Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm

Nam, Gi Pyo; Park, Kang Ryoung

doi:10.1155/2015/176091

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Therefore, we compared the performance of method [16] to that of our method. In addition, we compared the performance of other method [26] to that of our method. In [26], they proposed the QBH system based on the multi-stage matching like [16], but they used linear scaling (LS) and quantized DTW as the coarse matching and precise matching, respectively.…”

Section: Resultsmentioning

confidence: 99%

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

Nam

Park

2015

Symmetry

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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.

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Section: Resultsmentioning

confidence: 99%

“…In [26], they proposed the QBH system based on the multi-stage matching like [16], but they used linear scaling (LS) and quantized DTW as the coarse matching and precise matching, respectively. As shown in Tables 13 and 14, we can confirm that our method outperforms previous methods [16,26]. …”

Section: Resultsmentioning

confidence: 99%

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

Nam

Park

2015

Symmetry

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“…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.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

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

2013

KSII TIIS

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The increase in the number of music files in smart phone and MP3 player makes it difficult to find the music files which people want. So, Query-by-Singing/Humming (QbSH) systems have been developed to retrieve music from a user's humming or singing without having to know detailed information about the title or singer of song. Most previous researches on QbSH have been conducted using musical instrument digital interface (MIDI) files as reference songs. However, the production of MIDI files is a time-consuming process. In addition, more and more music files are newly published with the development of music market. Consequently, the method of using the more common MPEG-1 audio layer 3 (MP3) files for reference songs is considered as an alternative. However, there is little previous research on QbSH with MP3 files because an MP3 file has a different waveform due to background music and multiple (polyphonic) melodies compared to the humming/singing query. To overcome these problems, we propose a new QbSH method using MP3 files on mobile device. This research is novel in four ways. First, this is the first research on QbSH using MP3 files as reference songs. Second, the start and end positions on the MP3 file to be matched are estimated by using multi-layered perceptron (MLP) prior to performing the matching with humming/singing query file. Third, for more accurate results, four MLPs are used, which produce the start and end positions for dynamic time warping (DTW) matching algorithm, and those for chroma-based DTW algorithm, respectively. Fourth, two matching scores by the DTW and chroma-based DTW algorithms are combined by using PRODUCT rule, through which a higher matching accuracy is obtained. Experimental results with AFA MP3 database show that the accuracy (Top 1 accuracy of 98%, with an MRR of 0.989) of the proposed method is much higher than that of other methods. We also showed the effectiveness of the proposed system on consumer mobile device.

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Advanced query by humming system using diffused hidden Markov model and tempo based dynamic programming

Lin

Ding

2016

2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

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Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm

Cited by 4 publications

References 15 publications

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

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

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

Advanced query by humming system using diffused hidden Markov model and tempo based dynamic programming

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