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earyx is a framework for developing and running psychoacoustic experiments. It is inspired by Psylab a Matlab implementation written by Martin Hansen.

Documentation is hosted on Read the docs

With earyx it is possible to create experiments in the most simple way. Currently N-AFC (numbered alternate-forced-choice) and Matching experiments are supported. For running the experiments there are several options. earyx offers a commandline text user interface, a browser based graphical UI and the possibility to hook in models or additional UI´s (no concrete model implemented yet).


There are several ways to install earyx. For now there is no official PyPi package you can simply install via pip. Notice that earyx uses Pysoundcard for playback which itself depends on the external C library Portaudio. Proper Pysoundcard installation is necessary in order to install earyx successfully. For users not so familiar with python and its package the following step by step description will guide you through the installation process depending on your platform. Maybe a more practical alternative could be sounddevice <>.

We recommend to install earyx with miniconda .

Ubuntu / OSX / Windows

If you don't have miniconda installed yet: Download and install miniconda from

Afterwards we recommend to create a virtual environment to install earyx in:

conda create -n earyx python=3
source activate earyx
pip install --upgrade pip
conda install numpy

Now download the earyx repository (therefore a git installation is required):

git clone
cd earyx
source activate earyx
pip install .

That's it. Try to run:

python (commandline)


python -u gui (gui)

If this works you have installed earyx sucessfully, nevertheless you will see some crazy messages in the terminal (using Ubuntu).

Philosophy of earyx

earyx can be used for a wide range of psychoacoustic experiments but not for all. For a simple decision whether earyx can handle the experiment you want to build, just answer the following questions.

  1. Is there exactly one value you are interested in finding a relative or absolute threshold for? - in earyx this value is called variable
  2. Is there at least one other value or value-set the threshold to be measured depends on? - in earyx this kind of dependencies are called parameters
  3. Do you plan to determine this threshold either with matching or in N-AFC manner?

In case you answered all of this questions with yes earyx will do the job! If not, don't claim. earyx has en extremly modular and extendible design. So feel free to read the API documentation to understand how to extend earyx for your needs.

Structure of an earyx experiment

To understand the overall structure of an earyx experiment it is necessary to understand the explanation of the following terms:


Parameters define different conditions in which an experiment is performed.


For this value a threshold is to be measured. Therefore it changes over time depending on the answers the user gives.

Adapt rule

The behaviour of variable change in relation to the user's answer and given by the adapt rule. For now, earyx supports 1up2down, 2up1down, 1up3down and weighted up down. In earyx the adapt rules are also parameters. That means, you can specify more than one adapt rule and the threshold is determined using all of them (interleaved or sequential).


Reaching a threshold is mainly controlled by reversals. Depending on your selected adapt rule a reversal occurs if the direction the variable changes its value reverses. Changing variable direction from up to down is called upper reversal changing from down to up lower reversal.

Step size - start and minimal step size

The step size defines how much the variable changes from trial to trial. If a reversal occurs (upper or lower) this step size is halved until the minimal step size is reached. Then the measurement phase starts.

Measurement phase

Each run is split into two phases. One phase before reaching the minimal step size and one after which is called measurement phase. Only variable values in this phase are used to determine the interesting threshold which is the median of this values.

Maximal reversals

This is the stop criterion for a run. When the number of reversals within the measurement phase reaches the maximum value the run is finished.

Now you are able to understand the two main building blocks of earyx: Runs and Trials.

A run is a combination formed from all given parameters and adapt rules. That means, the more parameters are specified, the more runs result. Within such a run the parameter settings are fixed and only the variable changes from presentation to presentation according to the selected adapt rule. One presentation is called trial.

Therefore in principle the overall structure is nearly the same for all earyx experiments. An experiment has at least one run and each of that runs has a number of trials necessary to determine the threshold.

How to write an experiment

For implementing an experiment you need to build an (when class called MyExperiment) file containing three methods.


Depending on your experiment choice you have to import AFCExperiment or MatchingExperiment from earyx.experiments and Sequential or Interleaved from earyx.order.

It is recommended to use given tools like gensin, rms and hanwin from earyx.utils and numpy for building your signals.

