A Music Information System Based on Improved Melody Contour Extraction

Phiwma, Nattha; Sanguansat, Parinya

doi:10.1109/icsap.2010.8

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…The following algorithm describes how to extract pitch from humming sound to obtain the melody contour. Melody Contour Extraction, we have proposed in [16]. Let m represents melody contour and let p be the pitch.…”

Section: Melody Contour Extraction Algorithmmentioning

confidence: 99%

Untitled

2013

IAJIT

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In this paper, we propose new methods for feature extraction and soft majority voting to adjust efficiency and accuracy of music retrieval. For our work, the input is humming sound which is sound wave and Musical Instrument Digital Interface (MIDI) is used as the reference song in database. A critical issue of humming sound are variation such as duration, sound, tempo, key, and noise interference from both environment and acquisition instruments. Besides all the problems of humming sound we have mentioned earlier, whether humming sound and MIDI in different domain which will make the difficulty for two domains to compare each other. However, to make these two in the same domain, we convert them into the frequency domain. Our approach starts from pre-processing by using features for note segmentation by humming sound. The process consists of four steps as follows: Firstly, the MIDI is already a sequence of pitch while the pitch in humming sound is needed to extract by Subharmonic-to-Harmonic (SHR). Subsequently, the extracted pitch can be used to calculate all above attributes and then multiple classifiers are applied to classify the multiple subsets of these features. Afterwards, the subset contain the multiple attributes, Multi-Dimensional Dynamic Time Warping (MD-DTW) is used for similarity measurement. Finally, Nearest Neighbours (NN) and soft majority voting are used to obtain the retrieval results in case of equal scores. From the experiments, to achieve 100% accuracy rate at the early top-n rank in retrieving, the appropriate feature set should consist of five classifiers.

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Section: Melody Contour Extraction Algorithmmentioning

confidence: 99%

Untitled

2013

IAJIT

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“…For instance, removing the percussion from a given recording led to significant improvements in a subsequent harmonic analysis [1]. Similarly, many existing query-by-humming systems rely on methods for separating the main melody from a polyphonic music recording [2,3,4]. Furthermore, extracting sounds with a similar spectral envelope is a fundamental step in the recognition of instruments in polyphonic recordings [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient data adaption for musical source separation methods based on parametric models

Ewert

Müller

Sandler

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The decomposition of a monaural audio recording into musically meaningful sound sources constitutes one of the central research topics in music signal processing. In this context, many recent approaches employ parametric models that describe a recording in a highly structured and musically informed way. However, a major drawback of such approaches is that the parameter learning process typically relies on computationally expensive data adaption methods. In this paper, the main idea is to distinguish parameters in which the model is linear explicitly from the remaining parameters. Exploiting the linearity we translate the data adaption problem into a sparse linear least squares problem with box constraints (SLLS-BC), a class of problems for which highly efficient numerical solvers exist. First experiments show that our approach based on modified SLLS-BC methods accelerates the data adaption by a factor of four or more compared to recently proposed methods.

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Synthesizing the note-specific atoms based on their fundamental frequency, used for single-channel musical source separation

Azamian

Kabir

2019

Multimed Tools Appl

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A Music Information System Based on Improved Melody Contour Extraction

Cited by 5 publications

References 27 publications

Untitled

Untitled

Efficient data adaption for musical source separation methods based on parametric models

Synthesizing the note-specific atoms based on their fundamental frequency, used for single-channel musical source separation

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