Separation of Voice and Music by Harmonic Structure Stability Analysis

Zhang, Yungang; Zhang, Changshui

doi:10.1109/icme.2005.1521485

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Recently, some scholars take use of spectrum matching and spectrum filter technology for music source separation [2] , Computational Auditory Scene Analysis (CASA) restructures the audio stream with the same psychological acoustic characteristics audio sense objects to realize music mix separation [3,4] . Methods based on sinusoidal model extract sinusoidal tracks from some T-F representations of the signal, and then apply grouping rules to assign these tracks to different sources [5,6,7] .…”

Section: Introductionmentioning

confidence: 99%

Music Source Separation Based on Improved Sinusoidal Modeling

Jiang

Chang

2013

AMR

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Traditional sinusoidal modeling can not express spectrum of music signal factually as it only considers pitch characteristic. To solve this problem, an improved sinusoidal modeling of combining spectrum amplitude with pitch characteristic was proposed. Separation algorithm based on improved sinusoidal modeling extracts sinusoidal parameters of mix music signal utilizing sinusoidal analysis technology, and then applies grouping rules to assign these tracks to reconstruct the separated music signal. Simulation experiments demonstrate the feasibility and effectiveness of the proposed improved sinusoidal modeling separate algorithm.

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Section: Introductionmentioning

confidence: 99%

Music Source Separation Based on Improved Sinusoidal Modeling

Jiang

Chang

2013

AMR

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“…Extensions exist on BSS for speaker separation, which are not bounded by this independent number of observations constraint. These solutions typically rely on constraints inspired on the characteristics of voice, such as the regular harmonic structure of voiced phonemes (Zhang and Zhang 2006), a priori knowledge on sound models (Kristjansson et al 2004, Potamitis and Ozerov 2008, Jang and Lee 2004 or by assuming that fundamental frequencies do not overlap (Barry et al 2005). In the context of HS processing these approaches have some disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Noise detection during heart sound recording using periodicity signatures

Kumar

Carvalho

Antunes³

et al. 2011

Physiol. Meas.

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Heart sound is a valuable biosignal for diagnosis of a large set of cardiac diseases. Ambient and physiological noise interference is one of the most usual and highly probable incidents during heart sound acquisition. It tends to change the morphological characteristics of heart sound that may carry important information for heart disease diagnosis. In this paper, we propose a new method applicable in real time to detect ambient and internal body noises manifested in heart sound during acquisition. The algorithm is developed on the basis of the periodic nature of heart sounds and physiologically inspired criteria. A small segment of uncontaminated heart sound exhibiting periodicity in time as well as in the time-frequency domain is first detected and applied as a reference signal in discriminating noise from the sound. The proposed technique has been tested with a database of heart sounds collected from 71 subjects with several types of heart disease inducing several noises during recording. The achieved average sensitivity and specificity are 95.88% and 97.56%, respectively.

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Real-time speech/music classification with a hierarchical oblique decision tree

Wang¹,

Wu²,

Deng³

et al. 2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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Table 1: accuracy (%) of reference classifiers [1] [5] [8] [3] [4] Segmentlevel 91.9 95.4 97 98 99.43 [6] [9] [7] [2] Framelevel 80.9 87 94.2 95In the problem of classification of audio signals, the requirements of low-complexity, high-accuracy and short delay are crucial for some practical scenarios. This paper proposes a method of real-time speech/music classification with a hierarchical oblique decision tree. A set of discrimination features in frequency domain are selected together with a proposed simple harmonic structure stability feature, which is based on a rough estimation of the harmonic structure. A feature subset selection tool is used to select a subset of short and long term features to feed into a hierarchical oblique decision tree classifier. The method is evaluated and compared with the open loop selection mode in AMR-WB+. Experiments show the proposed approach gives a better performance (98.3%) compared to other prevailing approaches. In particular, it comes with promising short delay of 10 ms and low complexity of 1 wmops.

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Separation of Voice and Music by Harmonic Structure Stability Analysis

Cited by 6 publications

References 8 publications

Music Source Separation Based on Improved Sinusoidal Modeling

Music Source Separation Based on Improved Sinusoidal Modeling

Noise detection during heart sound recording using periodicity signatures

Real-time speech/music classification with a hierarchical oblique decision tree

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