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Timbre – The Color of Musical Sound

Minakshi P. Atre, Shashikant Sahare

Abstract


Sound can be represented in time domain as a waveform or in frequency domain as a set of spectra. Along with the pitch analysis, timbre, the vertical dimension of sound drives the research in the field of classification and identification of sounds. The pitch and timbre analysis, hand in hand, also run the manipulation- synthesis processes of musical instruments. And here it becomes the matter of crux whether to highlight pitch analysis or timbre analysis of musical sounds. Timbre, called the color of sound, distinguishes the two different instruments playing the same note. Pitch will be same for these instruments. Timbre analysis involves modeling of spectral characteristics of sound when we think of the transformations, may be time or frequency domain. The spectral parameters of musical sound help the researchers to capture the expressiveness of sound. Considering these aspects, this work helps to select the timbre features across a wide variety and categories and is focused on monophonic sounds. It contributes to study of timbral features which are helpful for manipulation of musical notes.

Keywords


Spectra, Pitch, Timbre, Spectral Parameters, Monophonic Sounds.

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References


Klapuri, A., & Davy, M. (2006). “Signal Processing Methods for Music Transcription” New York: Springer.

“Towards Automatic Musical Instrument Timbre Recognition” by Tae Hong Park

“The temporal character of timbre” by Miha Cigar.

“An Adaptive Technique for Automated Recognition of Musical Tones” by Stephen C. Davies and Delores M. Etter.

“Pitch Detection Algorithms Based on Zero-Cross Rate and Autocorrelation Function for Musical Notes” by Rafael George Amado Jozue Vieira Filho.

“Segmenting Melodies into Notes” by Kristopher Jensen and Declan Murphy.

“Discriminate Feature Analysis for Music Timbre Recognition” by Xin Zhang1, Zbigniew W. Ras.

“Musical Instrument Timbres Classification with Spectral Features” Giulio Agostini, Maurizio Longari, Emanuele Pollster.

“Time-Pitch Representation: Acoustic Signal Processing and Auditory Representation” by Gregory H. Wakefield

“On the Use of Autocorrelation Analysis for Pitch Detection” Lawrence R. Rabiner.

“Neuromimetic Sound Representation for Percept Detection and Manipulation” Dmitry N. Zotkin and Ramani Duraiswami, Taishih Chi and Shihab A. Shamma.


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