1,941 research outputs found
Comparing Probabilistic Models for Melodic Sequences
Modelling the real world complexity of music is a challenge for machine
learning. We address the task of modeling melodic sequences from the same music
genre. We perform a comparative analysis of two probabilistic models; a
Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional
Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns
descriptive music features, such as underlying chords and typical melody
transitions and dynamics. We assess the models for future prediction and
compare their performance to a VMM, which is the current state of the art in
melody generation. We show that both models perform significantly better than
the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally,
we evaluate the short order statistics of the models, using the
Kullback-Leibler divergence between test sequences and model samples, and show
that our proposed methods match the statistics of the music genre significantly
better than the VMM.Comment: in Proceedings of the ECML-PKDD 2011. Lecture Notes in Computer
Science, vol. 6913, pp. 289-304. Springer (2011
Predictive uncertainty in auditory sequence processing
Copyright © 2014 Hansen and Pearce. This is an open-access article distributed under
the terms of the Creative Commons Attribution License (CC BY). The use, distribution
or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance
with accepted academic practice. No use, distribution or reproduction is permitted
which does not comply with these terms
A group model for stable multi-subject ICA on fMRI datasets
Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract sets of mutually correlated brain
regions without prior information on the time course of these regions. Some of
these sets of regions, interpreted as functional networks, have recently been
used to provide markers of brain diseases and open the road to paradigm-free
population comparisons. Such group studies raise the question of modeling
subject variability within ICA: how can the patterns representative of a group
be modeled and estimated via ICA for reliable inter-group comparisons? In this
paper, we propose a hierarchical model for patterns in multi-subject fMRI
datasets, akin to mixed-effect group models used in linear-model-based
analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based
on i) probabilistic dimension reduction of the individual data, ii) canonical
correlation analysis to identify a data subspace common to the group iii)
ICA-based pattern extraction. In addition, we introduce a procedure based on
cross-validation to quantify the stability of ICA patterns at the level of the
group. We compare our method with state-of-the-art multi-subject fMRI ICA
methods and show that the features extracted using our procedure are more
reproducible at the group level on two datasets of 12 healthy controls: a
resting-state and a functional localizer study
Probabilistic models for melodic prediction
AbstractChord progressions are the building blocks from which tonal music is constructed. The choice of a particular representation for chords has a strong impact on statistical modeling of the dependence between chord symbols and the actual sequences of notes in polyphonic music. Melodic prediction is used in this paper as a benchmark task to evaluate the quality of four chord representations using two probabilistic model architectures derived from Input/Output Hidden Markov Models (IOHMMs). Likelihoods and conditional and unconditional prediction error rates are used as complementary measures of the quality of each of the proposed chord representations. We observe empirically that different chord representations are optimal depending on the chosen evaluation metric. Also, representing chords only by their roots appears to be a good compromise in most of the reported experiments
Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation
Engineering and Physical Sciences Research Council (EPSRC) funding via grant EP/M000702/1
Data-driven, memory-based computational models of human segmentation of musical melody
When listening to a piece of music, listeners often identify distinct sections or segments
within the piece. Music segmentation is recognised as an important process in the abstraction
of musical contents and researchers have attempted to explain how listeners
perceive and identify the boundaries of these segments.The present study seeks the development of a system that is capable of performing
melodic segmentation in an unsupervised way, by learning from non-annotated musical
data. Probabilistic learning methods have been widely used to acquire regularities in
large sets of data, with many successful applications in language and speech processing.
Some of these applications have found their counterparts in music research and have
been used for music prediction and generation, music retrieval or music analysis, but
seldom to model perceptual and cognitive aspects of music listening.We present some preliminary experiments on melodic segmentation, which highlight
the importance of memory and the role of learning in music listening. These experiments
have motivated the development of a computational model for melodic segmentation
based on a probabilistic learning paradigm.The model uses a Mixed-memory Markov Model to estimate sequence probabilities
from pitch and time-based parametric descriptions of melodic data. We follow the assumption
that listeners' perception of feature salience in melodies is strongly related
to expectation. Moreover, we conjecture that outstanding entropy variations of certain
melodic features coincide with segmentation boundaries as indicated by listeners.Model segmentation predictions are compared with results of a listening study on
melodic segmentation carried out with real listeners. Overall results show that changes
in prediction entropy along the pieces exhibit significant correspondence with the listeners'
segmentation boundaries.Although the model relies only on information theoretic principles to make predictions
on the location of segmentation boundaries, it was found that most predicted segments
can be matched with boundaries of groupings usually attributed to Gestalt rules.These results question previous research supporting a separation between learningbased
and innate bottom-up processes of melodic grouping, and suggesting that some
of these latter processes can emerge from acquired regularities in melodic data
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