Improved Query by Humming System Using the Tempo and Frequency Information and Advanced Onset Detection and Melody Matching Methods

In recent years, voice signal system has been used in a wide range. The techniques include voice signal analysis, feature extraction, voice synthesis and compression. Applying these techniques to the query by humming (QBH) system, we do not need to know the traditional concrete descriptions like song name, singer or lyrics. Instead, we can use the melody and tempo information to search a song. The music database nowadays is large and the search result should be revealed in a short time, so the system efficiency is as important as the effect. A QBH system includes three parts: input data, music database and matching algorithm. Input data of the QBH system is usually the humming or singing from human. The database is a collection of songs provided for search. The system first extract the features of input signal like pitch and length of notes, then use the matching algorithm to compare with the songs in database. The QBH system compares the input signal and database to list a ranking of possible songs by the matching score. However, people usually cannot sing perfectly just as the reference song. Also the singing style is different from person to person. A good QBH system should be able to deal with all possible problems for amateur singing. Generally speaking, the melody matching at least two types: the note-based and the frame-based method. The advantage of note-based system is its efficiency while the frame-based system is more effective. In this paper, we use the note-based method. We proposed an advanced onset detection and improved melody matching system to improve the QBH system. Besides, we use our own pitch estimation method to estimate the fundamental frequency. In our experiment, we show the fact that our proposed onset detection has the best performance than other methods. However, the future work should make an effort to improve the system efficiency and effect further since the database in the real world is huge.