Inner ear models for Python

Overview

cochlea

cochlea is a collection of inner ear models. All models are easily accessible as Python functions. They take sound signal as input and return spike trains of the auditory nerve fibers:

                         +-----------+     __|______|______|____
 .-.     .-.     .-.     |           |-->  _|________|______|___
/   \   /   \   /   \ -->|  Cochlea  |-->  ___|______|____|_____
     '-'     '-'         |           |-->  __|______|______|____
                         +-----------+
          Sound                               Spike Trains
                                            (Auditory Nerve)

The package contains state-of-the-art biophysical models, which give realistic approximation of the auditory nerve activity.

The models are implemented using the original code from their authors whenever possible. Therefore, they return the same results as the original models. We made an effort to verify it with unit testing (see tests directory for details).

The implementation is also fast. It is easy to generate responses of hundreds or even thousands of auditory nerve fibers (ANFs). It is possible, for example, to generate responses of the whole human auditory nerve (around 30,000 ANFs). We usually tested the models with sounds up to 1 second in duration.

I developed cochlea during my PhD in the group of Werner Hemmert (Bio-Inspired Information Processing) at the TUM. It went through several versions and rewrites. Now, it is quite stable and we decided to release it for the community.

Features

  • State of the art inner ear models accessible from Python.
  • Contains full biophysical inner ear models: sound in, spikes out.
  • Fast; can generate thousands of spike trains.
  • Interoperability with neuron simulation software such as NEURON and Brian.

Implemented Models

  • Holmberg, M. (2007). Speech Encoding in the Human Auditory Periphery: Modeling and Quantitative Assessment by Means of Automatic Speech Recognition. PhD thesis, Technical University Darmstadt.
  • Zilany, M. S., Bruce, I. C., Nelson, P. C., & Carney, L. H. (2009). A phenomenological model of the synapse between the inner hair cell and auditory nerve: long-term adaptation with power-law dynamics. The Journal of the Acoustical Society of America, 126(5), 2390-2412.
  • Zilany, M. S., Bruce, I. C., & Carney, L. H. (2014). Updated parameters and expanded simulation options for a model of the auditory periphery. The Journal of the Acoustical Society of America, 135(1), 283-286.
  • MATLAB Auditory Periphery by Meddis et al. (external model, not implemented in the package, but easily accessible through matlab_wrapper).

Usage

Check our online DEMO and examples (probably the easiest is to start with run_zilany2014.py).

Initialize the modules:

import cochlea
import thorns as th
import thorns.waves as wv

Generate sound:

fs = 100e3
sound = wv.ramped_tone(
    fs=fs,
    freq=1000,
    duration=0.1,
    dbspl=50
)

Run the model (responses of 200 cat HSR fibers):

anf_trains = cochlea.run_zilany2014(
    sound,
    fs,
    anf_num=(200,0,0),
    cf=1000,
    seed=0,
    species='cat'
)

Plot the results:

th.plot_raster(anf_trains)
th.show()

You can browse through the API documentation at: https://pythonhosted.org/cochlea/

Installation

pip install cochlea

Check INSTALL.rst for details.

Spike Train Format

Spike train data format is based on a standard DataFrame format from the excellent pandas library. Spike trains and their meta data are stored in DataFrame, where each row corresponds to a single neuron:

index duration type cf spikes
0 0.15 hsr 8000 [0.00243, 0.00414, 0.00715, 0.01089, 0.01358, ...
1 0.15 hsr 8000 [0.00325, 0.01234, 0.0203, 0.02295, 0.0268, 0....
2 0.15 hsr 8000 [0.00277, 0.00594, 0.01104, 0.01387, 0.0234, 0...
3 0.15 hsr 8000 [0.00311, 0.00563, 0.00971, 0.0133, 0.0177, 0....
4 0.15 hsr 8000 [0.00283, 0.00469, 0.00929, 0.01099, 0.01779, ...
5 0.15 hsr 8000 [0.00352, 0.00781, 0.01138, 0.02166, 0.02575, ...
6 0.15 hsr 8000 [0.00395, 0.00651, 0.00984, 0.0157, 0.02209, 0...
7 0.15 hsr 8000 [0.00385, 0.009, 0.01537, 0.02114, 0.02377, 0....

