Open Access
Towards morphological sound description using segmental models
Julien Bloit,Nicolas Rasamimanana,Frédéric Bevilacqua +2 more
- 01 Sep 2009
pp 1-1
TL;DR: This work presents an approach to model the temporal evolution of audio descriptors using Segmental Models, which allows to segment a signal as a sequence of primitives, constituted by a set of trajectories defined by the user.
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Abstract: We present an approach to model the temporal evolution of audio descriptors using Segmental Models (SMs). This method allows to segment a signal as a sequence of primitives, constituted by a set of trajectories defined by the user. This allows one to explicitly model the time duration of primitives, and to take into account the time dependence between successive signal frames, contrary to standard Hidden Markov Models. We applied this approach to a database of violin playing. Various types of glissando and dynamics variations were specifically recorded. Our results shows that our approach using Segmental Models provides a segmentation that can be easily interpreted. Quantitatively, the Segmental Models performed better than standard implementation of Hidden Markow Models.
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Citations
•Dissertation
Self-organised Sound with Autonomous Instruments: Aesthetics and experiments
Risto Holopainen
- 01 Jan 2012
TL;DR: This thesis addresses questions of aesthetics and the role of the composer in music made with more or less autonomous instruments and develops a particular form of autonomous instruments, called feature-feedback systems, which use feature extractors in a feedback loop.
26
On the modeling of sound textures based on the STFT representation
Wei Hsiang Liao,Axel Roebel,Alvin W.Y. Su +2 more
- 01 Jan 2013
TL;DR: An algorithm to extract and modify the statistical properties of a sound texture based on its STFT representation is proposed and it is shown that the algorithm is capable of generating high quality sounds from an extracted model.
10
Modeling and segmentation of audio descriptor profiles with segmental models
TL;DR: The results show that the segmental model can segment and recognize these different musical elements with a satisfactory level on a dataset made of violin recording containing crescendo/decrescende, glissando and sforzando.
7
On stretching Gaussian noises with the phase vocoder
Wei Hsiang Liao,Axel Roebel,Alvin W.Y. Su +2 more
- 01 Dec 2012
TL;DR: The problems that arise when time stretching noise with the phase vocoder are demonstrated, a description of some relevant statistical properties of the time frequency representation of noise are provided and an algorithm is introduced that significantly improves the perceptual quality of theTime stretched noise signals.
7
Abstract sounds and their applications in audio and perception research
Adrien Merer,Sølvi Ystad,Richard Kronland-Martinet,Mitsuko Aramaki +3 more
- 21 Jun 2010
TL;DR: This paper introduces what it is called abstract sounds with the existing musical background and shows their relevance for different applications.
References
A tutorial on hidden Markov models and selected applications in speech recognition
Lawrence R. Rabiner
- 01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
•Book
Fundamentals of speech recognition
Lawrence R. Rabiner,Biing-Hwang Juang +1 more
- 01 Jan 1993
TL;DR: This book presents a meta-modelling framework for speech recognition that automates the very labor-intensive and therefore time-heavy and therefore expensive and expensive process of manually modeling speech.
9.4K
•Journal Article
A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models
TL;DR: In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.
YIN, a fundamental frequency estimator for speech and music
TL;DR: An algorithm is presented for the estimation of the fundamental frequency (F0) of speech or musical sounds, based on the well-known autocorrelation method with a number of modifications that combine to prevent errors.
Functional Data Analysis
James O. Ramsay,Bernard W. Silverman +1 more
- 01 Jan 2001
TL;DR: In this article, the authors introduce the concept of functional data analysis (FDA) to describe the smoothness of the process of generating functional data from a set of observed curves and images.
1.5K