There is a newer version of this record available.

Dataset Open Access

SignalTrain LA2A Dataset

Colburn, Benjamin; Hawley, Scott


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/8aeb333f-e22e-4c83-a44b-7bbab05e1e6b/SignalTrain_LA2A_Dataset.tgz"
      }, 
      "checksum": "md5:4c31a4013996546c9e4466ef545ab90d", 
      "bucket": "8aeb333f-e22e-4c83-a44b-7bbab05e1e6b", 
      "key": "SignalTrain_LA2A_Dataset.tgz", 
      "type": "tgz", 
      "size": 20995801787
    }
  ], 
  "owners": [
    47432
  ], 
  "doi": "10.5281/zenodo.3348083", 
  "stats": {
    "version_unique_downloads": 52.0, 
    "unique_views": 152.0, 
    "views": 167.0, 
    "version_views": 206.0, 
    "unique_downloads": 47.0, 
    "version_unique_views": 182.0, 
    "volume": 1196760701859.0, 
    "version_downloads": 62.0, 
    "downloads": 57.0, 
    "version_volume": 1300254483844.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.3348083", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.3348082", 
    "bucket": "https://zenodo.org/api/files/8aeb333f-e22e-4c83-a44b-7bbab05e1e6b", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.3348082.svg", 
    "html": "https://zenodo.org/record/3348083", 
    "latest_html": "https://zenodo.org/record/3824876", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.3348083.svg", 
    "latest": "https://zenodo.org/api/records/3824876"
  }, 
  "conceptdoi": "10.5281/zenodo.3348082", 
  "created": "2019-07-23T22:41:04.406049+00:00", 
  "updated": "2020-05-13T16:41:44.544672+00:00", 
  "conceptrecid": "3348082", 
  "revision": 7, 
  "id": 3348083, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.3348083", 
    "description": "<p>LA-2A Compressor data to accompany&nbsp;the paper &quot;SignalTrain: Profiling Audio Compressors with Deep Neural Networks,&quot;&nbsp;<a href=\"https://arxiv.org/abs/1905.11928\">https://arxiv.org/abs/1905.11928</a></p>\n\n<p>Accompanying computer code:&nbsp;<a href=\"https://github.com/drscotthawley/signaltrain\">https://github.com/drscotthawley/signaltrain</a></p>\n\n<p>A collection of recorded data from an analog&nbsp;Teletronix LA-2A opto-electronic compressor, for various settings of the Peak Reduction knob.&nbsp; Other knobs were kept constant.&nbsp;&nbsp;</p>\n\n<p>Audio samples present in these files are either &#39;randomly generated&#39;, or downloaded audio clips with Create Commons licenses, or are property of Scott Hawley freely distributed as part of this dataset.&nbsp;</p>\n\n<p>Data taken by Ben Colburn, supervised by Scott Hawley</p>\n\n<p>&nbsp;</p>", 
    "language": "eng", 
    "title": "SignalTrain LA2A Dataset", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "relations": {
      "version": [
        {
          "count": 2, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3348082"
          }, 
          "is_last": false, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "3824876"
          }
        }
      ]
    }, 
    "version": "1.0", 
    "references": [
      "arXiv:1905.11928"
    ], 
    "keywords": [
      "audio effects, analog audio"
    ], 
    "publication_date": "2019-07-23", 
    "creators": [
      {
        "affiliation": "ARiA Acoustics", 
        "name": "Colburn, Benjamin"
      }, 
      {
        "orcid": "0000-0002-5743-6441", 
        "affiliation": "Belmont University", 
        "name": "Hawley, Scott"
      }
    ], 
    "meeting": {
      "acronym": "AES 147", 
      "url": "http://www.aes.org/events/147/", 
      "dates": "16-19 October 2019", 
      "place": "New York, USA", 
      "title": "Audio Engineering Society 147th Conference"
    }, 
    "access_right": "open", 
    "resource_type": {
      "type": "dataset", 
      "title": "Dataset"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.3348082", 
        "relation": "isVersionOf"
      }
    ]
  }
}
206
62
views
downloads
All versions This version
Views 206167
Downloads 6257
Data volume 1.3 TB1.2 TB
Unique views 182152
Unique downloads 5247

Share

Cite as