50,834 research outputs found
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
We propose new compressive parameter estimation algorithms that make use of
polar interpolation to improve the estimator precision. Our work extends
previous approaches involving polar interpolation for compressive parameter
estimation in two aspects: (i) we extend the formulation from real non-negative
amplitude parameters to arbitrary complex ones, and (ii) we allow for mismatch
between the manifold described by the parameters and its polar approximation.
To quantify the improvements afforded by the proposed extensions, we evaluate
six algorithms for estimation of parameters in sparse translation-invariant
signals, exemplified with the time delay estimation problem. The evaluation is
based on three performance metrics: estimator precision, sampling rate and
computational complexity. We use compressive sensing with all the algorithms to
lower the necessary sampling rate and show that it is still possible to attain
good estimation precision and keep the computational complexity low. Our
numerical experiments show that the proposed algorithms outperform existing
approaches that either leverage polynomial interpolation or are based on a
conversion to a frequency-estimation problem followed by a super-resolution
algorithm. The algorithms studied here provide various tradeoffs between
computational complexity, estimation precision, and necessary sampling rate.
The work shows that compressive sensing for the class of sparse
translation-invariant signals allows for a decrease in sampling rate and that
the use of polar interpolation increases the estimation precision.Comment: 13 pages, 5 figures, to appear in IEEE Transactions on Signal
Processing; minor edits and correction
A sticky HDP-HMM with application to speaker diarization
We consider the problem of speaker diarization, the problem of segmenting an
audio recording of a meeting into temporal segments corresponding to individual
speakers. The problem is rendered particularly difficult by the fact that we
are not allowed to assume knowledge of the number of people participating in
the meeting. To address this problem, we take a Bayesian nonparametric approach
to speaker diarization that builds on the hierarchical Dirichlet process hidden
Markov model (HDP-HMM) of Teh et al. [J. Amer. Statist. Assoc. 101 (2006)
1566--1581]. Although the basic HDP-HMM tends to over-segment the audio
data---creating redundant states and rapidly switching among them---we describe
an augmented HDP-HMM that provides effective control over the switching rate.
We also show that this augmentation makes it possible to treat emission
distributions nonparametrically. To scale the resulting architecture to
realistic diarization problems, we develop a sampling algorithm that employs a
truncated approximation of the Dirichlet process to jointly resample the full
state sequence, greatly improving mixing rates. Working with a benchmark NIST
data set, we show that our Bayesian nonparametric architecture yields
state-of-the-art speaker diarization results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS395 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Neural Discrete Representation Learning
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations
Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features
During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6–1.3 Hz), words (1.8–3 Hz), syllables (2.8–4.8 Hz), and phonemes (8–12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13–30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway
- …