This paper describes an application of constraint programming to interfaces for audio mixing. MidiSpace is an interface representing each sound source of a musical piece as a graphical icon, as well as an object corresponding to the listener in a window. MidiSpace is coupled to a spatialization system so that moving graphical objects modifies the audio mixing of the musical piece according to the respective position of the sound sources to the avatar. We further introduce a constraint-based mechanism which allows to maintain consistency in the overall mixing. Constraints represent properties of related sound sources, which should always remain true, and may be stated by the user through the interface. When an object is moved, a constraint solver uses the constraints to propagate changes. We describe the library of currently designed constraints, and propose an extension of the system to handle reproduction systems with multiple loudspeakers.

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We address the issue of automatically extracting rhythm descriptors from audio signals, to be eventually used in content-based musical applications such as in the context of MPEG7. Our aim is to approach the comprehension of auditory scenes in raw polyphonic audio signals without preliminary source separation. As a first step towards the automatic extraction of rhythmic structures out of signals taken from the popular music repertoire, we propose an approach for automatically extracting time indexes of occurrences of different percussive timbres in an audio signal. Within this framework, we found that a particular issue lies in the classification of percussive sounds. In this paper, we report on the method currently used to deal with this problem.

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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.

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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.

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There is an increasing need for automatically classifying sounds for MIR and interactive music applications. In the context of supervised classification, we describe an approach that improves the performance of the general bag-of-frame scheme without loosing its generality. This method is based on the construction and exploitation of specific audio features, called analytical, as input to classifiers. These features are better, in a sense we define precisely than standard, general features, or even than ad hoc features designed by hand for specific problems. To construct these features, our method explores a very large space of functions, by composing basic operators in syntactically correct ways. These operators are taken from the Mathematical and Audio Processing domains. Our method allows us to build a large number of these features, evaluate and select them automatically for arbitrary audio classification problems. We present here a specific study concerning the analysis of Pandeiro (Brazilian tambourine) sounds. Two problems are considered: the classification of entire sounds, for MIR applications, and the classification of attacks portions of the sound only, for interactive music applications. We evaluate precisely the gain obtained by analytical features on these two problems, in comparison with standard approaches.

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