Multi-Classifier Based on a Query-by-Singing/Humming System

Abstract

Abstract:With the increase in the number of music files on various devices, it can be difficult to locate a desired file, especially when the title of the song or the name of the singer is not known. We propose a new query-by-singing/humming (QbSH) system that can find music files that match what the user is singing or humming. This research is novel in the following three ways: first, the Fourier descriptor (FD) method is proposed as the first classifier; it transforms the humming or music waveform into the frequency domain. Second, quantized dynamic time warping (QDTW) using symmetrical search space and quantized linear scaling (QLS) are used as the second and third classifiers, respectively, which increase the accuracy of the QbSH system compared to the conventional DTW and LS methods. Third, five classifiers, which include the three already mentioned along with the conventional DTW using symmetrical search space and LS methods, are combined using score level fusion, which further enhances performance. Experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases show that the proposed method outperforms those using a single classifier and other fusion methods.

A Novel Query-by-Singing/Humming Method by Estimating Matching Positions Based on Multi-layered Perceptron

“…The pitch information is extracted from each humming/singing file by a musical note estimation method using spectro-temporal autocorrelation (STA) [4][5] [22][23]. After this step, the extracted pitch features are preprocessed as follows [4][5] [22] [23]. We eliminate all the zero values from the extracted features.…”

“…For good matching between two data sequences of different length, DTW is used. In previous researches, the DTW-based matching is performed by sliding the input humming/singing data based on the reference song [3][4][5] [22], whereas the matching is performed without sliding only at the determined start or end position estimated by the MLPs in this research. For higher matching accuracy, chroma-based DTW is also used [20].…”