Abstract
We introduce two methods for improving melody recognition rate by filtering bad queries in query by singing/humming (QBSH) system. One is “Filtration using Gaussian Mixture Model (GMM)”, and the other is “Filtration using features”. “Filtration using GMM” involves training a GMM by our discovered features and using the trained GMM to compute log-likelihood ratio to decide the threshold of filtration. “Filtration using features” directly uses the results of each feature to choose thresholds to reject bad queries. The value of a threshold is decided by the sum of false rejection rate and false acceptance rate.In this paper, we use three melody recognition methods, which are linear scaling (LS), dynamic time warping (DTW), and the combination of LS and DTW, to recognize songs from the position of head or anywhere. We use five features: mean volume, mean pitch, mean clarity, mean ratio of pitch, and number of frames. The training data and test data are derived from the results of melody recognition methods. Because too high rejective rate may cause unfriendly feeling to users, we only took the results with rejection rate under 30% in our experiment. The results show that the both proposed systems improve at least 1% recognition rate for each melody recognition method. The best result improves 6.45% recognition rate with 14.01% rejection rate by filtration using GMM to reject the songs recognized by LS from the position of anywhere. Our proposed methods successfully accomplish the elimination of bad queries and improve the recognition rate at a certain degree.