{
  "id": 580160,
  "title": "EDA Insights — BirdCLEF+ 2025",
  "url": "/competitions/birdclef-2025/discussion/580160",
  "author_name": "andyops",
  "post_date": "2025-05-22T20:11:14.887000",
  "votes": -1,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Hi everyone,</p>\n<p>I recently completed an exploratory data analysis (EDA) on the BirdCLEF 2025 dataset and wanted to share some key findings that could help guide modeling decisions. This year’s task focuses on identifying species (birds, amphibians, mammals, insects) from real-world audio recordings in the El Silencio Natural Reserve, Colombia.</p>\n<p>.</p>\n<p>🔗 Notebook: <a href=\"https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration\" target=\"_blank\">https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration</a><br>\n(Pardon the mess—will clean it up soon! 😅)</p>\n<p>✅ Dataset Overview<br>\nTotal recordings: 28,564</p>\n<p>Unique species: 206</p>\n<p>Classes: Aves, Amphibia, Mammalia, Insecta</p>\n<p>🐦 Class &amp; Species Imbalance<br>\nAves dominate the dataset with over 27k recordings and 146 unique species.</p>\n<p>Other classes (Amphibia, Mammalia, Insecta) are severely underrepresented.</p>\n<p>Clear long-tail distribution: a few species have thousands of samples, while many have &lt;50.</p>\n<p>🔍 This imbalance needs to be addressed to avoid bias toward majority classes.</p>\n<p>📁 Source Collections<br>\nXC: 21,204 recordings (rated, high quality)</p>\n<p>iNat &amp; CSA: 7,198 + 162 recordings, no quality ratings</p>\n<p>XC ratings peak at 4.0–5.0, indicating clean, high-quality audio.</p>\n<p>🔍 We might consider using rating-based sampling or model weighting based on source.</p>\n<p>🌍 Geolocation Patterns<br>\nAves: Spread across Americas and Europe</p>\n<p>Amphibia: Central America</p>\n<p>Insecta: Clustered in NW Brazil</p>\n<p>Mammalia: Sparse, mostly North America</p>\n<p>🔍 Geographic clustering suggests we may need spatial-aware cross-validation to avoid overfitting to regions.</p>\n<p>🔈 Audio Energy (train_soundscapes/)<br>\nRMS analysis of random 1000 samples shows most recordings are near-silent.</p>\n<p>Only a few have high energy, likely indicating presence of species calls.</p>\n<p>🔍 Helps in targeting active segments for model training or pseudo-labeling.</p>\n<p>🧬 Taxonomy Data<br>\nAll species are correctly matched and classified across the taxonomy dataset.</p>\n<p>🔍 Ensures we can safely join metadata and taxonomy for feature engineering.</p>\n<p>💡 Final Thoughts<br>\nThe need to address class imbalance and source bias.</p>\n<p>The importance of geographic diversity and careful cross-validation.</p>\n<p>Opportunities to leverage audio energy for efficient training.</p>\n<p>Excited to see how everyone tackles the challenges in this year’s competition. Good luck! 🍀</p>",
  "messages": [
    {
      "id": 3207491,
      "postDate": "2025-05-22T20:11:14.887Z",
      "content": "<p>Hi everyone,</p>\n<p>I recently completed an exploratory data analysis (EDA) on the BirdCLEF 2025 dataset and wanted to share some key findings that could help guide modeling decisions. This year’s task focuses on identifying species (birds, amphibians, mammals, insects) from real-world audio recordings in the El Silencio Natural Reserve, Colombia.</p>\n<p>.</p>\n<p>🔗 Notebook: <a href=\"https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration\" target=\"_blank\">https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration</a><br>\n(Pardon the mess—will clean it up soon! 😅)</p>\n<p>✅ Dataset Overview<br>\nTotal recordings: 28,564</p>\n<p>Unique species: 206</p>\n<p>Classes: Aves, Amphibia, Mammalia, Insecta</p>\n<p>🐦 Class &amp; Species Imbalance<br>\nAves dominate the dataset with over 27k recordings and 146 unique species.</p>\n<p>Other classes (Amphibia, Mammalia, Insecta) are severely underrepresented.</p>\n<p>Clear long-tail distribution: a few species have thousands of samples, while many have &lt;50.</p>\n<p>🔍 This imbalance needs to be addressed to avoid bias toward majority classes.</p>\n<p>📁 Source Collections<br>\nXC: 21,204 recordings (rated, high quality)</p>\n<p>iNat &amp; CSA: 7,198 + 162 recordings, no quality ratings</p>\n<p>XC ratings peak at 4.0–5.0, indicating clean, high-quality audio.</p>\n<p>🔍 We might consider using rating-based sampling or model weighting based on source.</p>\n<p>🌍 Geolocation Patterns<br>\nAves: Spread across Americas and Europe</p>\n<p>Amphibia: Central America</p>\n<p>Insecta: Clustered in NW Brazil</p>\n<p>Mammalia: Sparse, mostly North America</p>\n<p>🔍 Geographic clustering suggests we may need spatial-aware cross-validation to avoid overfitting to regions.</p>\n<p>🔈 Audio Energy (train_soundscapes/)<br>\nRMS analysis of random 1000 samples shows most recordings are near-silent.</p>\n<p>Only a few have high energy, likely indicating presence of species calls.</p>\n<p>🔍 Helps in targeting active segments for model training or pseudo-labeling.</p>\n<p>🧬 Taxonomy Data<br>\nAll species are correctly matched and classified across the taxonomy dataset.</p>\n<p>🔍 Ensures we can safely join metadata and taxonomy for feature engineering.</p>\n<p>💡 Final Thoughts<br>\nThe need to address class imbalance and source bias.</p>\n<p>The importance of geographic diversity and careful cross-validation.</p>\n<p>Opportunities to leverage audio energy for efficient training.</p>\n<p>Excited to see how everyone tackles the challenges in this year’s competition. Good luck! 🍀</p>",
