HYBRID MUSICAL INSTRUMENT SOUND GENERATION USING

TEMPORAL AND SPECTRAL SHAPE FEATURES

Guided By Presented By

Mr.Arun Jose Noufiya Nazarudin

Asst. Professor,ECE SP M.Tech

TKMIT TKMIT1

OBJECTIVE

Quest for a fresh music soundHybrid musical instrument sound generation

using sound morphing

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INTRODUCTION

Sound morphing-blending of two or more sounds into a single percept

Transform across timbre dimensionsTemporal and Spectral shape features are usedFeatures are morphed separately and mixed

back together Features and interpolation factor should vary

linearly

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BLOCK DIAGRAM

Selected 2 or more sounds

Temporal Processing

Spectral Processing

Feature Extraction Comparison Feature

Extraction

Morphing

Minimum Error

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Compare with a sound database

Target sound

METHODS USED

Feature extraction* Temporal- Log attack time & Temporal centroid

* Spectral- centroid, spread, skewness, kurtosis

Temporal Processing

* Segmentation

* Alignment

* Envelope Estimation

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Contd…

Spectral Processing

* Sinusoidal & Residual Decomposition

* Source-Filter Modeling

Morphing

* Spectral envelope morphing

* Interpolation of frequencies of partials

* Temporal envelope morphing

MODIFICATION

Can tune the morphed sound using recorded music

Combination of various morphed sounds can be used for automatic orchestra/karoke generation

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APPLICATIONS

Entertainment and Music industryMusic compositionComputer Music Generation

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REFERENCES[1] Marcelo Caetano and Xavier Rodet, “Musical Instrument Sound

Morphing Guided by Perceptually Motivated Features”, IEEE Trans.on audio,speech and language processing., vol.21, no.8,pp.1666–1675,Aug. 2013.

[2] William Sethares and James Bucklew, “Kernel Techniques for Audio Morphing”, Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, May,2012.

[3] Nicholas Waggoner and Chris McGilliard, “Music Morph”, Physics of Music.,pp 498-599, May 2005.

[4] Gianpaolo Evangelista and Sergio Cavaliere, “Audio Effects Based on Biorthogonal Time-Varying FrequencyWarping”, EURASIP Journal on Applied Signal Processing 2001:1, 27–35.

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