{
  "id": 496565,
  "title": "Weakly labeled recordings",
  "url": "/competitions/birdclef-2024/discussion/496565",
  "author_name": "",
  "post_date": "2024-04-21T16:05:49.637120200Z",
  "votes": 2,
  "comment_count": 3,
  "views": 0,
  "content": "<p>Dear Community,</p>\n<p>As I understand it, each audio file in the testing dataset is labeled with a single bird species present in the recording. In the literature they refer to this as 'weak labeling'. There might be interference from other birds in the background. Each recording will be sliced into 5-second snippets. I was wondering how that jives with the label. For example, suppose the entire recording is labeled as 'Sparrow'. There might be portions of the recording where you could only hear another bird, that's background noise.</p>\n<p>Wouldn't a good model be capable of switching to the background noise bird in those snippets? However, then the scoring function will penalize you for predicting high probability for something different than the ground truth label, right?</p>\n<p>Do I understand this correctly? How can I deal with this? Any suggestions?</p>\n<p>Thank you, </p>\n<p>Bas</p>",
  "messages": [
    {
      "id": "2766304",
      "postDate": "04/21/2024 16:05:49",
      "content": "<p>Dear Community,</p>\n<p>As I understand it, each audio file in the testing dataset is labeled with a single bird species present in the recording. In the literature they refer to this as 'weak labeling'. There might be interference from other birds in the background. Each recording will be sliced into 5-second snippets. I was wondering how that jives with the label. For example, suppose the entire recording is labeled as 'Sparrow'. There might be portions of the recording where you could only hear another bird, that's background noise.</p>\n<p>Wouldn't a good model be capable of switching to the background noise bird in those snippets? However, then the scoring function will penalize you for predicting high probability for something different than the ground truth label, right?</p>\n<p>Do I understand this correctly? How can I deal with this? Any suggestions?</p>\n<p>Thank you, </p>\n<p>Bas</p>",
      "rawMarkdown": "Dear Community,\n\nAs I understand it, each audio file in the testing dataset is labeled with a single bird species present in the recording. In the literature they refer to this as 'weak labeling'. There might be interference from other birds in the background. Each recording will be sliced into 5-second snippets. I was wondering how that jives with the label. For example, suppose the entire recording is labeled as 'Sparrow'. There might be portions of the recording where you could only hear another bird, that's background noise.\n\nWouldn't a good model be capable of switching to the background noise bird in those snippets? However, then the scoring function will penalize you for predicting high probability for something different than the ground truth label, right?\n\nDo I understand this correctly? How can I deal with this? Any suggestions?\n\nThank you, \n\nBas",
      "votes": null
    },
    {
      "id": "2766491",
      "postDate": "04/21/2024 18:10:42",
      "content": "<p>If there are multiple species in a 5-second segment in the test data, the ground truth data is supposed to label all of them.</p>",
      "rawMarkdown": "If there are multiple species in a 5-second segment in the test data, the ground truth data is supposed to label all of them.",
      "votes": null
    },
    {
      "id": "2766549",
      "postDate": "04/21/2024 18:49:09",
      "content": "<p>Thank you. So each 5-second snippet we encounter in the test data set will contain a ground truth label? It's not just a direct copy of the label associated with the entire four minute file the snippet was taken from?</p>",
      "rawMarkdown": "Thank you. So each 5-second snippet we encounter in the test data set will contain a ground truth label? It's not just a direct copy of the label associated with the entire four minute file the snippet was taken from?",
      "votes": null
    },
    {
      "id": "2766555",
      "postDate": "04/21/2024 18:55:32",
      "content": "<p>Correct. Each 5-second snippet has its own ground-truth label. They're labelled by human reviewers though, so they likely have some mistakes.</p>",
      "rawMarkdown": "Correct. Each 5-second snippet has its own ground-truth label. They're labelled by human reviewers though, so they likely have some mistakes.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2766491,
      "author_name": "janhuus",
      "author_url": "",
      "post_date": "04/21/2024 18:10:42",
      "content": "<p>If there are multiple species in a 5-second segment in the test data, the ground truth data is supposed to label all of them.</p>",
      "votes": null,
      "replies": [
        {
          "id": 2766549,
          "author_name": "basmts",
          "author_url": "",
          "post_date": "04/21/2024 18:49:09",
          "content": "<p>Thank you. So each 5-second snippet we encounter in the test data set will contain a ground truth label? It's not just a direct copy of the label associated with the entire four minute file the snippet was taken from?</p>",
          "votes": null,
          "replies": [
            {
              "id": 2766555,
              "author_name": "janhuus",
              "author_url": "",
              "post_date": "04/21/2024 18:55:32",
              "content": "<p>Correct. Each 5-second snippet has its own ground-truth label. They're labelled by human reviewers though, so they likely have some mistakes.</p>",
              "votes": null,
              "replies": []
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2766304": "Dear Community,\n\nAs I understand it, each audio file in the testing dataset is labeled with a single bird species present in the recording. In the literature they refer to this as 'weak labeling'. There might be interference from other birds in the background. Each recording will be sliced into 5-second snippets. I was wondering how that jives with the label. For example, suppose the entire recording is labeled as 'Sparrow'. There might be portions of the recording where you could only hear another bird, that's background noise.\n\nWouldn't a good model be capable of switching to the background noise bird in those snippets? However, then the scoring function will penalize you for predicting high probability for something different than the ground truth label, right?\n\nDo I understand this correctly? How can I deal with this? Any suggestions?\n\nThank you, \n\nBas",
    "2766491": "If there are multiple species in a 5-second segment in the test data, the ground truth data is supposed to label all of them.",
    "2766549": "Thank you. So each 5-second snippet we encounter in the test data set will contain a ground truth label? It's not just a direct copy of the label associated with the entire four minute file the snippet was taken from?",
    "2766555": "Correct. Each 5-second snippet has its own ground-truth label. They're labelled by human reviewers though, so they likely have some mistakes."
  },
  "source": "meta"
}