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Melody is an important property for the perceptual description of Western musical pieces. In the monophonic context, retrieval systems based on melodic similarity generally consider sequences of pitches and durations. Algorithms that have been proposed for measuring melodic similarity rely on geometric representations, string matching techniques, etc. Adaptations of editing algorithms, mainly applied in bioinformatic applications, to the musical domain have already been proposed. However, we present in this paper several experiments in order to optimize these methods. The different possible representations for pitches and durations are discussed and evaluated. Optimizations specific to musical applications are proposed and imply significant improvements of the editing algorithm studied. Evaluation of this algorithm led to the best results during the MIREX 2006 symbolic melodic similarity contest. 2 Problem Formalization Algorithms for retrieval systems based on melodic similarity consist of two main steps. The first one transforms a symbolic monophonic musical piece into a symbolic sequence. The second one computes a similarity score between two representations. These two steps are presented in this section.

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________________________________________________________________________In this article we propose a musical instrument that provides young children with a way to play in a virtual sound space by means of a joystick. We present the work of a multidiscipline team working on various aspects of this project. Sound models that are close to perception are used to define the sound spaces. Pedagogical experiments in public schools are done on the basis of cognitive pedagogy. Ethological and psychoacoustic experiments are carried out to improve the ergonomics of the instruments.

We propose an original model for noise analysis, transformation, and synthesis: the CNSS model. Noisy sounds are represented with short-time sinusoids whose frequencies and phases are random variables. This spectral and statistical model represents information about the spectral density of frequencies. This perceptually relevant property is modeled by three mathematical parameters that define the distribution of the frequencies. This model also represents the spectral envelope. The mathematical parameters are defined and the analysis algorithms to extract these parameters from sounds are introduced. Then algorithms for generating sounds from the parameters of the model are presented. Applications of this model include tools for composers, psychoacoustic experiments, and pedagogy

Usual noise models represent sounds with filtered white noise and only extract the spectral envelope from sounds analyzed. Psychoacoustic experiments have shown the ability for humans to discriminate noise bands with different spectral density. This property may be taken into account for the mathematical representation of noises. In this paper we propose an original method to approximate this new parameter. The limitations of the discrete short-term transform lead us to consider a new approach. This technique is based on the statistical studies of the intensity fluctuations which are theoretically related to the spectral density. An algorithm is detailed and its parameters are discussed. Experiments show the quality and the limitations of this method. Although it may be improved, this technique of estimation is correct enough to be applied in a complete analysidsynthesis model for noisy sounds.

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