# Multiobjective time series matching

### Multiobjective time series matching

###### We present here an innovative problem that can be casted into a new approach merging multiobjective optimization and time series matching algorithms, called //MultiObjective Time Series// (MOTS) matching. We formally state this novel problem that could lead to a whole range of applications in several fields of research and report an efficient implementation. This approach allowed in the scope of sound samples querying to cope with the multidimensional nature of timbre perception and also to obtain a whole set of efficient

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### Artificial creative intelligence

We present here an innovative problem that can be casted into a new approach merging multiobjective optimization and time series matching algorithms, called //MultiObjective Time Series// (MOTS) matching. We formally state this novel problem that could lead to a whole range of applications in several fields of research and report an efficient implementation. This approach allowed in the scope of sound samples querying to cope with the multidimensional nature of timbre perception and also to obtain a whole set of efficient propositions rather than a single best solution.

### Musical orchestration

Orchestration is the subtle art of mixing instrumental properties. Among all techniques of musical composition, it has always remained an empirical activity. Our approach is intended to search for sound combinations within instrument sample databases that best match a target timbre defined by the composer. We propose an original approach for the discovery of relevant sound combinations, in which we explicitly address combinatorial issues and tackle the problem of temporal descriptors evolution.

### Biological diversity analysis - DNA Metagenetics

The study of biological diversity through analysis of the DNA sequences found in the environment. We rely on billion-sized datasets of genetic sequences obtained from Next-Generation Sequencing (NGS) to monitor environmental impacts. This work is led with the Department of Genetics and Evolution at the University of Geneva, Switzerland.

### Intelligent sound samples database

Seeking sound samples in a massive database can be a very tedious and painstaking task. Even when meta-informations are available, querying results may remain far from the mental representation expected by the user. We worked on the development of the first intelligent sound sample database. We propose a scheme where sounds can be retrieved by simply drawing the temporal shape of spectral descriptors. Then we addressed two completely novel ways of intuitive querying. First, by optimizing simultaneously multiple temporal shapes of descriptors with //MultiObjective Spectral Evolution Query// (MOSEQ). Second by performing a vocal imitation of the sound sample with //Query by Vocal Imitation// (QVI).

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