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Musical Information Retrieval Take 2: Interval Hashing Based Ranking

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Musical Information Retrieval Take 2: Interval Hashing Based Ranking

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Retrieving musical records from a corpus of Information, using an audio input as a query is not an easy task. Various approaches try to solve the problem modelling the query and the corpus of Information as an array of hashes calculated from the chroma features of the audio input.
Scope of this talk is to introduce a novel approach in calculating such hashes, considering the intervals of the most intense pitches of sequential chroma vectors.
Building on the theoretical introduction, a prototype will show you this approach in action with Apache Solr with a sample dataset and the benefits of positional queries.
Challenges and future works will follow up as conclusive considerations.

Retrieving musical records from a corpus of Information, using an audio input as a query is not an easy task. Various approaches try to solve the problem modelling the query and the corpus of Information as an array of hashes calculated from the chroma features of the audio input.
Scope of this talk is to introduce a novel approach in calculating such hashes, considering the intervals of the most intense pitches of sequential chroma vectors.
Building on the theoretical introduction, a prototype will show you this approach in action with Apache Solr with a sample dataset and the benefits of positional queries.
Challenges and future works will follow up as conclusive considerations.

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