{"id":27097506,"url":"https://github.com/dschwertfeger/cbar","last_synced_at":"2025-04-06T10:48:12.315Z","repository":{"id":62561094,"uuid":"80744062","full_name":"dschwertfeger/cbar","owner":"dschwertfeger","description":"A Python package for content-based audio retrieval with text queries.","archived":false,"fork":false,"pushed_at":"2017-03-27T14:12:20.000Z","size":878,"stargazers_count":5,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-20T21:03:53.111Z","etag":null,"topics":["audio","content-based","gradient-descent","machine-learning","retrieval","riemann"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dschwertfeger.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-02-02T16:28:38.000Z","updated_at":"2021-08-27T17:14:55.000Z","dependencies_parsed_at":"2022-11-03T14:45:44.711Z","dependency_job_id":null,"html_url":"https://github.com/dschwertfeger/cbar","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dschwertfeger%2Fcbar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dschwertfeger%2Fcbar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dschwertfeger%2Fcbar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dschwertfeger%2Fcbar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dschwertfeger","download_url":"https://codeload.github.com/dschwertfeger/cbar/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247471397,"owners_count":20944154,"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","content-based","gradient-descent","machine-learning","retrieval","riemann"],"created_at":"2025-04-06T10:48:11.703Z","updated_at":"2025-04-06T10:48:12.305Z","avatar_url":"https://github.com/dschwertfeger.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"CBAR: Content-Based Audio Retrieval in Python\n=============================================\n\nCBAR is a Python package for content-based audio retrieval with text queries.\n\nIt contains two retrieval methods. The Passive-Aggressive Model for Image Retrieval (PAMIR) was initially\ndeveloped in the context of an image retrieval application [1]_ but has been\nproven to work equally well for audio retrieval applications [2]_.\n\nThe second approach combines on a Low-Rank Retraction Algorithm (LORETA) [3]_\nand the Weighted Approximate-Rank Pairwise loss (WARP loss) [4]_ to efficiently\ninfer the model parameters. A similar algorithm, constrained to the context\nof finding similar items of the same kind (similarity search), has been shown to\nwork well on image and audio datasets [5]_.\n\n\nGetting started\n---------------\n\nJump straight to the :doc:`CAL500 quickstart \u003cnotebooks/quickstart\u003e` guide\nif you are impatient.\n\n\nInstallation\n------------\n\nThe latest release of CBAR can be installed from PyPI using ``pip``.\n\n.. code:: bash\n\n    pip install cbar\n\n\nDependencies\n------------\n\nCBAR is tested on Python 2.7 and depends on NumPy, SciPy, Pandas, NLTK, and\nscikit-learn. See ``setup.py`` for version information.\n\n\nDocumentation\n-------------\n\nhttps://dschwertfeger.github.io/cbar\n\n\nOn GitHub\n---------\n\nhttps://github.com/dschwertfeger/cbar\n\n\nReferences\n----------\n\n\n.. [1] Grangier, D. and Bengio, S., 2008. `A discriminative kernel-based\n        approach to rank images from text queries.\n        \u003chttps://infoscience.epfl.ch/record/146417/files/grangier-rr07-38.pdf\u003e`_\n        IEEE transactions on pattern analysis and machine intelligence, 30(8),\n        pp.1371-1384.\n\n.. [2] Chechik, G., Ie, E., Rehn, M., Bengio, S. and Lyon, D., 2008,\n        October. `Large-scale content-based audio retrieval from text queries.\n        \u003chttps://static.googleusercontent.com/media/research.google.com/en//pubs/archive/33429.pdf\u003e`_\n        In Proceedings of the 1st ACM international conference on Multimedia\n        information retrieval (pp. 105-112). ACM.\n\n.. [3] Shalit, U., Weinshall, D. and Chechik, G., 2012. `Online learning in\n        the embedded manifold of low-rank matrices.\n        \u003chttp://www.jmlr.org/papers/volume13/shalit12a/shalit12a.pdf\u003e`_\n        Journal of Machine Learning Research, 13(Feb), pp.429-458.\n\n.. [4] Weston, J., Bengio, S. and Usunier, N., 2010. `Large scale image\n        annotation: learning to rank with joint word-image embeddings.\n        \u003chttps://research.google.com/pubs/archive/35780.pdf\u003e`_\n        Machine learning, 81(1), pp.21-35.\n\n.. [5] Lim, D. and Lanckriet, G., 2014. `Efficient Learning of Mahalanobis\n        Metrics for Ranking.\n        \u003chttp://www.jmlr.org/proceedings/papers/v32/lim14.pdf\u003e`_\n        In Proceedings of The 31st International Conference on Machine Learning\n        (pp. 1980-1988).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdschwertfeger%2Fcbar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdschwertfeger%2Fcbar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdschwertfeger%2Fcbar/lists"}