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Explicit duration hidden Markov models for multiple-instrument polyphonic music transcription
Music in the first days of life
In adults, specific neural systems with right-hemispheric weighting are necessary to process pitch, melody and harmony, as well as structure and meaning emerging from musical sequences. To which extent does this neural specialization result from exposure to music or from neurobiological predispositions? We used fMRI to measure brain activity in 1 to 3 days old newborns while listening to Western tonal music, and to the same excerpts altered, so as to include tonal violations or dissonance. Music caused predominant right hemisphere activations in primary and higher-order auditory cortex. For altered music, activations were seen in the left inferior frontal cortex and limbic structures. Thus, the newborn's brain is able to plenty receive music and to figure out even small perceptual and structural differences in the music sequences. This neural architecture present at birth provides us the potential to process basic and complex aspects of music, a uniquely human capacity
Algorithmic Clustering of Music
We present a fully automatic method for music classification, based only on
compression of strings that represent the music pieces. The method uses no
background knowledge about music whatsoever: it is completely general and can,
without change, be used in different areas like linguistic classification and
genomics. It is based on an ideal theory of the information content in
individual objects (Kolmogorov complexity), information distance, and a
universal similarity metric. Experiments show that the method distinguishes
reasonably well between various musical genres and can even cluster pieces by
composer.Comment: 17 pages, 11 figure
The Faculty Notebook, September 2008
The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost
Musical notes classification with Neuromorphic Auditory System using FPGA and a Convolutional Spiking Network
In this paper, we explore the capabilities of a sound
classification system that combines both a novel FPGA cochlear
model implementation and a bio-inspired technique based on a
trained convolutional spiking network. The neuromorphic
auditory system that is used in this work produces a form of
representation that is analogous to the spike outputs of the
biological cochlea. The auditory system has been developed using
a set of spike-based processing building blocks in the frequency
domain. They form a set of band pass filters in the spike-domain
that splits the audio information in 128 frequency channels, 64
for each of two audio sources. Address Event Representation
(AER) is used to communicate the auditory system with the
convolutional spiking network. A layer of convolutional spiking
network is developed and trained on a computer with the ability
to detect two kinds of sound: artificial pure tones in the presence
of white noise and electronic musical notes. After the training
process, the presented system is able to distinguish the different
sounds in real-time, even in the presence of white noise.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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