Processing of Musical Data Employing Rough Sets and Artificial Neural Networks

Kostek, Bożena; Szczuko, Piotr; Zwan, Pawel; Dalka, Piotr

doi:10.1007/11427834_5

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…The MPEG-7 audio parameters can be divided into the following groups: -ASE (Audio Spectrum Envelope) -describes the short-term power spectrum of the waveform with logarithmic frequency axis in ¼-octave resolution sized bands, between 62.5 Hz and 16 kHz (frequencies below 62. 5 …”

Section: Mpeg-7-based Parametersmentioning

confidence: 97%

“…Although they are not related to the singing voice biomechanics, they can be analyzed statistically in order to see whether they are useful in the singing voice recognition process. The MPEG-7 parameters [5], [9] are not to be presented in a detail, such a detailed description is out of the focus of the paper, thus only a short description is to be shown. The MPEG-7 audio parameters can be divided into the following groups: -ASE (Audio Spectrum Envelope) -describes the short-term power spectrum of the waveform with logarithmic frequency axis in ¼-octave resolution sized bands, between 62.5 Hz and 16 kHz (frequencies below 62.…”

Section: Mpeg-7-based Parametersmentioning

confidence: 99%

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Automatic classification of singing voice quality

Kostek

Zwan

2005

5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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In the paper problems related to the classification of singing voice quality are presented. For this purpose a database consisting of singers' sample recordings is constructed and parameters are extracted from recorded voice of trained and untrained singers. The parameterization process is based on both voice source and formant analysis of a singing voice. These parameters are explained as to their physical interpretation and analyzed statistically in order to diminish their number. The statistical analysis is based on the Fisher Statistic. In such a way a feature vector of a singing voice is formed. Decision systems based on neutral networks and rough sets are utilized in the context of the voice type and voice quality classification. Results obtained in the automatic classification performed by both decision systems are compared. A possibility to classify automatically type/quality of voice is judged. The methodology proposed provides means for discerning trained and untrained singers.

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Section: Mpeg-7-based Parametersmentioning

confidence: 97%

Section: Mpeg-7-based Parametersmentioning

confidence: 99%

Automatic classification of singing voice quality

Kostek

Zwan

2005

5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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“…Multiple classifiers are applied for classification such as GMM, SVM, and AdaBoost. Representative work in this branch were published by (Wold, Blum, Keislar, & Wheaten, 1996), (Martin, 1998), (Martin, 1998), (Allegro, 2001), (McKinney & Breebaart, 2003), (Liu, 2003), (Kostek et al, 2004), (Pedro & Cano, 2005), (Eck, Lamere, BertinMahieux, & Green, 2007), (Narayanan, 2007), (Burred, Cella, Peeters, Rbel, & Schwarz, 2008), (L. N. Chen, Wolfgang;Wright, Phillip., 2009), (Dhanalakshmi, Palanivel, & Ramalingam, 2009), (Edith Law, 2009), (Luke Barrington, 2009), (Lidy et al, 2010), (Lee, 2009), (Miotto, Barrington, & Lanckriet, 2010), (Kuznetsov & Pyshkin, 2010), (Gordon Wichern, 2010), (Luke Barrington, 2010), (Jun Takagi, 2011). Multiple audio features are selected and extracted for classification tasks.…”

Section: Automatic Taggingmentioning

confidence: 99%

A survey of tagging techniques for music, speech and environmental sound

et al. 2012

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Sound tagging has been studied for years. Among all sound types, music, speech, and environmental sound are three hottest research areas. This survey aims to provide an overview about the state-of-the-art development in these areas. We discuss about the meaning of tagging in different sound areas at the beginning of the journey. Some examples of sound tagging applications are introduced in order to illustrate the significance of this research. Typical tagging techniques include manual, automatic, and semi-automatic approaches. After reviewing work in music, speech and environmental sound tagging, we compare them and state the research progress to date. Research gaps are identified for each research area and the common features and discriminations between three areas are discovered as well. Published datasets, tools used by researchers, and evaluation measures frequently applied in the analysis are listed. In the end, we summarise the worldwide distribution of countries dedicated to sound tagging research for years.

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“…A decision system employing rough sets and neural networks is presented in [76]. The aim of the study was to automatically classify musical instrument sounds on the basis of a limited number of parameters, and to test the quality of musical sound parameters that are included in the MPEG-7 standard.…”

Section: Music and Acousticsmentioning

confidence: 99%

Rough sets, their extensions and applications

Shen

Jensen

2007

Int J Automat Comput

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Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.Rough set theory provides a useful mathematical foundation for developing automated computational systems that can help understand and make use of imperfect knowledge. Despite its recency, the theory and its extensions have been widely applied to many problems, including decision analysis, data-mining, intelligent control and pattern recognition. This paper presents an outline of the basic concepts of rough sets and their major extensions, covering variable precision, tolerance and fuzzy rough sets. It also shows the diversity of successful applications these theories have entailed, ranging from financial and business, through biological and medicine, to physical, art, and meteorological.Peer reviewe

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Processing of Musical Data Employing Rough Sets and Artificial Neural Networks

Cited by 10 publications

References 20 publications

Automatic classification of singing voice quality

Automatic classification of singing voice quality

A survey of tagging techniques for music, speech and environmental sound

Rough sets, their extensions and applications

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