Abstract
Abstract This paper proposes a query by singing/humming (QBSH) method based on locality sensitive hashing (LSH).The QBSH system allows a user to sing or hum a song, and the system compares this song with the songs in the database and returns the most similar song to the user. First, we perform linear scaling to the segmented pitch vector of the user-sung or hummed tune in order to maximize the performance of LSH. LSH is then used to efficiently find relatively similar song segments in the database, and the corresponding songs of these song segments are labelled as candidate songs. Lastly the input pitch vector is compared with the candidate song segments to obtain the top-10 list. In our experiments, we use a dataset with 454 input queries and 2048 MIDI songs in the database. Our method achieves a 61.23% top-1 accuracy, 76.87% top-10 accuracy. The average computation time is 3.87 second per query. Keywords: QBSH、Linear Scaling、LSH