Abhishek/peragro GSoC2016Proposal: Difference between revisions

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<u> '''Vector Space Model(VSM)''' </u>
<u> '''Vector Space Model(VSM)''' </u>
<p> This is a basic yet effective model. The idea is to represent every audio entity in vector form in feature space. Given a query audio, learn the vector representation of it and then find the similarity between the query vector and indexed audio vectors. More the similarity more is its relevance. Cosine similarity measure can be used to calculate relevance score. Features for audio could be tempo, bpm, average volume, genre, mood, fingerprint etc.
<p> This is a basic yet effective model. The idea is to represent every audio entity in vector form in feature space. Given a query audio, learn the vector representation of it and then find the similarity between the query vector and indexed audio vectors. More the similarity more is its relevance. Cosine similarity measure can be used to calculate relevance score. Features for audio could be tempo, bpm, average volume, genre, mood, fingerprint etc. </p>


<u> '''Spectrum analysis(Fingerprint) model''' </u>
<u> '''Spectrum analysis(Fingerprint) model''' </u>
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