There is a post from @ultron in Rainforest regarding the research papers which is worth checking out .
\nOrginal Post : https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/197740
\nSnippets from above post for quick reference
\nPANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
\nhttps://arxiv.org/pdf/1912.10211.pdf
Bird Species Identification in Soundscapes
\nhttp://ceur-ws.org/Vol-2380/paper_86.pdf
\nBird recognition - review of useful resources
\nhttps://github.com/AgaMiko/bird-recognition-review
\nA Closer Look at Weak Label Learning for Audio Events
\nhttps://arxiv.org/pdf/1804.09288.pdf
\nCONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS
\nhttps://arxiv.org/pdf/2010.10915.pdf
\nmixup: BEYOND EMPIRICAL RISK MINIMIZATION
\nhttps://arxiv.org/pdf/1710.09412.pdf
\nSpecAugment: A New Data Augmentation Method for Automatic Speech Recognition
\nhttps://arxiv.org/pdf/1904.08779.pdf
\nAutomatically identifying, counting, and describing wild animals in camera-trap images with deep learning
\nhttps://www.pnas.org/content/115/25/E5716/
\nAutomatic acoustic identification of individuals in multiple species: improving identification across recording conditions
\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505557/
\nAutomatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
\nhttps://arxiv.org/pdf/1807.05812.pdf
\nAlso check out extensive resource compilation for audio data
\nhttps://www.kaggle.com/c/rfcx-species-audio-detection/discussion/199619
", "messages": [ { "id": 1259820, "postDate": "2021-04-01T17:18:50.787Z", "content": "There is a post from @ultron in Rainforest regarding the research papers which is worth checking out .
\nOrginal Post : https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/197740
\nSnippets from above post for quick reference
\nPANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition
\nhttps://arxiv.org/pdf/1912.10211.pdf
Bird Species Identification in Soundscapes
\nhttp://ceur-ws.org/Vol-2380/paper_86.pdf
\nBird recognition - review of useful resources
\nhttps://github.com/AgaMiko/bird-recognition-review
\nA Closer Look at Weak Label Learning for Audio Events
\nhttps://arxiv.org/pdf/1804.09288.pdf
\nCONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS
\nhttps://arxiv.org/pdf/2010.10915.pdf
\nmixup: BEYOND EMPIRICAL RISK MINIMIZATION
\nhttps://arxiv.org/pdf/1710.09412.pdf
\nSpecAugment: A New Data Augmentation Method for Automatic Speech Recognition
\nhttps://arxiv.org/pdf/1904.08779.pdf
\nAutomatically identifying, counting, and describing wild animals in camera-trap images with deep learning
\nhttps://www.pnas.org/content/115/25/E5716/
\nAutomatic acoustic identification of individuals in multiple species: improving identification across recording conditions
\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505557/
\nAutomatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
\nhttps://arxiv.org/pdf/1807.05812.pdf
\nAlso check out extensive resource compilation for audio data
\nhttps://www.kaggle.com/c/rfcx-species-audio-detection/discussion/199619
", "rawMarkdown": "\n\nThere is a post from @ultron in Rainforest regarding the research papers which is worth checking out .\n\nOrginal Post : https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/197740\n\nSnippets from above post for quick reference\n\n**PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition**\nhttps://arxiv.org/pdf/1912.10211.pdf\n\n**Bird Species Identification in Soundscapes**\n\nhttp://ceur-ws.org/Vol-2380/paper_86.pdf\n\n**Bird recognition - review of useful resources**\n\nhttps://github.com/AgaMiko/bird-recognition-review\n\n**A Closer Look at Weak Label Learning for Audio Events**\n\nhttps://arxiv.org/pdf/1804.09288.pdf\n\n**CONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS**\n\nhttps://arxiv.org/pdf/2010.10915.pdf\n\n**mixup: BEYOND EMPIRICAL RISK MINIMIZATION**\n\nhttps://arxiv.org/pdf/1710.09412.pdf\n\n**SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition**\n\nhttps://arxiv.org/pdf/1904.08779.pdf\n\n**Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning**\n\nhttps://www.pnas.org/content/115/25/E5716/\n\n**Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions**\n\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505557/\n\n**Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge**\n\nhttps://arxiv.org/pdf/1807.05812.pdf\n\n**Also check out extensive resource compilation for audio data** \n\nhttps://www.kaggle.com/c/rfcx-species-audio-detection/discussion/199619", "votes": 24 } ], "comments": [], "raw_markdown_by_id": { "1259820": "\n\nThere is a post from @ultron in Rainforest regarding the research papers which is worth checking out .\n\nOrginal Post : https://www.kaggle.com/c/rfcx-species-audio-detection/discussion/197740\n\nSnippets from above post for quick reference\n\n**PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition**\nhttps://arxiv.org/pdf/1912.10211.pdf\n\n**Bird Species Identification in Soundscapes**\n\nhttp://ceur-ws.org/Vol-2380/paper_86.pdf\n\n**Bird recognition - review of useful resources**\n\nhttps://github.com/AgaMiko/bird-recognition-review\n\n**A Closer Look at Weak Label Learning for Audio Events**\n\nhttps://arxiv.org/pdf/1804.09288.pdf\n\n**CONTRASTIVE LEARNING OF GENERAL-PURPOSE AUDIO REPRESENTATIONS**\n\nhttps://arxiv.org/pdf/2010.10915.pdf\n\n**mixup: BEYOND EMPIRICAL RISK MINIMIZATION**\n\nhttps://arxiv.org/pdf/1710.09412.pdf\n\n**SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition**\n\nhttps://arxiv.org/pdf/1904.08779.pdf\n\n**Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning**\n\nhttps://www.pnas.org/content/115/25/E5716/\n\n**Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions**\n\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6505557/\n\n**Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge**\n\nhttps://arxiv.org/pdf/1807.05812.pdf\n\n**Also check out extensive resource compilation for audio data** \n\nhttps://www.kaggle.com/c/rfcx-species-audio-detection/discussion/199619" } }