Study On Real-time Rhythmic Information Retrieval Of Musical Audio Signal And The System Implementation

Music is the one of the greatest arts. It has a profound impact on the activity of human spirit. In recent years, digital music has become deeply diffused among the diverse aspects of human activity owing to the rapid developments of digital audio processing technology and multimedia technology, which continuously affects the manner of the music appreciation while it increases the demand for the music appreciation. In the meantime, digital music information retrieval (MIR) technology has been well developed. At home and abroad, more and more scientific researchers and institutes carried out research works in depth on musical characteristics retrieval and musical content understanding.To promote the growth and development of MIR as well as its relative technology applications, we executed an extensive investigation and made a deep research on it. We have already designed a real-time system capable of predicating beats and tracking musical rhythmic information such as tempo and meter with the purpose of enabling the computer to hear the rhythmic structure of music as human beings do.In the process of designing our system, we firstly preprocessed specially the raw audio signal so as to obtain the onset detection function of musical events, and then took this function sequence as the input into the musical rhythmic retrieval model. Utilizing the method based on the unbiased autocorrelation function and comb sampling function template, we estimated the beat period and beat phase, tracking tempo and recognizing meter at the same time. Because of the sensitivity to big tempo change, the accuracy of the predicted beats was often unsatisfied, leading to a low reliability of the system. In order to increase the accuracy and reliability of real-time prediction model, we devised an approach of analyzing the past audio data using Viterbi decoding algorithm and a method based on dynamic programming to deduce the optimal beat sequence. After that, we improved the real-time prediction model of musical rhythmic information.Eventually, we carried out experiments and evaluated our system by several musical audio experts labeled by ground truth. The results turn out that the improved model has achieved the anticipated goal of increasing prediction accuracy.