DAGA 2010 - Deutsche Gesellschaft für Akustik eV

Programm DAGA 2010 73
lokal vorherrschende Tempo eines Musikstückes beschreiben, Segmente
mit homogenem Tempo bestimmt werden. Des Weiteren zeigen wir,
wie diese Segmentierung als sinnvolle Ergänzung zu vorherigen Verfahren,
die eine Segmentierung hinsichtlich der Klangfarbe oder Harmonie
eines Musikstückes vollziehen, eingesetzt werden kann.
Di. 17:00 Grashof C 24 Music Processing I
Drumloop Separation using adaptive Spectrogram Templates
C. Dittmar, J. Abeßer und D. Gärtner
Fraunhofer IDMT
The separation of single drumsounds from drumloops is a desirable signal
processing functionality with a wide variety of applications in music
production and music video games. Since recognition of the distinct
drumsounds is a pre-requisite for separation, the detection of onsets and
instrument types is necessary. Although machine-learning based classification
of isolated drumsounds has been proven do be feasible, it is not
applicable to the problem of drumloop separation. The main challenge
is the strong overlap of drumsound spectra when two or more drumsounds
share the same onset-time. It leads to erroneous estimation of the
involved instruments, e.g., a tom and a hi-hat appearing simultaneously
could easily be misclassified as being a snare. Different approaches
have been proposed in the literature to overcome that problem, mainly
template matching vs. decomposition based methods. We pursue the
approach of template matching, but without the need for any prior assumption
about the involved instruments. A heuristic update rule for the
templates is described as well as an expectation maximization approach
to the final thresholding. The quality of separation is evaluated with artificially
generated as well as real drumloops. The transcription is tested
against manually annotated excerpts from commercially available music
recordings.
Di. 17:25 Grashof C 24 Music Processing I
Estimating Similarity of Musical Rhythm Patterns through the use
of a Neural Network Model
A. Fouloulis a , G. Papadelis b , K. Pastiadis b und G. Papanikolaou a
a Aristotle University of Thessaloniki, Electrical & Computer Eng.;
b Aristotle University of Thessaloniki, Dept. of Music Studies
Assessment of musical aptitude in young children usually includes the
task of rhythm copying, where the child is asked to tap a short rhythm
pattern after listening to it. Evaluation of performance accuracy on this
task is based on indices of similarity between the stimulus and the performed
pattern, which may result either from mathematical ”distance metrics”
or, alternatively, from ratings provided by experienced musicians in
pair wise comparison tasks.
In this work we present the architecture of a system containing two artificial
neural networks in cascade - a self-organizing neural network (called