https://twubs.com/
ç¾ä»£ä¿³å¥ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹ - ç¾ä»£ä¿³å¥å”会
ç¾ä»£ä¿³å¥å”会 æ±äº¬éƒ½åƒä»£ç”°åŒºå¤–神田6-5-4å•æ¥½ãƒ“ル（外神田）7階 TEL 03-3839-8190 FAX 03-3839-8191 サイトマップ ãŠå•ã„åˆã‚ã›
SimpldR: livedoor Readerをシンプルã«
ã—ã°ã‚‰ãå‰ã‹ã‚‰ãƒ¦ãƒ¼ã‚¶ãƒ¼ãƒ»ã‚¹ã‚¿ã‚¤ãƒ«ã‚·ãƒ¼ãƒˆã¨ãƒ¦ãƒ¼ã‚¶ãƒ¼ãƒ»ã‚¹ã‚¯ãƒªãƒ—トã®ã‚³ãƒ³ãƒ“ãƒãƒ¼ã‚·ãƒ§ãƒ³ã§livedoor Readerをシンプルã«ã—ã¦ä½¿ã†ã‚ˆã†ã«ã—ã¦ã„ã¦ä¸ã€…å¿«é©ãªã®ã§ã¾ã¨ã‚ã¦ã‚¨ãƒ³ãƒˆãƒªã«ã—ã¦ã¿ã‚‹ã€‚ã‚ーボードã§ã‚µã‚¯ã‚µã‚¯æ“作ã§ãã¦å¿«é©ã‚¦ãƒã‚¦ãƒã¨ã‹ã˜ã‚ƒãªãã¦ã‚·ãƒ³ãƒ—ルãªç”»é¢ã§ãƒžã‚¦ã‚¹ãƒ»ãƒ›ã‚¤ãƒ¼ãƒ«ã‚’使ã£ã¦ã²ã‚…ã„ã‚“ã²ã‚…ã„ã‚“èªã‚€æ„Ÿã˜ã€‚ ユーザー・スタイルシート ヘッダã®è‰²ãªã©ã‚’排除ã™ã‚‹ã®ãŒãƒ¡ã‚¤ãƒ³ã€‚userContent.cssã«ä»¥ä¸‹ã®CSSã‚’è¿½åŠ ã™ã‚‹ã€‚ /* livedoor Reader ----------------------------- */ @-moz-document domain("reader.livedoor.com") { * { font-family: "Lucida Grande", "Trebuchet MS", sans-serif !important; } pre, code,
旧・Macã®æ‰‹æ›¸ã説明書 - FC2 BLOG パスワードèªè¨¼
ブãƒã‚° パスワードèªè¨¼ 閲覧ã™ã‚‹ã«ã¯ç®¡ç†äººãŒè¨å®šã—㟠パスワードã®å…¥åŠ›ãŒå¿…è¦ã§ã™ã€‚ 管ç†äººã‹ã‚‰ã®ãƒ¡ãƒƒã‚»ãƒ¼ã‚¸ https://mac-tegaki.comã¸ç§»è»¢ä¸ 閲覧パスワード Copyright © since 1999 FC2 inc. All Rights Reserved.
ARel - hs9587’s diary
二講目㯠ARelセッション #railstokyo 2:45 PM Nov 15th web㧠A Relational Algebra。ã¾ã gemã‚‚ãªãã€ï½‡ï½‰ï½”ã«ã„ãã—ã‹ãªã„ #railstokyo 2:46 PM Nov 15th web㧠ARel http://github.com/brynary/arel #railstokyo 2:51 PM Nov 15th web㧠ActiveRecord ãŒã„ã‚ã„ã‚SQLã¨ã‹æ›¸ã„ã¦ã‚‹éƒ¨åˆ†ã€ãŒã©ã‚“ã©ã‚“ ARel ã«ç½®ãæ›ã‚ã£ã¦ãã¦ã‚‹ã€‚ARel 㯠AR ã®ä¸‹ã®ãƒ¬ã‚¤ãƒ¤ãƒ¼ã§ã€SQLã¨ã‹DBã¨è©±ã‚’ã™ã‚‹ã‚‚ã® #railstokyo 2:57 PM Nov 15th web㧠ARelã€ã‚‚ã£ã¨ä¸Šã®æ–¹ã«å‡ºã¦ã㦠Railsプãƒã‚°ãƒ©ãƒžãƒ¼ãŒã¾ã•ã«è§¦ã‚‹ã¹ãã‚‚ã®ã«ãªã‚‹ã¨ã„ã†è©±ã—ã‚‚ã‚る。ã‚ã—ãŸã¯ã©ã£ã¡ã ? #railstokyo 3:00 PM Nov 15
beaTunes
Doesn't just play music, also listens beaTunes employs sophisticated algorithms to analyze your music for metadata like tempo (BPM), key, color, segments, similarities, loudness, and acoustical fingerprints. It lets you know, what's in all those files! Analysis is a stable foundation for great sounding playlists as well as tag lookup and acoustical duplicate detection. Let science do some work for
Identifying unknown music with Ruby
Ben Bleything inspired me (or rather distracted me from my yak shaving) to get my music library cleaned up and remove duplicates. Unfortunately my duplicates don’t necessarily have ID3 tags, and they may be in different formats, so I wrote a gem called “earworm†which will identify unknown music. First I’ll give you a code sample, then explain how to get earworm working, and finally explain how ea
Fingerprinting - MusicBrainz
Images are currently not being copied over after event or release merges. For now, please make sure to upload any images that should be kept to the merge target beforehand. MusicBrainz has used several audio fingerprinting systems over its lifetime. All of them (so far) work in essentially the same way based on ACR technology. It is generally a two-step process of submission and lookup. First, the
How PUIDs Work - MusicBrainz
Music Analysis vs Fingerprinting There are two processes that MusicIP makes available: MusicAnalysis and audio fingerprinting. Finally there are the PUIDs which are just IDs, no fingerprints. Music Analysis Before a PUID is available for MusicBrainz or Picard to use, Music Analysis must have been performed on a track. MusicAnalysis uses up to 10 minutes of the track and examines all sorts of thing
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
Chrome Extentions by os0x
todeshatena.mov
http://blog.dbtune.org/post/2008/07/01/Echonest-Analyze-XML-to-Music-Ontology-RDF
GitHub - brynary/arel: A Relational Algebra
{{#tags}}- {{label}}
{{/tags}}