexes within# the FFT data)# Then retrieve audio levels each time you have new data.levels, means, stds = audio_levels.compute(data, bands_indexes)

The returned value is a tuple of tuple:

• levels: power spectrum levels for each frequency band
• means: means for each band
• stds: standard deviation for each band

data must be a numpy array of 2**DATA_SIZE real data with float dtype (np.float32),with only 1 dimension.You may want to preliminary multiply your audio data chunks with a hanning window (for example)before providing them to the compute() method.