Presenting summarization of a conversation between @nischaydnk @init27 where they discussed how to Get started in BirdClef, these are few ideas i could grasp that could be helpful in this years BirdCLEF
\nhttps://www.youtube.com/watch?v=TrpI9ih0Vxs
\nSound Event Detection (SED) architecture: Many top solutions have used the SED architecture, which generates predictions for the complete audio file as well as for the desired time span (e.g., five seconds). This can be useful for post-processing and improving predictions.
\nPre-processing and feature extraction: Convert raw audio signals into spectrograms using techniques like log-mel transformation. This helps in converting audio data into images that can be fed into a neural network.
\nAugmentations: Apply augmentations to both audio files and spectrograms. Examples include pink noise, mixup, cutmix, and SpecAugment (masking time or frequency domain).
\nLoss functions: Experiment with different loss functions to deal with class imbalance, such as binary cross-entropy (BCE) loss, focal loss, or a combination of these.
\nModel architecture: Consider using EfficientNet or NFNet as the backbone of your model. You can also try using attention blocks for improved feature extraction.
\nPost-processing: Explore post-processing techniques to improve predictions for shorter time spans.
\nExternal data: Utilize external data and background noise to improve the robustness of your models.
\nEnsembles and pseudo-labeling: Experiment with ensembling different models and incorporating pseudo-labeling for improving your model's performance.
\nValidation strategy: Use a stratified split to create cross-validation folds, keeping in mind the competition's constraints on computation time and resources during inference.
\nAnd finally, Keep an eye on recent developments in audio classification and deep learning, such as noise prediction, better pre-processing techniques, and new normalization methods to improve the signal-to-noise ratio.
\nBy combining these insights and experimenting with different techniques, you can develop a competitive solution for this year's BirdCLEF competition. Good luck!
", "messages": [ { "id": "2205485", "postDate": "04/01/2023 15:53:12", "content": "Presenting summarization of a conversation between @nischaydnk @init27 where they discussed how to Get started in BirdClef, these are few ideas i could grasp that could be helpful in this years BirdCLEF
\nhttps://www.youtube.com/watch?v=TrpI9ih0Vxs
\nSound Event Detection (SED) architecture: Many top solutions have used the SED architecture, which generates predictions for the complete audio file as well as for the desired time span (e.g., five seconds). This can be useful for post-processing and improving predictions.
\nPre-processing and feature extraction: Convert raw audio signals into spectrograms using techniques like log-mel transformation. This helps in converting audio data into images that can be fed into a neural network.
\nAugmentations: Apply augmentations to both audio files and spectrograms. Examples include pink noise, mixup, cutmix, and SpecAugment (masking time or frequency domain).
\nLoss functions: Experiment with different loss functions to deal with class imbalance, such as binary cross-entropy (BCE) loss, focal loss, or a combination of these.
\nModel architecture: Consider using EfficientNet or NFNet as the backbone of your model. You can also try using attention blocks for improved feature extraction.
\nPost-processing: Explore post-processing techniques to improve predictions for shorter time spans.
\nExternal data: Utilize external data and background noise to improve the robustness of your models.
\nEnsembles and pseudo-labeling: Experiment with ensembling different models and incorporating pseudo-labeling for improving your model's performance.
\nValidation strategy: Use a stratified split to create cross-validation folds, keeping in mind the competition's constraints on computation time and resources during inference.
\nAnd finally, Keep an eye on recent developments in audio classification and deep learning, such as noise prediction, better pre-processing techniques, and new normalization methods to improve the signal-to-noise ratio.
\nBy combining these insights and experimenting with different techniques, you can develop a competitive solution for this year's BirdCLEF competition. Good luck!
", "rawMarkdown": "Presenting summarization of a conversation between @nischaydnk @init27 where they discussed how to Get started in BirdClef, these are few ideas i could grasp that could be helpful in this years BirdCLEF\n\n[https://www.youtube.com/watch?v=TrpI9ih0Vxs](url)\n\n**Sound Event Detection (SED) architecture:** Many top solutions have used the SED architecture, which generates predictions for the complete audio file as well as for the desired time span (e.g., five seconds). This can be useful for post-processing and improving predictions.\n\n**Pre-processing and feature extraction:** Convert raw audio signals into spectrograms using techniques like log-mel transformation. This helps in converting audio data into images that can be fed into a neural network.\n\n**Augmentations:** Apply augmentations to both audio files and spectrograms. Examples include pink noise, mixup, cutmix, and SpecAugment (masking time or frequency domain).\n\n**Loss functions:** Experiment with different loss functions to deal with class imbalance, such as binary cross-entropy (BCE) loss, focal loss, or a combination of these.\n\n**Model architecture:** Consider using EfficientNet or NFNet as the backbone of your model. You can also try using attention blocks for improved feature extraction.\n\n**Post-processing:** Explore post-processing techniques to improve predictions for shorter time spans.\n\n**External data:** Utilize external data and background noise to improve the robustness of your models.\n\n**Ensembles and pseudo-labeling:** Experiment with ensembling different models and incorporating pseudo-labeling for improving your model's performance.\n\n**Validation strategy:** Use a stratified split to create cross-validation folds, keeping in mind the competition's constraints on computation time and resources during inference.\n\nAnd finally, Keep an eye on recent developments in audio classification and deep learning, such as noise prediction, better pre-processing techniques, and new normalization methods to improve the signal-to-noise ratio.\n\nBy combining these insights and experimenting with different techniques, you can develop a competitive solution for this year's BirdCLEF competition. Good luck!", "votes": null }, { "id": "2206122", "postDate": "04/02/2023 10:06:06", "content": "Thanks for watching! Good Luck in the competition! 🙏
