21,569 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
Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm
Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective. However, these approaches do not consider neurophysiological realities and may ignore important features that could improve the accuracy of these methods. Further, standard algorithms typically require selection of at least one free parameter, which can have significant effects on the quality of the output. We describe a Heuristic Spike Sorting Tuner (HSST) that determines the optimal choice of the free parameters for a given spike sorting algorithm based on the neurophysiological qualification of unit isolation and signal discrimination. A set of heuristic metrics are used to score the output of a spike sorting algorithm over a range of free parameters resulting in optimal sorting quality. We demonstrate that these metrics can be used to tune parameters in several spike sorting algorithms. The HSST algorithm shows robustness to variations in signal to noise ratio, number and relative size of units per channel. Moreover, the HSST algorithm is computationally efficient, operates unsupervised, and is parallelizable for batch processing
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
A Novel Hybrid CNN-AIS Visual Pattern Recognition Engine
Machine learning methods are used today for most recognition problems.
Convolutional Neural Networks (CNN) have time and again proved successful for
many image processing tasks primarily for their architecture. In this paper we
propose to apply CNN to small data sets like for example, personal albums or
other similar environs where the size of training dataset is a limitation,
within the framework of a proposed hybrid CNN-AIS model. We use Artificial
Immune System Principles to enhance small size of training data set. A layer of
Clonal Selection is added to the local filtering and max pooling of CNN
Architecture. The proposed Architecture is evaluated using the standard MNIST
dataset by limiting the data size and also with a small personal data sample
belonging to two different classes. Experimental results show that the proposed
hybrid CNN-AIS based recognition engine works well when the size of training
data is limited in siz
Perceptual-based textures for scene labeling: a bottom-up and a top-down approach
Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label
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