12,385 research outputs found
A Deep Representation for Invariance And Music Classification
Representations in the auditory cortex might be based on mechanisms similar
to the visual ventral stream; modules for building invariance to
transformations and multiple layers for compositionality and selectivity. In
this paper we propose the use of such computational modules for extracting
invariant and discriminative audio representations. Building on a theory of
invariance in hierarchical architectures, we propose a novel, mid-level
representation for acoustical signals, using the empirical distributions of
projections on a set of templates and their transformations. Under the
assumption that, by construction, this dictionary of templates is composed from
similar classes, and samples the orbit of variance-inducing signal
transformations (such as shift and scale), the resulting signature is
theoretically guaranteed to be unique, invariant to transformations and stable
to deformations. Modules of projection and pooling can then constitute layers
of deep networks, for learning composite representations. We present the main
theoretical and computational aspects of a framework for unsupervised learning
of invariant audio representations, empirically evaluated on music genre
classification.Comment: 5 pages, CBMM Memo No. 002, (to appear) IEEE 2014 International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation
To date a number of studies have shown that receptive field shapes of early
sensory neurons can be reproduced by optimizing coding efficiency of natural
stimulus ensembles. A still unresolved question is whether the efficient coding
hypothesis explains formation of neurons which explicitly represent
environmental features of different functional importance. This paper proposes
that the spatial selectivity of higher auditory neurons emerges as a direct
consequence of learning efficient codes for natural binaural sounds. Firstly,
it is demonstrated that a linear efficient coding transform - Independent
Component Analysis (ICA) trained on spectrograms of naturalistic simulated
binaural sounds extracts spatial information present in the signal. A simple
hierarchical ICA extension allowing for decoding of sound position is proposed.
Furthermore, it is shown that units revealing spatial selectivity can be
learned from a binaural recording of a natural auditory scene. In both cases a
relatively small subpopulation of learned spectrogram features suffices to
perform accurate sound localization. Representation of the auditory space is
therefore learned in a purely unsupervised way by maximizing the coding
efficiency and without any task-specific constraints. This results imply that
efficient coding is a useful strategy for learning structures which allow for
making behaviorally vital inferences about the environment.Comment: 22 pages, 9 figure
Feature Learning from Spectrograms for Assessment of Personality Traits
Several methods have recently been proposed to analyze speech and
automatically infer the personality of the speaker. These methods often rely on
prosodic and other hand crafted speech processing features extracted with
off-the-shelf toolboxes. To achieve high accuracy, numerous features are
typically extracted using complex and highly parameterized algorithms. In this
paper, a new method based on feature learning and spectrogram analysis is
proposed to simplify the feature extraction process while maintaining a high
level of accuracy. The proposed method learns a dictionary of discriminant
features from patches extracted in the spectrogram representations of training
speech segments. Each speech segment is then encoded using the dictionary, and
the resulting feature set is used to perform classification of personality
traits. Experiments indicate that the proposed method achieves state-of-the-art
results with a significant reduction in complexity when compared to the most
recent reference methods. The number of features, and difficulties linked to
the feature extraction process are greatly reduced as only one type of
descriptors is used, for which the 6 parameters can be tuned automatically. In
contrast, the simplest reference method uses 4 types of descriptors to which 6
functionals are applied, resulting in over 20 parameters to be tuned.Comment: 12 pages, 3 figure
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