Example imports:

from earyx.experiments import AFCExperiment
from earyx.order import Sequential
from earyx.utils import gensin
import numpy as np

Your experiment needs to be a class inheriting from an experiment type and from an order of your choice.


class MyExperiment(AFCExperiment,Sequential):

The following methods have to be implemented:

init experiment

In this method all options and parameters of the experiment need to be implemented. At least one parameter with one value, a variable and an adapt setting are needed. There is no limit of parameters and their values furthermore of adapt settings. There is always just one variable, it will be overridden by later variable definitions.


def init_experiment(self, exp):
    exp.add_parameter("sine_frequenzy",[1000, 2000, 3000], "Hz")
    exp.add_parameter("duration",[1], "s")
    exp.set_variable("sine_level", -20, "dB")
    # optinal:
    exp.num_afc = 3 #set number of AFC, *default = 3*
    exp.sample_rate = 48000 # set sampling rate, *default = 48000*
    exp.calib = 0 #free to use for calibrate a specific system
    exp.task = "In which Interval do you hear the test tone? (1,2,3)"
    exp.debug = False #user will see the plot of the variable *default = False*
    exp.discard_unfinished_runs = False # *default = False* = True #user gets response wrong or rigth, *default = True*
    exp.visual_indicator = True #buttons blinking simultaneous to sound, *default = True*
    exp.description = """This is the description of the experiment"""
    exp.allow_debug = True #user is able to de/activate the debug plotting *default = True*
    exp.pre_signal = 0.3 # Check signal generation

init run

All data and signals which are valid for the whole run need to be implemented here.


def init_run(self, run):
    #Implementing a frozen noise for the whole **run**:
    run.reference_signal = np.random.randn(np.round(run.duration*run.sample_rate))

init trial

All data and signals which are valid for a single trial need to be implemented here.


def init_trial(self, trial):
    ramp_dur = 0.1
    sine_ampl = np.sqrt(2)*10**(trial.variable/20)
    trial.test_signal = gensin(trial.sine_frequenzy,sine_ampl,trial.duration)
    #Add Sine to the frozen noise:
    trial.test_signal += trial.reference_signal
    #Hanwin for test and reference signal:
    trial.test_signal = hanwin(trial.test_signal, np.round(ramp_dur*trial.sample_rate))
    trial.reference_signal = hanwin(trial.reference_signal, np.round(ramp_dur*trial.sample_rate))
    return trial

Complete Experiment

The following code is a complete experiment, which you can try out or use as sample for your own experiment.


from earyx.experiments import AFCExperiment
from earyx.order import Sequential
from earyx.utils import gensin, rms
import numpy as np
import earyx

class SineInNoise(AFCExperiment, Sequential):

    def init_experiment(self, exp):
        exp.add_parameter("noise_level", [-40, -60], 'dB')
        exp.set_variable("sine_level", -20, 'dB')
        exp.add_adapt_setting('1up2down', max_reversals = 8,
                             start_step = 5, min_step = 1)

        exp.num_afc = 3
        exp.discard_unfinished_runs = False
        exp.pre_signal = 0.3
        exp.between_signal = (0.3, 0)
        exp.post_signal = 0.2

        exp.description = """This is the description
        of the experiment"""

    def init_run(self, run):
        ref = np.random.randn(np.round(0.3*run.sample_rate))
        ref = ref/rms(ref)*10**(run.noise_level/20)
        run.reference_signal = ref

    def init_trial(self, trial):
        ampl = np.sqrt(2)*10**(trial.variable/20)
        test = gensin(1600, ampl, 0.03, trial.sample_rate)
        pos = np.round((len(trial.reference_signal) - len(test))/2)
        trial.test_signal = trial.reference_signal.copy()
        trial.test_signal[pos:pos+len(test)] += test

if __name__ == '__main__':

Signal generation

In earyx there are exactly five different signals with predefined names which are connected to the output signal.

  1. pre_signal
  2. between_signal
  3. post_signal
  4. reference_signal
  5. test_signal

Depending on the selected type of experiment (AFC or Matching) they are automatically prepared for playback. In case of a 3-AFC experiment one possible signal order could be:

pre | reference | between | test | between | reference | post

or in case of a 2-AFC or Matching experiment:

pre | reference | between | test | post

Signal inheritance

earyx provides a mechanism called signal inheritance which means, that signals will be inherited from stage to stage. Let's have a look at the three stages init_experiment, init_run and init_experiment. In each of them you have access to the five earyx signals as attributes. For example, in init_experiment you can set the pre_signal via exp.pre_signal = .... The other stages init_run and init_trial also have the attribute pre_signal and inherit the values from parent stages if already set. That means, run.pre_signal and trial.pre_signal will automatically set to the value of exp.pre_signal defined in init_experiment. This concept allows comfortable and memory efficent signal generation. Let's say, you want to design an experiment in which pre, between and post signal should be the same in all trials. Simply define them in init_experiment. Maybe your reference signal should be the same not for the whole experiment but in every trial of one run, then you can define your reference_signal in init_run. For more inspiration see the example experiments.