The column 'spikes' is the most important and stores an array with spike times (time stamps) in seconds for every action potential. The column 'duration' is the duration of the sound. The column 'cf' is the characteristic frequency (CF) of the fiber. The column 'type' tells us what auditory nerve fiber generated the spike train. 'hsr' is for high-spontaneous rate fiber, 'msr' and 'lsr' for medium- and low-spontaneous rate fibers.

Advantages of the format:

  • easy addition of new meta data,

  • efficient grouping and filtering of trains using DataFrame functionality,

  • export to MATLAB struct array through mat files:

    scipy.io.savemat(
        "spikes.mat",
        {'spike_trains': spike_trains.to_records()}
    )
    

The library thorns has more information and functions to manipulate spike trains.

Contribute & Support

Similar Projects

Citing

Rudnicki M., Schoppe O., Isik M., Völk F. and Hemmert W. (2015). Modeling auditory coding: from sound to spikes. Cell and Tissue Research, Springer Nature, 361, pp. 159—175. doi:10.1007/s00441-015-2202-z https://link.springer.com/article/10.1007/s00441-015-2202-z

BibTeX entry:

@Article{Rudnicki2015,
  author    = {Marek Rudnicki and Oliver Schoppe and Michael Isik and Florian Völk and Werner Hemmert},
  title     = {Modeling auditory coding: from sound to spikes},
  journal   = {Cell and Tissue Research},
  year      = {2015},
  volume    = {361},
  number    = {1},
  pages     = {159--175},
  month     = {jun},
  doi       = {10.1007/s00441-015-2202-z},
  publisher = {Springer Nature},
}

Do not forget to cite the original authors of the models as listed in Implemented Models.

Acknowledgments

We would like to thank Muhammad S.A. Zilany, Ian C. Bruce and Laurel H. Carney for developing inner ear models and allowing us to use their code in cochlea.

Thanks goes to Marcus Holmberg, who developed the traveling wave based model. His work was supported by the General Federal Ministry of Education and Research within the Munich Bernstein Center for Computational Neuroscience (reference No. 01GQ0441, 01GQ0443 and 01GQ1004B).

We are grateful to Ray Meddis for support with the Matlab Auditory Periphery model.

And last, but not least, I would like to thank Werner Hemmert for supervising my PhD. The thesis entitled Computer models of acoustical and electrical stimulation of neurons in the auditory system can be found at https://mediatum.ub.tum.de/1445042

This work was supported by the General Federal Ministry of Education and Research within the Munich Bernstein Center for Computational Neuroscience (reference No. 01GQ0441 and 01GQ1004B) and the German Research Foundation Foundation's Priority Program PP 1608 Ultrafast and temporally precise information processing: Normal and dysfunctional hearing.

License

The project is licensed under the GNU General Public License v3 or later (GPLv3+).

Comments
  • Problems importing _pycat?

    Problems importing _pycat?

    First, thanks for this! I saw the announcement come across the auditory list, and have gotten the time to check it out. (Greetings from BU!)

    I'm running into what is probably a configuration issue, so my apologies for what may be a stupid question.

