A query by humming system based on locality sensitive hashing indexes
Signal Processing
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Using melody and/or lyric to query a music retrieval system is convenient for users but challenging for developers. This paper proposes efficient schemes for realizing key algorithms in such a kind of system. Specifically, we characterize our system by adding lyric to query as follows: A Support Vector Machine (SVM) is employed to distinguish humming queries from singing queries, For a singing query, lyrics of candidates, which are pre-selected by the commonly used melody matching method, are used to dynamically build up the recognition network, A novel fusion strategy, which is based on the classification confidence, is proposed to combine the lyric and melody scores. The experimental results show that error reduction rates as much as 22.9%, 25.0%, 28.7% and 33.5% for mean reciprocal rank (MRR) are achieved by using the proposed method, respectively for four existing query by singing/humming (QBSH) systems.