Mono, stereo, multichannel

In earyx the experiment creator is responsible for generating valid signals. That means, for automatic concatenation all signals must have the same shape. For simplicity we suggest to build your signals as numpy arrays with shape (SAMPLES, NUM_CHANNELS). In case all your signals are diotic (all channels are identical), there is one exception from this rule, because you can build your signals as numpy arrays either with shape (SAMPLES,) or (SAMPLES,1). Afterwards this single vector will be copied to all channels your playback system has.

Zero signals

In most cases pre_signal, between_signal and post_signal should be only zero signals of given length to control the pause duration between the presented test or reference signals. Therefore earyx provides a comfortable way to define them. To generate an zero signal with length 0.3s you can write:

pre_signal = 0.3 which is automatically transformed into np.zeros(0.3*sample_rate)

But there is also an exteded syntax to define multi channel zero signals.

pre_signal = (0.3,NUM_CHANNELS) leads to np.zeros((0.3*sample_rate, NUM_CHANNELS))

As you can see there are many ways to define proper signals in earyx. But to avoid some trouble always keep in mind: All your signals *must have* the same shape!


Calibration is a difficult topic in psychoacoustic experiments because there is no standarized way. In earyx the experiment creator has full control and responsibility to generate calibrated signals. earyx provides little help by offering the attribute calib aviable in the whole experiment (calib is equivalent to 0dB FS [dB SPL]). It is inherited in the same way like the signals, see Signal inheritance. You have full freedom how to use this variable for controlling your signals.


The invocation to start an experiment from terminal/command line is


You have to be in the same directory as the experiment file.

[-u]: Defines the user interface.
  • -u gui starts the experiment with a graphical user interface.
  • -u egui gives you an IP address with which the experiment can be performed on another device by calling that IP address in an internet browser. Your computer and the other device have to be in the same network for this!

If this flag is not set the default UI is a text based user interface (TUI).

[-a]: Defines the audio output.
  • -a 1: Sound from internet browser; default with -u gui
  • -a 2: Sound from internet browser and Python
  • -a 3: Sound from Python; default with no [-u] option and -u egui
[-l]: Opens a file dialog.
  • You can then load an unfinished experiment.

There is an earyx server that lets you perform several experiments at the same time on the same or on a different device. To start the server you have to be in YOUR_EARYX_PATH/earyx/. The invocation works as follows:


After this you will get an IP address that you type or paste in the URL line of your internet browser. If you use this with other devices you have to be in the same network. You can choose between all experiments, which are arrange in the folder experiments.

Here is an example how to load (-l) an unfinished experiment in a GUI (-u gui) and with audio output from Python (-a 3). Just to show how easy it is to use all flags at once:

python -u gui -a 3 -l


Both frameworks are developed at the Jade University of Applied Sciences Oldenburg in which the first version of earyx is the result of an one semester programming project. earyx is developed by Sven Hermann, Jonas Klug, Nils Schreiber, Matthias Stennes, Stephanus Volke and with the help of Bastian Bechtold.

TODOS (Contribute!)

If you want to contribute to this project, please fork and contribute!

We are sure that there are a lot of cool ideas to improve earyx! This is just the beginning! We are looking for your ideas! In our opinion the next steps to make are:

  • complete API documentation
  • earyx plotting engine for final results
    • at the moment it is not possible to plot thresholds or the like
    • at the moment the position is somehow random, so that it sometimes shows up and sometimes not (this differs from device to) device
  • add earyx logo to GUI
  • correct the display of the plotting window on mobile devices
  • change activate debugging button to a checkbox (in GUI)
  • write more testers

No warranty

earyx is distributed without any kind of warranty.

The use of earyx, for whatever purpose, is under the complete and sole responsibility of the user. Special attention is drawn to the fact that earyx does not comprise an automatic mechanism to prevent the delivery of too loud sound pressure levels. It is the explicit and sole responsibility of the user of earyx to make sure that the generated stimuli, in combination with any further equipment (sound card, amplifier, headphone or loudspeaker, etc.), will yield the desired sound level.


earyx is “free software” and is distributed in OpenSource format under the terms of the GNU General Public Licence (GPL). This means, amongst others, that copies of the earyx software may be distributed without asking for permission, given that the terms of the GNU GPL are obeyed to. For more details, see or look at a verbose copy of the GNU GPL in the root directory of earyx.


A framework for developing and running psychoacoustic experiments in Python, inspired by Psylab for MATLAB.




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