    I've set up anaconda 32-bit on a windows 7 x64 box, running python 2.7.x. I'm running cochlea under the debugger, using PyCharm as my IDE. I've cloned both cochlea and thorns, and they reside in C:/Projects/cochlea

    Running examples\run_zilany2014.py results in the following:

    "C:\Users\gvoysey\Anaconda\python.exe" C:/Projects/cochlea/cochlea/examples/run_zilany2014_rate.py
    Traceback (most recent call last):
    File "C:/Projects/cochlea/cochlea/examples/run_zilany2014_rate.py", line 42, in <module>
         import cochlea
    File "C:\Projects\cochlea\cochlea\cochlea\__init__.py", line 30, in <module>
    from cochlea.zilany2009 import run_zilany2009
    File "C:\Projects\cochlea\cochlea\cochlea\zilany2009\__init__.py", line 28, in <module>
    from . import _pycat
    ImportError: cannot import name _pycat
    

    I'm not sure why this may be. Any thoughts?

    opened by gvoysey 10
  • BUG: Windows 10 installation error

    BUG: Windows 10 installation error

    Hi there,

    I am am trying to install with standard pip command and getting an error that Microsoft Visual C++ 14.0 is required. This seems like an issue for two reasons: I have MS Build Tools installed (version 15), and from the installation instructions it sounds like I should be able to get binaries and there shouldn't be any need to build anyway.

    Thanks for any guidance (and for maintaining this extremely useful tool!).

    opened by rkmaddox 7
  • ffGn function bug?

    ffGn function bug?

    I am attempting to write a high speed version of the Zilany 2014 model. I noticed an inconsistency in the /cochlea/zilany2014/utils.py file.

    The ffGn function calculates the fGN @ line 73 for H == 0.5. However, the return statement (which returns y) is only present in the else statement. In the case of (H == 0.5), the ffGn function would not return an array?

    Although, digging through the code it seems that (in both the cochlea and original Zilany model) H will be hardcoded at 0.9 for this simulation.

    Hope this helps.

    --Nas

    opened by nasiryahm 5
  • examples/stats_tuning example does not work

    examples/stats_tuning example does not work

    I have installed cochlea on Ubuntu and trying to run stats_tuning.py. This gives a strange error as follows. I havn't changed any code. ERROR STACK - Traceback (most recent call last): File "stats_tuning.py", line 41, in main() File "stats_tuning.py", line 24, in main model_pars={'species': 'human'} File "/usr/local/lib/python3.6/dist-packages/cochlea/stats/tuning.py", line 53, in calc_tuning model_pars=model_pars File "/usr/local/lib/python3.6/dist-packages/thorns/util/maps.py", line 387, in wrap result = func(**kwargs) File "/usr/local/lib/python3.6/dist-packages/cochlea/stats/threshold_rate.py", line 88, in calc_spont_threshold silence = np.zeros(fs*tmax) TypeError: 'float' object cannot be interpreted as an integer

    opened by tokekark 3
  • example does not work

    example does not work

    I installed cochlea-master on Windows 8 with Anaconda(python2.7 64-bit). It works successfully in jupyter notebook to processing a generated sound as shown in usage website. Then I am trying to run the example by using a sound file but there is something wrong.

    C:\Users\Alice\Desktop\cochlea-master\cochlea-master\scripts>python run_zilany2014 --hsr=100 --msr=75 --lsr=25 --cf=1000 --species=human --seed=0 --dbspl=60 tone.wav Processing tone.wav Traceback (most recent call last): File "run_zilany2014", line 162, in main(args) File "run_zilany2014", line 155, in main space File "run_zilany2014", line 108, in convert_sound_to_mat_unpack convert_sound_to_mat(**args) File "run_zilany2014", line 72, in convert_sound_to_mat sound_raw = wv.resample(sound_raw, int(f.samplerate), int(fs)) File "C:\ProgramData\Anaconda3\envs\py27\lib\site-packages\thorns\waves.py", l ine 100, in resample new_signal = dsp.resample(signal, len(signal)*new_fs/fs) File "C:\ProgramData\Anaconda3\envs\py27\lib\site-packages\scipy\signal\signal tools.py", line 2203, in resample Y = zeros(newshape, 'D') TypeError: 'float' object cannot be interpreted as an index

    I tried to transform the datatype into int

    waves.py 100: new_signal = dsp.resample(signal, len(signal)*new_fs/fs) replaced by: new_signal = dsp.resample(signal, int(len(signal)*new_fs/fs))

    but then there were some other asserting problems. (like "assert sound.ndim == 1". I checked my data, in which the sound.ndim=2).

