{"id":13583890,"url":"https://github.com/swainshashwat/Audio-Classification-using-Deep-Learning","last_synced_at":"2025-04-06T21:33:18.325Z","repository":{"id":69759567,"uuid":"130747229","full_name":"swainshashwat/Audio-Classification-using-Deep-Learning","owner":"swainshashwat","description":"Classifying 10 different categories of Sound using Deep Learning.","archived":false,"fork":false,"pushed_at":"2018-07-21T18:31:58.000Z","size":2011,"stargazers_count":25,"open_issues_count":0,"forks_count":13,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-11-06T00:39:34.594Z","etag":null,"topics":["audio-processing","deep-learning","deep-neural-networks","keras","keras-classification-models","python3"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/swainshashwat.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-04-23T19:36:16.000Z","updated_at":"2024-10-11T07:50:46.000Z","dependencies_parsed_at":null,"dependency_job_id":"c31f8be2-662a-446d-8e28-901feee6d35b","html_url":"https://github.com/swainshashwat/Audio-Classification-using-Deep-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/swainshashwat%2FAudio-Classification-using-Deep-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/swainshashwat%2FAudio-Classification-using-Deep-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/swainshashwat%2FAudio-Classification-using-Deep-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/swainshashwat%2FAudio-Classification-using-Deep-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/swainshashwat","download_url":"https://codeload.github.com/swainshashwat/Audio-Classification-using-Deep-Learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247556722,"owners_count":20958021,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["audio-processing","deep-learning","deep-neural-networks","keras","keras-classification-models","python3"],"created_at":"2024-08-01T15:03:52.712Z","updated_at":"2025-04-06T21:33:17.952Z","avatar_url":"https://github.com/swainshashwat.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# Audio-Classification-using-Deep-Learning\nClassifying 10 different categories of Urban Sounds using Deep Learning.\n\nThe audio files can be downloaded from the following link: \nhttps://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU\n\n\n## IMPORTANT: The folders should be arranged in the following manner: \nDir of train label: sounds/labels/train.csv\n\nDir of test label: sounds/labels/test.csv\n\nDir of train sounds:sounds/train/train_sound/ (audio files in .wav format)\n\nDir of train sounds:sounds/test/test_sound/ (audio files in .wav format)\n\n\n### The train folder are labelled\n### The test folder aren't labelled\n\nWe separate one audio signal into 3 to actually load the data into a machine understandable format. \nFor this, we simply take values after every specific time steps. \nFor example; in a 2 second audio file, we extract values at half a second. \n![Alt Text](https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/08/23210623/sound.png)\nThis is called sampling of audio data, and the rate at which it is sampled is called the sampling rate.\n\nDifferent pure signals, which can now be represented as three unique values in frequency domain.\n\nThere are a few more ways in which audio data can be represented, for example. using MFCs (Mel-Frequency cepstrums).\nThese are nothing but different ways to represent the data.\n\nNext we extract features from this audio representations, so that our Deep Learning model can work on these features and perform the task it is designed for..\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswainshashwat%2FAudio-Classification-using-Deep-Learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fswainshashwat%2FAudio-Classification-using-Deep-Learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fswainshashwat%2FAudio-Classification-using-Deep-Learning/lists"}