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Iv.","email":"dfcreative@gmail.com"},"license":"MIT","homepage":"https://github.com/audiojs/mir#readme","keywords":["audio","dsp","mir","similarity"],"repository":{"type":"git","url":"git+https://github.com/audiojs/mir.git","directory":"packages/mir-similarity"},"description":"Audio similarity — distance metric between recordings","maintainers":[{"name":"jamen","email":"jamenmarz@gmail.com"},{"name":"dfcreative","email":"df.creative@gmail.com"},{"name":"dy","email":"df.creative@gmail.com"}],"readme":"# @audio/mir-similarity\n\n> Audio similarity — timbral distance from frame-MFCC statistics (mean + variance, symmetrized) blended with harmonic distance from mean-chroma cosine. Classical single-Gaussian MFCC baseline (Logan/Aucouturier lineage).\n\n`npm install @audio/mir-similarity`\n\n```js\nimport similarity from '@audio/mir-similarity'\n\nlet r = similarity(takeA, takeB, { fs: 44100 })\n// { score: 0..1, timbre: 0..1, harmony: 0..1 }\n```\n\nOptions: - `fs` — sample rate (default 44100, Hz) · `weight` — timbre vs. harmony blend, 1 = timbre only (default 0.5)\n\nAlso exported as an `audio.js` stat manifest (`./audio` — pass the comparison take as `{ ref }`).\n\nPart of [@audio/mir](https://github.com/audiojs/mir).\n","readmeFilename":"README.md"}