Stefan Lattner

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A salient characteristic of human perception of music is that musical events are perceived as being grouped temporally into structural units such as phrases or motifs. Segmentation of musical sequences into structural units is a topic of ongoing research, both in cognitive psychology and music information retrieval. Computational models of music(More)
The perception of tonal structure in music seems to be rooted both in low-level perceptual mechanisms and in en-culturation, the latter accounting for cross-cultural differences in perceived tonal structure. Unsupervised machine learning methods are a powerful tool for studying how musical concepts may emerge from exposure to music. In this paper, we(More)
We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient descent constraint optimization to provide further control over the generation process. Among other things, this allows for the use of a " template " piece, from which(More)
The ability to extract meaningful relationships from sequences is crucial to many aspects of perception and cognition, such as speech and music. This paper explores how leading computational techniques may be used to model how humans learn abstract musical relationships, namely, tonality and octave equivalence. Rather than hard-coding musical rules, this(More)
An important aspect of music perception in humans is the ability to segment streams of musical events into structural units such as motifs and phrases. A promising approach to the computational mod-eling of music segmentation employs the statistical and information-theoretic properties of musical data, based on the hypothesis that these properties can (at(More)
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