", "rawMarkdown": "Thanks for watching! Good Luck in the competition! 🙏", "votes": null }, { "id": "2206304", "postDate": "04/02/2023 13:14:23", "content": "No few-shot learning or representation learning? Might be interesting considering the number of species with only very few training samples. Just saying…🙂
", "rawMarkdown": "No few-shot learning or representation learning? Might be interesting considering the number of species with only very few training samples. Just saying...🙂", "votes": null }, { "id": "2206366", "postDate": "04/02/2023 13:59:06", "content": "If only we could get ChatGPT plugins to work in Kernels…. :D
\nJokes aside, this is from a livestream about previous winning solutions. Hence that topic didn't come up in the discussion. It was from the last 2 years of BirdCLEF competitions
", "rawMarkdown": "If only we could get ChatGPT plugins to work in Kernels.... :D \n\nJokes aside, this is from a livestream about previous winning solutions. Hence that topic didn't come up in the discussion. It was from the last 2 years of BirdCLEF competitions", "votes": null }, { "id": "2206478", "postDate": "04/02/2023 15:25:26", "content": "Ah…too bad. Might have helped in previous years too…
", "rawMarkdown": "Ah...too bad. Might have helped in previous years too...", "votes": null } ], "comments": [ { "id": 2206122, "author_name": "init27", "author_url": "", "post_date": "04/02/2023 10:06:06", "content": "Thanks for watching! Good Luck in the competition! 🙏
", "votes": null, "replies": [] }, { "id": 2206304, "author_name": "stefankahl", "author_url": "", "post_date": "04/02/2023 13:14:23", "content": "No few-shot learning or representation learning? Might be interesting considering the number of species with only very few training samples. Just saying…🙂
", "votes": null, "replies": [ { "id": 2206366, "author_name": "init27", "author_url": "", "post_date": "04/02/2023 13:59:06", "content": "If only we could get ChatGPT plugins to work in Kernels…. :D
\nJokes aside, this is from a livestream about previous winning solutions. Hence that topic didn't come up in the discussion. It was from the last 2 years of BirdCLEF competitions
", "votes": null, "replies": [ { "id": 2206478, "author_name": "stefankahl", "author_url": "", "post_date": "04/02/2023 15:25:26", "content": "Ah…too bad. Might have helped in previous years too…
", "votes": null, "replies": [] } ] } ] } ], "raw_markdown_by_id": { "2205485": "Presenting summarization of a conversation between @nischaydnk @init27 where they discussed how to Get started in BirdClef, these are few ideas i could grasp that could be helpful in this years BirdCLEF\n\n[https://www.youtube.com/watch?v=TrpI9ih0Vxs](url)\n\n**Sound Event Detection (SED) architecture:** Many top solutions have used the SED architecture, which generates predictions for the complete audio file as well as for the desired time span (e.g., five seconds). This can be useful for post-processing and improving predictions.\n\n**Pre-processing and feature extraction:** Convert raw audio signals into spectrograms using techniques like log-mel transformation. This helps in converting audio data into images that can be fed into a neural network.\n\n**Augmentations:** Apply augmentations to both audio files and spectrograms. Examples include pink noise, mixup, cutmix, and SpecAugment (masking time or frequency domain).\n\n**Loss functions:** Experiment with different loss functions to deal with class imbalance, such as binary cross-entropy (BCE) loss, focal loss, or a combination of these.\n\n**Model architecture:** Consider using EfficientNet or NFNet as the backbone of your model. You can also try using attention blocks for improved feature extraction.\n\n**Post-processing:** Explore post-processing techniques to improve predictions for shorter time spans.\n\n**External data:** Utilize external data and background noise to improve the robustness of your models.\n\n**Ensembles and pseudo-labeling:** Experiment with ensembling different models and incorporating pseudo-labeling for improving your model's performance.\n\n**Validation strategy:** Use a stratified split to create cross-validation folds, keeping in mind the competition's constraints on computation time and resources during inference.\n\nAnd finally, Keep an eye on recent developments in audio classification and deep learning, such as noise prediction, better pre-processing techniques, and new normalization methods to improve the signal-to-noise ratio.\n\nBy combining these insights and experimenting with different techniques, you can develop a competitive solution for this year's BirdCLEF competition. Good luck!", "2206122": "Thanks for watching! Good Luck in the competition! 🙏", "2206304": "No few-shot learning or representation learning? Might be interesting considering the number of species with only very few training samples. Just saying...🙂", "2206366": "If only we could get ChatGPT plugins to work in Kernels.... :D \n\nJokes aside, this is from a livestream about previous winning solutions. Hence that topic didn't come up in the discussion. It was from the last 2 years of BirdCLEF competitions", "2206478": "Ah...too bad. Might have helped in previous years too..." }, "source": "meta" }