Music – Instruments – Electrical musical tone generation
Reexamination Certificate
2001-08-17
2002-11-05
Donels, Jeffrey (Department: 2837)
Music
Instruments
Electrical musical tone generation
C084S623000, C084S600000
Reexamination Certificate
active
06476308
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to classification of a musical piece containing plural notes, and in particular, to classification of a musical piece for indexing and retrieval during management of a database.
2. Background Information
Known research has been directed to the electronic synthesis of individual musical notes, such as the production of synthesized notes for producing electronic music. Research has also been directed to the analysis of individual notes produced by musical instruments (i.e., both electronic and acoustic). The research in these areas has been directed to the classification and/or production of single notes as monophonic sound (i.e., sound from a single instrument, produced one note at a time) or as synthetic (e.g., MIDI) music.
Known techniques for the production and/or classification of single notes have involved the development of feature extraction methods and classification tools which can be used with respect to single notes. For example, a document entitled “Rough Sets As A Tool For Audio Signal Classification” by Alicja Wieczorkowska of the Technical University of Gdansk, Poland, pages 367-375, is directed to automatic classification of musical instrument sounds. A document entitled “Computer Identification of Musical Instruments Using Pattern Recognition With Cepstral Coefficients As Features”, by Judith C. Brown, J. Acoust. Soc. Am
105
(
3
) Mar. 1999, pages 1933-1941, describes using cepstral coefficients as features in a pattern analysis.
It is also known to use wavelet coefficients and auditory modeling parameters of individual notes as features for classification. See, for example, “Musical Timbre Recognition With Neural Networks” by Jeong, Jae-Hoon et al, Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, pages 869-872 and “Auditory Modeling and Self-Organizing Neural Networks for Timbre Classification” by Cosi, Piero et al., Journal of New Music Research, Vol. 23 (1994), pages 71-98, respectively. These latter two documents, along with a document entitled “Timbre Recognition of Single Notes Using An ARTMAP Neural Network” by Fragoulis, D. K. et al, National Technical University of Athens, ICECS 1999 (IEEE International Conference on Electronics, Circuits and Systems), pages 1009-1012 and “Recognition of Musical Instruments By A NonExclusive Neuro-Fuzzy Classifier” by Costantini, G. et al, ECMCS '99, EURASIP Conference, Jun. 24-26, 1999, Kraków, 4 pages, are also directed to use of artificial neural networks in classification tools. An additional document entitled “Spectral Envelope Modeling” by Kristoffer Jensen, Department of Computer Science, University of Copenhagen, Denmark, describes analyzing the spectral envelope of typical musical sounds.
Known research has not been directed to the analysis of continuous music pieces which contain multiple notes and/or polyphonic music produced by multiple instruments and/or multiple notes played at a single time. In addition, known analysis tools are complex, and unsuited to real-time applications such as the indexing and retrieval of musical pieces during database management.
SUMMARY OF THE INVENTION
The present invention is directed to classifying a musical piece based on determined characteristics for each of plural notes contained within the piece. Exemplary embodiments accommodate the fact that in a continuous piece of music, the starting and ending points of a note may overlap previous notes, the next note, or notes played in parallel by one or more instruments. This is complicated by the additional fact that different instruments produce notes with dramatically different characteristics. For example, notes with a sustaining stage, such as those produced by a trumpet or flute, possess high energy in the middle of the sustaining stage, while notes without a sustaining stage, such as those produced by a piano or guitar, posses high energy in the attacking stage when the note is first produced. Exemplary embodiments address these complexities to permit the indexing and retrieval of musical pieces in real time, in a database, thus simplifying database management and enhancing the ability to search multimedia assets contained in the database.
Generally speaking, exemplary embodiments are directed to a method of classifying a musical piece constituted by a collection of sounds, comprising the steps of detecting an onset of each of plural notes contained in a portion of the musical piece using a temporal energy envelope; determining characteristics for each of the plural notes; and classifying a musical piece for storage in a database based on integration of determined characteristics for each of the plural notes.
REFERENCES:
patent: 6185527 (2001-02-01), Petkovic et al.
patent: 6201176 (2001-03-01), Yourlo
“Rough Sets As A Tool For Audio Signal Classification” by Alicja Wieczorkowska of the Technical University of Gdansk, Poland, pp. 367-375.
“Computer Identification of Musical Instruments Using Pattern Recognition With Cepstral Coefficients As Features”, by Judith C. Brown, J. Acoust. Soc. Am 105 (3) Mar. 1999, pp. 1933-1941.
“Musical Timbre Recognition With Neural Networks” by Jeong, Jae-Hoon et al, Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, pp. 869-872.
“Auditory Modeling and Self-Organizing Neural Network for Timbre Classification” by Cosi, Piero et al., Journal of New Music Research, vol. 23 (1994), pp. 71-98.
“Timbre Recognition of Single Notes Using An ARTMAP Neural Network” by Fragoulis, D.K. et al, National Technical University of Athens, ICECS 1999 (IEEE International Conference on Electronics, Circuits and Systems), pp. 1009-1012.
“Recognition of Musical Instruments By A NonExclusive Neuro-Fuzzy Classifier” by Constantini, G. et al, ECMCS '99, EURASIP Conference, Jun. 24-26, 1999, Kraków, 4 pages.
“Spectral Envelope Modeling” by Kristoffer Jensen, Department of Computer Science, University of Copenhagen, Denmark, Aug. 1998, pp. 1-7.
N. Mohanty, “Random signals estimation and indentification—Analysis and Applications”, Van Nostrand Reinhold Company, 1986, Chpt. 4, pp. 319-343.
“An Introduction To Neural Networks”, by K. Gurney, UCL Press, 1997, Chpt. 6, pp. 65-129.
“Robust Text-Independent Speaker Identification Using Gaussian Mixture Models”, by D. Reynolds and R. Rose, IEEE Transactions On Speech and Audio Processing, vol. 3, No. 1, pp. 72-83, 1985.
Donels Jeffrey
Hewlett--Packard Company
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