      "rawMarkdown": "Hi everyone,\n\nI recently completed an exploratory data analysis (EDA) on the BirdCLEF 2025 dataset and wanted to share some key findings that could help guide modeling decisions. This year’s task focuses on identifying species (birds, amphibians, mammals, insects) from real-world audio recordings in the El Silencio Natural Reserve, Colombia.\n\n.\n\n🔗 Notebook: https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration\n(Pardon the mess—will clean it up soon! 😅)\n\n✅ Dataset Overview\nTotal recordings: 28,564\n\nUnique species: 206\n\nClasses: Aves, Amphibia, Mammalia, Insecta\n\n🐦 Class & Species Imbalance\nAves dominate the dataset with over 27k recordings and 146 unique species.\n\nOther classes (Amphibia, Mammalia, Insecta) are severely underrepresented.\n\nClear long-tail distribution: a few species have thousands of samples, while many have <50.\n\n🔍 This imbalance needs to be addressed to avoid bias toward majority classes.\n\n📁 Source Collections\nXC: 21,204 recordings (rated, high quality)\n\niNat & CSA: 7,198 + 162 recordings, no quality ratings\n\nXC ratings peak at 4.0–5.0, indicating clean, high-quality audio.\n\n🔍 We might consider using rating-based sampling or model weighting based on source.\n\n🌍 Geolocation Patterns\nAves: Spread across Americas and Europe\n\nAmphibia: Central America\n\nInsecta: Clustered in NW Brazil\n\nMammalia: Sparse, mostly North America\n\n🔍 Geographic clustering suggests we may need spatial-aware cross-validation to avoid overfitting to regions.\n\n🔈 Audio Energy (train_soundscapes/)\nRMS analysis of random 1000 samples shows most recordings are near-silent.\n\nOnly a few have high energy, likely indicating presence of species calls.\n\n🔍 Helps in targeting active segments for model training or pseudo-labeling.\n\n🧬 Taxonomy Data\nAll species are correctly matched and classified across the taxonomy dataset.\n\n🔍 Ensures we can safely join metadata and taxonomy for feature engineering.\n\n💡 Final Thoughts\nThe need to address class imbalance and source bias.\n\nThe importance of geographic diversity and careful cross-validation.\n\nOpportunities to leverage audio energy for efficient training.\n\nExcited to see how everyone tackles the challenges in this year’s competition. Good luck! 🍀\n\n",
      "votes": -1
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3207491": "Hi everyone,\n\nI recently completed an exploratory data analysis (EDA) on the BirdCLEF 2025 dataset and wanted to share some key findings that could help guide modeling decisions. This year’s task focuses on identifying species (birds, amphibians, mammals, insects) from real-world audio recordings in the El Silencio Natural Reserve, Colombia.\n\n.\n\n🔗 Notebook: https://www.kaggle.com/code/anasshahmurshad/birdclef-data-exploration\n(Pardon the mess—will clean it up soon! 😅)\n\n✅ Dataset Overview\nTotal recordings: 28,564\n\nUnique species: 206\n\nClasses: Aves, Amphibia, Mammalia, Insecta\n\n🐦 Class & Species Imbalance\nAves dominate the dataset with over 27k recordings and 146 unique species.\n\nOther classes (Amphibia, Mammalia, Insecta) are severely underrepresented.\n\nClear long-tail distribution: a few species have thousands of samples, while many have <50.\n\n🔍 This imbalance needs to be addressed to avoid bias toward majority classes.\n\n📁 Source Collections\nXC: 21,204 recordings (rated, high quality)\n\niNat & CSA: 7,198 + 162 recordings, no quality ratings\n\nXC ratings peak at 4.0–5.0, indicating clean, high-quality audio.\n\n🔍 We might consider using rating-based sampling or model weighting based on source.\n\n🌍 Geolocation Patterns\nAves: Spread across Americas and Europe\n\nAmphibia: Central America\n\nInsecta: Clustered in NW Brazil\n\nMammalia: Sparse, mostly North America\n\n🔍 Geographic clustering suggests we may need spatial-aware cross-validation to avoid overfitting to regions.\n\n🔈 Audio Energy (train_soundscapes/)\nRMS analysis of random 1000 samples shows most recordings are near-silent.\n\nOnly a few have high energy, likely indicating presence of species calls.\n\n🔍 Helps in targeting active segments for model training or pseudo-labeling.\n\n🧬 Taxonomy Data\nAll species are correctly matched and classified across the taxonomy dataset.\n\n🔍 Ensures we can safely join metadata and taxonomy for feature engineering.\n\n💡 Final Thoughts\nThe need to address class imbalance and source bias.\n\nThe importance of geographic diversity and careful cross-validation.\n\nOpportunities to leverage audio energy for efficient training.\n\nExcited to see how everyone tackles the challenges in this year’s competition. Good luck! 🍀\n\n"
  }
}