![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14852316%2F741036c0ff370457e01dbae8875d3a24%2Fdownload.png?generation=1749186870435104&alt=media\")
I'd like to share an approach for mel spectrogram generation that focuses on extracting the most acoustically active 5-second segments from audio recordings.
\nUsing the mel spectrograms generated with this approach, I achieved:
\nwith EfficientNet-B0 5-fold cross-validation.
\n
Figure 1 shows a direct comparison between center extraction vs. energy-based extraction:
\n![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14852316%2F9f34b9a6ac78b75e7cb7c3d1d0684ced%2F__results___20_5.png?generation=1749186856461506&alt=media\")
Figure 2 reveals the distribution pattern of highest energy segments across 1000 audio samples:
\nThe mel spectrogram generation notebook is available here:
\nhttps://www.kaggle.com/code/ichigoe/extracting-5-second-segments-with-highest-energy/
This energy-based segment extraction technique could be useful for future BirdCLEF competitions as it focuses on the most acoustically relevant portions of recordings where bird calls are most likely to occur.
", "messages": [ { "id": 3218345, "postDate": "2025-06-06T05:17:25.860Z", "content": "I'd like to share an approach for mel spectrogram generation that focuses on extracting the most acoustically active 5-second segments from audio recordings.
\nUsing the mel spectrograms generated with this approach, I achieved:
\nwith EfficientNet-B0 5-fold cross-validation.
\n
Figure 1 shows a direct comparison between center extraction vs. energy-based extraction:
\n
Figure 2 reveals the distribution pattern of highest energy segments across 1000 audio samples:
\nThe mel spectrogram generation notebook is available here:
\nhttps://www.kaggle.com/code/ichigoe/extracting-5-second-segments-with-highest-energy/
This energy-based segment extraction technique could be useful for future BirdCLEF competitions as it focuses on the most acoustically relevant portions of recordings where bird calls are most likely to occur.
", "rawMarkdown": "I'd like to share an approach for mel spectrogram generation that focuses on extracting the most acoustically active 5-second segments from audio recordings.\n\n## Key Features:\n- **Energy-based segment extraction**: Instead of using center or random 5-second segments, this method calculates energy distribution across the audio and selects the highest energy window\n- **Human voice detection and removal**: Attenuates human voice frequencies (300-3500Hz) to reduce contamination from researchers' voices in field recordings \n- **GPU-accelerated processing**: Optimized pipeline using PyTorch transforms for faster mel spectrogram computation\n\n## Results:\nUsing the mel spectrograms generated with this approach, I achieved:\n- **Private LB: 0.847**\n- **Public LB: 0.842**\n\nwith EfficientNet-B0 5-fold cross-validation.\n\n## Visual Comparison\n\n\n**Figure 1** shows a direct comparison between center extraction vs. energy-based extraction:\n- **Top panels**: The energy-based method selects segments with significantly higher amplitude and acoustic activity\n- **Bottom panels**: The resulting mel spectrograms show much richer frequency patterns and clearer harmonic structures in the energy-based extraction, providing more informative features for the model\n\n\n\n**Figure 2** reveals the distribution pattern of highest energy segments across 1000 audio samples:\n- **Mean position: 0.362** (36.2% into the audio)\n- **Median position: 0.336** (33.6% into the audio)\n- **Observation**: Both values are significantly earlier than the traditional center point (0.5), with a prominent peak at the very beginning of recordings. This pattern suggests that the most acoustically active segments tend to occur in the earlier portions of field recordings, which may explain why center extraction is often suboptimal.\n\nThe mel spectrogram generation notebook is available here:\nhttps://www.kaggle.com/code/ichigoe/extracting-5-second-segments-with-highest-energy/\n\nThis energy-based segment extraction technique could be useful for future BirdCLEF competitions as it focuses on the most acoustically relevant portions of recordings where bird calls are most likely to occur.\n", "votes": 3 } ], "comments": [], "raw_markdown_by_id": { "3218345": "I'd like to share an approach for mel spectrogram generation that focuses on extracting the most acoustically active 5-second segments from audio recordings.\n\n## Key Features:\n- **Energy-based segment extraction**: Instead of using center or random 5-second segments, this method calculates energy distribution across the audio and selects the highest energy window\n- **Human voice detection and removal**: Attenuates human voice frequencies (300-3500Hz) to reduce contamination from researchers' voices in field recordings \n- **GPU-accelerated processing**: Optimized pipeline using PyTorch transforms for faster mel spectrogram computation\n\n## Results:\nUsing the mel spectrograms generated with this approach, I achieved:\n- **Private LB: 0.847**\n- **Public LB: 0.842**\n\nwith EfficientNet-B0 5-fold cross-validation.\n\n## Visual Comparison\n\n\n**Figure 1** shows a direct comparison between center extraction vs. energy-based extraction:\n- **Top panels**: The energy-based method selects segments with significantly higher amplitude and acoustic activity\n- **Bottom panels**: The resulting mel spectrograms show much richer frequency patterns and clearer harmonic structures in the energy-based extraction, providing more informative features for the model\n\n\n\n**Figure 2** reveals the distribution pattern of highest energy segments across 1000 audio samples:\n- **Mean position: 0.362** (36.2% into the audio)\n- **Median position: 0.336** (33.6% into the audio)\n- **Observation**: Both values are significantly earlier than the traditional center point (0.5), with a prominent peak at the very beginning of recordings. This pattern suggests that the most acoustically active segments tend to occur in the earlier portions of field recordings, which may explain why center extraction is often suboptimal.\n\nThe mel spectrogram generation notebook is available here:\nhttps://www.kaggle.com/code/ichigoe/extracting-5-second-segments-with-highest-energy/\n\nThis energy-based segment extraction technique could be useful for future BirdCLEF competitions as it focuses on the most acoustically relevant portions of recordings where bird calls are most likely to occur.\n" } }