AutoEq is a tool for equalizing headphone frequency responses automatically, and it achieves this by parsing frequency response measurements and producing equalization settings which correct the headphone to a neutral sound. AutoEq provides methods for reading data, equalizing it to a given target response and saving the results for usage with equalizers. It's possible to use different compensation (target) curves, apply tilt for making the headphones brighter/darker and adding a bass boost. It's even possible to make one headphone sound (roughly) like another headphone. For more info about usage see Usage.
AutoEq Github page also serves as a database for headphone frequency response measurements, pre-computed results and has documentation about different equalizers and how the implementation works.
Added --preamp parameter
Fixed README in PyPi package
Fixed dependencies for Apple Silicon and added --treble-boost parameter.
Restructured the project and published in PyPi. Source code moved under autoeq directory and
command line usage changed from python autoeq.py to python -m autoeq with underscores _ replaced with hyphens -
in the parameter names.
Parametric eq optimizer reworked. The new optimizer supports shelf filters, has a powerful configuration system, run 10x faster, has limits for Fc, Q and gain value ranges and treats +10 kHz range as average value instead of trying to fix it precisely.
AutoEq requires Python 3 and should work with any decently recent version of Python 3.
pip install autoeqYou may need to install libsndfile
On Windows you may need to install Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019
AutoEq has command line interface in addition to Python methods. See python -m autoeq --help for arguments.
The full functionality with file input and output can be used with batch_processing:
from autoeq.batch_processing import batch_processing
batch_processing(
input_dir='path/to/measurements', output_dir='path/to/results', new_only=False, standardize_input=False,
compensation='path/to/compensation.csv', parametric_eq=True, fixed_band_eq=True, rockbox=False,
ten_band_eq=True, parametric_eq_config='8_PEAKING_WITH_SHELVES', fixed_band_eq_config='10_BAND_GRAPHIC_EQ',
convolution_eq=True, fs=44100, bit_depth=16, phase='minimum', f_res=10, bass_boost_gain=6,
bass_boost_fc=105, bass_boost_q=0.7, treble_boost_gain=0, treble_boost_fc=10000, treble_boost_q=0.7, tilt=None,
sound_signature=None, max_gain=12, thread_count=0)The main functionalities of AutoEq are in frequency_response which implements FrequencyResponse class. Parametric
equalizer optimization and frequency response computations are implemented in peq.
from autoeq.frequency_response import FrequencyResponse
from autoeq.constants import PEQ_CONFIGS
harman_target = FrequencyResponse.read_from_csv('path/to/harman_over-ear_2018.csv')
fr = FrequencyResponse.read_from_csv('path/to/measurement.csv')
fr.interpolate() # Creates standard logarithmic sampling when no argument is passed
fr.center() # Centers the frequency response around 0 dB
fr.compensate(harman_target) # Creates target and error data for the FR
fr.smoothen_fractional_octave() # Smoothens the FR data and error
fr.equalize(concha_interference=True) # Creates equalization target
peqs = fr.optimize_parametric_eq(PEQ_CONFIGS['8_PEAKING_WITH_SHELVES'], 44100)
for filt in peqs[0].filters:
print(f'{filt.gain:.2f} db, {filt.fc:.2f} Hz, {filt.q:.2f} Q')