This paper presents research into scanned synthesis on a multitouch screen device. This synthesis technique involves scanning a wavetable that is dynamically evolving in the manner of a massspring network. It is argued that scanned synthesis can provide a good solution to some of the issues in digital musical instrument design, and is particularly well suited to multi-touch screens. In this implementation, vibrating mass-spring networks with a variety of configurations can be created. These can be manipulated by touching, dragging and altering the orientation of the tablet. Arbitrary scanning paths can be drawn onto the structure. Several extensions to the original scanned synthesis technique are proposed, most important of which for multi-touch implementations is the freedom of the masses to move in two dimensions. An analysis of the scanned output in the case of a 1D ideal string model is given, and scanned synthesis is also discussed as being a generalisation of a number of other synthesis methods.

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In this paper, we propose a method for modeling and classifying acoustic scenes using temporally-constrained shift-invariant probabilistic latent component analysis (SIPLCA). SIPLCA can be used for extracting time-frequency patches from spectrograms in an unsupervised manner. Component-wise hidden Markov models are incorporated to the SIPLCA formulation for enforcing temporal constraints on the activation of each acoustic component. The time-frequency patches are converted to cepstral coefficients in order to provide a compact representation of acoustic events within a scene. Experiments are made using a corpus of train station recordings, classified into 6 scene classes. Results show that the proposed model is able to model salient events within a scene and outperforms the non-negative matrix factorization algorithm for the same task. In addition, it is demonstrated that the use of temporal constraints can lead to improved performance.

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