The emergence of audio and video data types in databases will require new information retrieval methods adapted to the specific characteristics and needs of these data types. An effective and natural way of querying a musical audio database is by humming the tune of a song. This project implements a query by humming system based on locality sensitive hashing (LSH). This method constructs an index of melodic fragments by extracting pitch vectors from a database of melodies. In retrieval, the method automatically transcribes a sung query into notes and then extracts pitch vectors similarly to the index construction. For each query pitch vector, the method searches for similar melodic fragments in the database to obtain a list of candidate melodies. The result of this project is a ranked list of songs based on the query hummed by the user. The relevancy of this project is its use in content-based audio retrieval systems.