Download Musical Mosaicing This work addresses the issue of retrieving efficiently sound samples in large databases, in the context of digital music composition. We propose a sequence generation mechanism called musical mosaicing, which enables to generate automatically sequences of sound samples by specifying only high-level properties of the sequence to generate. The properties of the sequence specified by the user are translated automatically into constraints holding on descriptors of the samples. The system we propose is able to scale up on databases containing more than 100.000 samples, using a local search method based on constraint solving. In this paper, we describe the method for retrieving and sequencing audio samples, and illustrate it with rhythmic and melodic musical sequences.
Download Extracting automatically the perceived intensity of music titles We address the issue of extracting automatically high-level musical descriptors out of their raw audio signal. This work focuses on the extraction of the perceived intensity of music titles, that evaluates how energic the music is perceived by listeners. We present here first the perceptive tests that we have conducted, in order to evaluate the relevance and the universality of the perceived intensity descriptor. Then we present several methods used to extract relevant features used to build automatic intensity extractors: usual Mpeg7 low level features, empirical method, and features automatically found using our Extractor Discovery System (EDS), and compare the final performances of their extractors.