International audienc

After covering the basics of sound perception and giving an overview of commonly used audio effects (using a perceptual categorization), we propose a new concept called adaptive digital audio effects (A-DAFx). This consists of combining a sound transformation with an adaptive control. To create A-DAFx, low-level and perceptual features are extracted from the input signal, in order to derive the control values according to specific mapping functions. We detail the implementation of various new adaptive effects and give examples of their musical use.

Content processing is a vast and growing field that integrates different approaches borrowed from the signal processing, information retrieval and machine learning disciplines. In this article we deal with a particular type of content processing: the so-called content-based transformations. We will not focus on any particular application but rather try to give an overview of different techniques and conceptual implications. We first describe the transformation process itself, including the main model schemes that are commonly used, which lead to the establishment of the formal basis for a definition of content-based transformations. Then we take a quick look at a general spectral based analysis/synthesis approach to process audio signals and how to extract features that can be used in the content-based transformation context. Using this analysis/synthesis approach we give some examples on how content-based transformations can be applied to modify the basic perceptual axis of a sound and how we can even combine different basic effects in order to perform more meaningful transformations. We finish by going a step further in the abstraction ladder and present transformations that are related to musical (and thus symbolic) properties rather than to those of the sound or the signal itself.

Producing a tone by increasing the blowing pressure to excite a higher frequency impedance minimum, or overblowing, is widely used in standard flute technique. In this paper, the effect of overblowing a fingering is explored with spectral analysis, and a fingering detector is designed based on acoustical knowledge and pattern classification techniques. The detector performs signal analysis of the strong broadband signal, that is, spectrally shaped by the pipe impedance, and measures the spectral energy during the attack around multiples of the fundamental frequency sub-multiples over the first octave and a half. It is trained and evaluated on sounds recorded with four expert performers. They played six series of tones from overblown and regular fingerings, with frequencies that are octave- and non-octave-related to the playing frequency. The best of the four proposed sound descriptors allows for a detection error below 1.3% for notes with two and three fingerings (C(5), D(5), C(6), and Cmusical sharp(6)) and below 14% for four (E(6)) or five fingerings (G(6)). The error is shown to dramatically increase when two fingerings' impedance become too similar (E(6) and A(4) and G(6) and C(5)).

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