{"action":"created","issue":{"url":"https://api.github.com/repos/pytorch/audio/issues/30","repository_url":"https://api.github.com/repos/pytorch/audio","labels_url":"https://api.github.com/repos/pytorch/audio/issues/30/labels{/name}","comments_url":"https://api.github.com/repos/pytorch/audio/issues/30/comments","events_url":"https://api.github.com/repos/pytorch/audio/issues/30/events","html_url":"https://github.com/pytorch/audio/issues/30","id":297998910,"number":30,"title":"reduce aliasing in downsampling","user":{"login":"faroit","id":72940,"avatar_url":"https://avatars3.githubusercontent.com/u/72940?v=4","gravatar_id":"","url":"https://api.github.com/users/faroit","html_url":"https://github.com/faroit","followers_url":"https://api.github.com/users/faroit/followers","following_url":"https://api.github.com/users/faroit/following{/other_user}","gists_url":"https://api.github.com/users/faroit/gists{/gist_id}","starred_url":"https://api.github.com/users/faroit/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/faroit/subscriptions","organizations_url":"https://api.github.com/users/faroit/orgs","repos_url":"https://api.github.com/users/faroit/repos","events_url":"https://api.github.com/users/faroit/events{/privacy}","received_events_url":"https://api.github.com/users/faroit/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2018-02-17T11:44:07Z","updated_at":"2018-02-19T21:39:27Z","closed_at":null,"author_association":"NONE","body":"Currently the VCTK dataset loader does [implement](https://github.com/pytorch/audio/blob/master/torchaudio/datasets/vctk.py#L42) downsampling without low-pass filtering (=decimation). This results in severe aliasing artifacts and [should be avoided](https://en.wikipedia.org/wiki/Decimation_(signal_processing)). \r\n\r\nI would propose to add [resampy](https://github.com/bmcfee/resampy) as a dependency and include this in the base modules so that dataset loaders can utilize a high quality resampling. \r\n\r\nI can compile a PR if you like this proposal"},"comment":{"url":"https://api.github.com/repos/pytorch/audio/issues/comments/366808918","html_url":"https://github.com/pytorch/audio/issues/30#issuecomment-366808918","issue_url":"https://api.github.com/repos/pytorch/audio/issues/30","id":366808918,"user":{"login":"faroit","id":72940,"avatar_url":"https://avatars3.githubusercontent.com/u/72940?v=4","gravatar_id":"","url":"https://api.github.com/users/faroit","html_url":"https://github.com/faroit","followers_url":"https://api.github.com/users/faroit/followers","following_url":"https://api.github.com/users/faroit/following{/other_user}","gists_url":"https://api.github.com/users/faroit/gists{/gist_id}","starred_url":"https://api.github.com/users/faroit/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/faroit/subscriptions","organizations_url":"https://api.github.com/users/faroit/orgs","repos_url":"https://api.github.com/users/faroit/repos","events_url":"https://api.github.com/users/faroit/events{/privacy}","received_events_url":"https://api.github.com/users/faroit/received_events","type":"User","site_admin":false},"created_at":"2018-02-19T21:39:27Z","updated_at":"2018-02-19T21:39:27Z","author_association":"NONE","body":"I think it depends on what the future of torchaudio should be.\r\nFor many basic transforms like STFT it would be possible and favorable to implement them purely in pytorch so that users would benefit from a gpu speedup (See [https://github.com/keunwoochoi/kapre](kapre) from @keunwoochoi). But then, for many advanced operations or data augmentations, users would still use librosa or muda anyway and I don't think it makes sense to build a wrapper for librosa..."}}
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