    I am new to python and I 'm not sure why this may be. Could you help me with it?

    opened by xiaokebubu 2
  • AttributeError: module 'cochlea.stats' has no attribute 'calc_rate_intensity'

    AttributeError: module 'cochlea.stats' has no attribute 'calc_rate_intensity'

    https://github.com/mrkrd/cochlea/blob/f4f9734f07a6792eac14d10eae0dd30224209bb8/examples/cochlea_demo.ipynb?short_path=b9f3970#L256

    Call 'calc_rate_level' instead to resolve the issue

    cochlea==2

    opened by SchraivogelS 1
  • human group delay

    human group delay

    Dear all, First, thanks for sharing your code. I'm using it and it is really neat! I have been using the model available via pip but I have also been looking at your code here. What is not clear to me is why for the human_group_delay the beta value from Harte et al. 2009 is being divided by 2. I can see in the code that the human_group delay is not being used (cat instead) but if this happens, then the beta value needs to be corrected. Could I kindly ask you what group_delay is implemented in the available code in pip repository? Kind regards, Jaime

    opened by jundurraga 1
  • AttributeError: module 'numpy.fft' has no attribute 'fftpack'

    AttributeError: module 'numpy.fft' has no attribute 'fftpack'

    https://github.com/mrkrd/cochlea/blob/f4f9734f07a6792eac14d10eae0dd30224209bb8/cochlea/zilany2014/init.py#L120

    Removal of if statement resolves the issue

    Python 3.9.7 cochlea==2 numpy==1.22.2

    opened by SchraivogelS 0
  • Cannot run demo, getting float>integer issue

    Cannot run demo, getting float>integer issue

    I'm having an issue running the demos. I'm running the demo in a Jupyter Notebook, but when I get to generating a tone, I get this error, which seems to be coming from numpy's ramped_tone() function. Any chance you know how to get around this?

    ` TypeError Traceback (most recent call last) in 1 fs = 100e3 2 cf = 1000 ----> 3 tone = wv.ramped_tone( 4 fs=fs, 5 freq=1000,

    /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/thorns/waves.py in ramped_tone(fs, freq, duration, pad, pre, ramp, dbspl, phase) 169 170 if ramp != 0: --> 171 ramp_signal = np.linspace(0, 1, np.ceil(ramp * fs)) 172 s[0:len(ramp_signal)] = s[0:len(ramp_signal)] * ramp_signal 173 s[-len(ramp_signal):] = s[-len(ramp_signal):] * ramp_signal[::-1]

    /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/overrides.py in linspace(*args, **kwargs)

    /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy/core/function_base.py in linspace(start, stop, num, endpoint, retstep, dtype, axis) 118 119 """ --> 120 num = operator.index(num) 121 if num < 0: 122 raise ValueError("Number of samples, %s, must be non-negative." % num)

    TypeError: 'numpy.float64' object cannot be interpreted as an integer `

    opened by mbrown0294 0
Releases(v1.2.4)
In this project we can see how we can generate automatic music using character RNN.

Automatic Music Genaration Table of Contents Project Description Approach towards the problem Limitations Libraries Used Summary Applications Referenc

Pronay Ghosh 2 May 27, 2022
Spotifyd - An open source Spotify client running as a UNIX daemon.

