{"id":20580624,"url":"https://github.com/pmhalvor/music-genre-classifier","last_synced_at":"2026-03-19T16:30:57.952Z","repository":{"id":207961486,"uuid":"720503088","full_name":"pmhalvor/music-genre-classifier","owner":"pmhalvor","description":"An experiment aimed to compare a range of ML-based music classifiers","archived":false,"fork":false,"pushed_at":"2024-02-03T07:27:08.000Z","size":48953,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T11:51:27.756Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","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/pmhalvor.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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-11-18T17:25:35.000Z","updated_at":"2023-11-27T13:30:02.000Z","dependencies_parsed_at":"2024-02-03T08:44:14.157Z","dependency_job_id":null,"html_url":"https://github.com/pmhalvor/music-genre-classifier","commit_stats":null,"previous_names":["pmhalvor/music-genre-classifier"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pmhalvor/music-genre-classifier","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pmhalvor%2Fmusic-genre-classifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pmhalvor%2Fmusic-genre-classifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pmhalvor%2Fmusic-genre-classifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pmhalvor%2Fmusic-genre-classifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pmhalvor","download_url":"https://codeload.github.com/pmhalvor/music-genre-classifier/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pmhalvor%2Fmusic-genre-classifier/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273859034,"owners_count":25180816,"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","status":"online","status_checked_at":"2025-09-06T02:00:13.247Z","response_time":2576,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2024-11-16T06:24:36.249Z","updated_at":"2026-02-13T11:55:42.192Z","avatar_url":"https://github.com/pmhalvor.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# music-genre-classifier\nAn experiment aimed to compare a range of ML-based music classifiers\n\nWe test three different types of machine learning classifiers (traditional, simple neural networks, and pre-trained models) on the [GTZAN Dataset - Music Genre Classification](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification). \n\n\n# Classifier Types\nWe want to measure performance between complex models and simpler ones. The experiment aims to  ultimately show how much compute resources are needed for good classifications. \n\n## Traditional\nWe train and briefly tune 3 traditional machine learning models: random forest, support vector machines, and k-nearest neighbors. \nThen measure the performance based on their macro F-1 scores. We also review category misclassification rate per category using a confusion matrix. \n\n## Neural networks\nWe then train three simple neural networks: feed-forward (mlp), cnn, rnn. This is mainly to establish a lower limit for performance of neural architectures. Minimal fine tuning should be done here to save resources and maintain the baseline score. \n\n## Pretrained models \nThere exists many open-source audio/music classifiers. We will select one publicly available through HuggingFace. When choosing a model here, it is important to have a reproducible preprocessing step. Once this preprocessing is set, then we can re-run the previous experiments, but with the same format of data as used here, for a fair comparison. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpmhalvor%2Fmusic-genre-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpmhalvor%2Fmusic-genre-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpmhalvor%2Fmusic-genre-classifier/lists"}