10,976 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
Why musical memory can be preserved in advanced Alzheimer's disease
Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/brain/awv148) for a scientific commentary on this article.
Musical memory is relatively preserved in Alzheimer's disease and other dementias. In a 7 Tesla functional MRI study employing multi-voxel pattern analysis, Jacobsen et al. identify brain regions encoding long-term musical memory in young healthy controls, and show that these same regions display relatively little atrophy and hypometabolism in patients with Alzheimer's disease.See Clark and Warren (doi:10.1093/awv148) for a scientific commentary on this article
Self-Supervised Pretraining and Transfer Learning on fMRI Data with Transformers
Transfer learning is a machine learning technique founded on the idea that knowledge acquired by a model during “pretraining” on a source task can be transferred to the learning of a target task. Successful transfer learning can result in improved performance, faster convergence, and reduced demand for data. This technique is particularly desirable for the task of brain decoding in the domain of functional magnetic resonance imaging (fMRI), wherein even the most modern machine learning methods can struggle to decode labelled features of brain images. This challenge is due to the highly complex underlying signal, physical and neurological differences between brains, low data collection throughput, and other factors. Transfer learning is exciting in its potential to mitigate these challenges, but with this application still in its infancy, we must begin on the ground floor. The goals of this thesis were to design, implement, and evaluate a framework for pretraining and transfer learning on arbitrary fMRI datasets, then demonstrate its performance with respect to the literature, and achieve substantive progress toward generalized pretrained models of the brain. The primary contribution is our novel framework which achieves these goals, called BEAT, which stands for Bi-directional Encoders for Auditory Tasks. The design and implementation of BEAT include adapting state-of-the-art deep learning architectures to sequences of fMRI data, as well as a novel self-supervised pretraining task called Next Thought Prediction and several novel supervised brain decoding tasks. To evaluate BEAT, we pretrained ii on Next Thought Prediction and performed transfer learning to the brain decoding tasks, which are specific to one of three fMRI datasets. To demonstrate significant benefits of transfer learning, BEAT decoded instrumental timbre from one of the fMRI datasets which standard methods failed to decode in addition to improved downstream performance. Toward generalized pretrained models of the brain, BEAT learned Next Thought Prediction on one fMRI dataset, and then successfully transferred that learning to a supervised brain decoding task on an entirely distinct dataset, with different participants and stimuli. To our knowledge this is the first instance of transfer learning across participants and stimuli–a necessity for whole-brain pretrained models
Audio Features Affected by Music Expressiveness
Within a Music Information Retrieval perspective, the goal of the study
presented here is to investigate the impact on sound features of the musician's
affective intention, namely when trying to intentionally convey emotional
contents via expressiveness. A preliminary experiment has been performed
involving tuba players. The recordings have been analysed by extracting a
variety of features, which have been subsequently evaluated by combining both
classic and machine learning statistical techniques. Results are reported and
discussed.Comment: Submitted to ACM SIGIR Conference on Research and Development in
Information Retrieval (SIGIR 2016), Pisa, Italy, July 17-21, 201
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