368 research outputs found
Deformable Spectrograms
Speech and other natural sounds show high temporal correlation and smooth spectral evolution punctuated by a few, irregular and abrupt changes. In a conventional Hidden Markov Model (HMM), such structure is represented weakly and indirectly through transitions between explicit states representing 'steps' along such smooth changes. It would be more efficient and informative to model successive spectra as transformations of their immediate predecessors, and we present a model which focuses on local deformations of adjacent bins in a time-frequency surface to explain an observed sound, using explicit representation only for those bins that cannot be predicted from their context. We further decompose the log-spectrum into two additive layers, which are able to separately explain and model the evolution of the harmonic excitation, and formant filtering of speech and similar sounds. Smooth deformations are modeled with hidden transformation variables in both layers, using Markov Random Fields (MRFs) with overlapping subwindows as observations; inference is efficiently performed via loopy belief propagation. The model can fill-in deleted time-frequency cells without any signal model, and an entire signal can be compactly represented with a few specific states along with the deformation maps for both layers. We discuss several possible applications for this new model, including source separation
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
Printable microscale interfaces for long-term peripheral nerve mapping and precision control
The nascent field of bioelectronic medicine seeks to decode and modulate peripheral nervous system signals to obtain therapeutic control of targeted end organs and effectors. Current approaches rely heavily on electrode-based devices, but size scalability, material and microfabrication challenges, limited surgical accessibility, and the biomechanically dynamic implantation environment are significant impediments to developing and deploying advanced peripheral interfacing technologies. Here, we present a microscale implantable device – the nanoclip – for chronic interfacing with fine peripheral nerves in small animal models that begins to meet these constraints. We demonstrate the capability to make stable, high-resolution recordings of behaviorally-linked nerve activity over multi-week timescales. In addition, we show that multi-channel, current-steering-based stimulation can achieve a high degree of functionally-relevant modulatory specificity within the small scale of the device. These results highlight the potential of new microscale design and fabrication techniques for the realization of viable implantable devices for long-term peripheral interfacing.https://www.biorxiv.org/node/801468.fullFirst author draf
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A Methodology for Continuous Monitoring of Rail Corrugation on Subway Lines Based on Axlebox Acceleration Measurements
Rail corrugation is a degradation phenomenon that manifests as a quasi-periodic irregularity on the running surface of the rail. It is a critical problem for urban railway lines because it induces ground-borne vibrations transmitted to the buildings near the infrastructure, causing complaints from the inhabitants. A typical treatment to mitigate the rail corrugation problem is the periodic grinding of the rails, performed by dedicated vehicles. The scheduling of rail maintenance is particularly critical because it can be performed only when the service is interrupted. A procedure for the continuous monitoring of rail corrugation is proposed, based on axlebox acceleration measurements. The rail irregularity is estimated from the measured acceleration by means of a frequency domain model of vertical dynamics of the wheel–rail interaction. The results obtained by using two different methods (a state-of-the-art method and a new one) are compared. Finally, the study of the evolution of the power content of the rail irregularity enables the identification of the track sections where corrugation is developing and rail grinding is necessary
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