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A-SPADE: the Analysis SParse Audio DEclipper is a software to improve the sound quality of saturated audio files

Please log in to perform a job with this app.


Clipping, also known as saturation, is a common phenomenon leading to sometimes seriously distorted audio recordings. Declipping consists in performing the inverse process, to restore saturated audio recordings and improve their quality. A-SPADE is a declipping algorithm developed by PANAMA (a joint project-team between Inria and CNRS). It is based on on the expression of declipping as a linear inverse problem and the use of analysis sparse regularization in the time-frequency domain. To the best of our knowledge SPADE achieves state-of-the-art declipping quality.

For more information, please visit this website: spade.inria.fr. You can also contact us through this form.

Before using the software, have a look at this documentation and at the “Demo” tab which provides some input examples (parameters and audio files) that can be used with A-SPADE. The “Demo” tab also provides output examples.


A-SPADE will give you the desaturated version of your input signal as a .wav file. Supported input formats are .wav, .mp3, .mp4, .m4a, .ogg, .flac. Long multi-channel files are eligible for declipping. This online application, as a trial version of the software with limited parameters options, will process only the first 2 channels and the first 30 seconds of your input audio files. If you are interested in processing longer excerpts or more than stereophonic recordings, please contact us.

To use the software, follow these steps:

  1. Upload your audio file(s) with the “+ Upload File” button.

    • a mandatory clipped input file;
    • an optional clean reference (Warning: If you have a clean reference make sure that it is recorded with the same sampling rate as the saturated one, that it has the same channel number and the same duration but a different name).
  2. In the “Version” pop-up menu, verify that the latest software version is selected.

  3. In the “Parameters” field, type (all parameters must be separated using a single whitespace - see the image below):

    • Mandatory: the name of your clipped file as first parameter (yes, you do have to provide it, A||go cannot guess it);
    • Optional: the other parameters (in any order):
      • The name of the reference file (to compute the Signal to Distortion Ratio);
      • One preset allowing some tuning of the algorithm: “HighQuality”, “Average” (default value), “Fastest”;
      • One numerical value (offset in dB) used to adjust the automatically estimated clipping threshold (0 is the default value). With a negative offset value, more samples will be considered as clipped and then modified by the algorithm. The figure in the output file “Offset_overview.png” can help you understanding the impact of the “offset” parameter. If you do not notice any improvement listening to the result, try with a negative offset. If you notice degraded results with respect to your original file, try with a positive offset.


  4. Start your declipping by clicking on the “Run this job” button.

  5. Once your job is finished you should be able to listen to the result and save your declipped signal in a file named “Declipped_<name of your input file>.wav”. As in the screenshot below, if you provided a reference signal you will get the SDR (Signal to Distortion Ration in dB) measurements before and after processing in the “allgo.log” file. Finally, you will find an overview of the “offset” parameter displayed in the “Offset_overview.png” file.

  6. To try the application with other files and submit a new job, click on the “A-SPADE” link in the left top corner of the result page as shown below.


Processing time

The processing time depends on the chosen preset (“Fastest”, “Average”, “HighQuality”) and increases with:

  • the number of clipped samples;
  • the sampling frequency;
  • the file duration;
  • the number of channels.

With the 20 seconds long mono demo file sampled at 16 kHz and clipped at 3dB SDR, the processing time with the “HighQuality” preset is about 3 min. With the “Fastest” preset, the computing time is around 20 seconds and respectively 40 seconds for the “Average” preset.

Licence and Credits

You may exploit this software for a non-commercial scientific purpose provided you mention it in any written work or software you derive from its use. Within a published article, paper or report, the software must appear in the bibliographical references as:
S. Kitic, N. Bertin and R.Gribonval. Sparsity and cosparsity for audio declipping: a flexible non-convex approach. In Latent Variable Analysis and Signal Separation, Liberec, 2015.

The examples shown in the "Demo" section are from the RWC Jazz Database: M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka: RWC Music Database: Popular, Classical, and Jazz Music Databases In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), 2002.

In input :



In output :

Offset overview
User parameters:
Clipped input signal: clipped.wav
Original input signal: original.wav
Used preset: HighQuality
Clipping threshold offset: 0.00 dB

Clipping thresholds computation: 
   - Estimated clipping threshold for channel 1: -25.84 dB (positive samples) -26.13 dB (negative samples)

Declipping in progress: 100%
   - Channel 1: 200041 (62%) clipped samples reconstructed on a total of 320832 samples.

SDR measurements: 
   - Channel 1:  New SDR: 9.66 dB  |  Old SDR: 3.00 dB  |  Improvement: 6.66 dB

Declipping succeeded!


08/03/2018 : Version 2.0,
16/01/2018 : Version 1.5,
21/12/2017 : Version 1.4,
26/04/2017 : Version 1.3,
22/03/2017 : Version 1.2,
28/02/2017 : Version 1.1,
06/10/2016 : Version 1.0,

How to use our REST API :

Think to check your private token in your account first. You can find more detail in our documentation tab.

This app id is : 134

This curl command will create a job, and return your job url, and also the average execution time

files and/or dataset are optionnal, think to remove them if not wanted
curl -H 'Authorization: Token token=<your_private_token>' -X POST
-F job[webapp_id]=134
-F job[param]=""
-F job[queue]=standard
-F files[0]=@test.txt
-F files[1]=@test2.csv
-F job[file_url]=<my_file_url>
-F job[dataset]=<my_dataset_name> https://allgo.inria.fr/api/v1/jobs

Then, check your job to get the url files with :

curl -H 'Authorization: Token token=<your_private_token>' -X GET https://allgo.inria.fr/api/v1/jobs/<job_id>