Spotifyd An open source Spotify client running as a UNIX daemon. Spotifyd streams music just like the official client, but is more lightweight and sup

8.5k Jan 09, 2023
Voice to Text using Raspberry Pi

This module will help to convert your voice (speech) into text using Speech Recognition Library. You can control the devices or you can perform the desired tasks by the word recognition

Raspberry_Pi Pakistan 2 Dec 15, 2021
a library for audio and music analysis

aubio aubio is a library to label music and sounds. It listens to audio signals and attempts to detect events. For instance, when a drum is hit, at wh

aubio 2.9k Dec 30, 2022
Terminal-based music player written in Python for the best music in the world 🎵 🎧 💻

audius-terminal-player Terminal-based music player written in Python for the best music in the world 🎵 🎧 💻 Browse and listen to Audius from the com

Audius 21 Jul 23, 2022
Mina - A Telegram Music Bot 5 mandatory Assistant written in Python using Pyrogram and Py-Tgcalls

Mina - A Telegram Music Bot 5 mandatory Assistant written in Python using Pyrogram and Py-Tgcalls

3 Feb 07, 2022
Jarvis From Basic to Advance - make a voice assistant similar to JARVIS (in iron man movie)

JARVIS (Basic to Advance) This was my attempt to make a voice assistant similar to JARVIS (in iron man movie) Let's be honest, it's not as intelligent

codesempai 17 Dec 25, 2022
:sound: Play and Record Sound with Python :snake:

Play and Record Sound with Python This Python module provides bindings for the PortAudio library and a few convenience functions to play and record Nu

spatialaudio.net 750 Dec 31, 2022
Music bot of # Owner

Pokimane-Music Music bot of # Owner How To Host The easiest way to deploy this Bot Support Channel :- TeamDlt Support Group :- TeamDlt Please fork thi

5 Dec 23, 2022
IDing the songs played on the do you radio show

IDing the songs played on the do you radio show

Rasmus Jones 36 Nov 15, 2022
Desktop music recognition application for windows

MusicRecognizer Music recognition application for windows You can choose from which of the devices the recording will be made. If you choose speakers,

Nikita Merzlyakov 28 Dec 13, 2022
Real-Time Spherical Microphone Renderer for binaural reproduction in Python

ReTiSAR Implementation of the Real-Time Spherical Microphone Renderer for binaural reproduction in Python [1][2]. Contents: | Requirements | Setup | Q

Division of Applied Acoustics at Chalmers University of Technology 51 Dec 17, 2022
Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

MediumVC MediumVC is an utterance-level method towards any-to-any VC. Before that, we propose SingleVC to perform A2O tasks(Xi → Ŷi) , Xi means utter

谷下雨 47 Dec 25, 2022
Cobra is a highly-accurate and lightweight voice activity detection (VAD) engine.

On-device voice activity detection (VAD) powered by deep learning.

Picovoice 88 Dec 16, 2022
Audio augmentations library for PyTorch for audio in the time-domain

Audio augmentations library for PyTorch for audio in the time-domain, with support for stochastic data augmentations as used often in self-supervised / contrastive learning.

Janne 166 Jan 08, 2023
Code for "Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose"

Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose We provide PyTorch implementations for our arxiv paper "Audio-dr

Ran Yi 497 Jan 09, 2023
Frescobaldi LilyPond Editor

README for Frescobaldi Homepage: http://www.frescobaldi.org/ Main author: Wilbert Berendsen Frescobaldi is a LilyPond sheet music text editor. It aims

Frescobaldi 600 Dec 29, 2022
❤️ This Is The EzilaXMusicPlayer Advaced Repo 🎵

Telegram EzilaXMusicPlayer Bot 🎵 A bot that can play music on telegram group's voice Chat ❤️ Requirements 📝 FFmpeg NodeJS nodesource.com Python 3.7+

Sadew Jayasekara 11 Nov 12, 2022
Analyze, visualize and process sound field data recorded by spherical microphone arrays.

Sound Field Analysis toolbox for Python The sound_field_analysis toolbox (short: sfa) is a Python port of the Sound Field Analysis Toolbox (SOFiA) too

Division of Applied Acoustics at Chalmers University of Technology 69 Nov 23, 2022
GNOME powered sound conversion

SoundConverter A simple sound converter application for the GNOME environment. It reads anything the GStreamer library can read, and writes Ogg Vorbis

Gautier Portet 188 Dec 17, 2022