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Factor Analysis for Spectral Estimation
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<span title="2017-05-08">2017</span>
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Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this
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... bspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.
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Synthesizing developmental trajectories
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<span title="2017-06-30">2017</span>
<i title="Cold Spring Harbor Laboratory">
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a,1 , Joakim Andén b,1 , Bomyi Lim a,c , Hang Lu d,e , Ioannis G. Kevrekidis b,c , Amit Singer b,f , and Stanislav Y. ...
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Structural Variability from Noisy Tomographic Projections
[article]
<span title="2018-02-07">2018</span>
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arXiv
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[35] and Andén et al. [3] , it can be applied efficiently to a wide range of data. ...
The use of the conjugate gradient method to estimate the covariance was previously considered in Andén et al. ...
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Classification with Joint Time-Frequency Scattering
[article]
<span title="2018-07-24">2018</span>
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arXiv
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We use a 400-phrase validation set to optimize hyperparameters (see Andén & Mallat [12] ). ...
Andén is with the Flatiron Institute, New York, NY, USA (e-mail: janden@flatironinstitute.org). V. ...
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Extended playing techniques: The next milestone in musical instrument recognition
[article]
<span title="2018-08-29">2018</span>
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arXiv
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The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening
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... tems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.
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Multitaper estimation on arbitrary domains
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<span title="2020-06-18">2020</span>
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arXiv
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Multitaper estimators have enjoyed significant success in estimating spectral densities from finite samples using as tapers Slepian functions defined on the acquisition domain. Unfortunately, the numerical calculation of these Slepian tapers is only tractable for certain symmetric domains, such as rectangles or disks. In addition, no performance bounds are currently available for the mean squared error of the spectral density estimate. This situation is inadequate for applications such as
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... lectron microscopy, where noise models must be estimated from irregular domains with small sample sizes. We show that the multitaper estimator only depends on the linear space spanned by the tapers. As a result, Slepian tapers may be replaced by proxy tapers spanning the same subspace (validating the common practice of using partially converged solutions to the Slepian eigenproblem as tapers). These proxies may consequently be calculated using standard numerical algorithms for block diagonalization. We also prove a set of performance bounds for multitaper estimators on arbitrary domains. The method is demonstrated on synthetic and experimental datasets from cryo-electron microscopy, where it reduces mean squared error by a factor of two or more compared to traditional methods.
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Structural Variability from Noisy Tomographic Projections
<span title="2018-05-31">2018</span>
<i title="Society for Industrial & Applied Mathematics (SIAM)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/amdwhvj4srb33cknicgjbgvsze" style="color: black;">SIAM Journal of Imaging Sciences</a>
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[38] and Andén et al. [3] , it can be applied efficiently to a wide range of data. ...
The use of the conjugate gradient method to estimate the covariance was previously considered in Andén et al. ...
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Synthesizing developmental trajectories
<span title="2017-09-18">2017</span>
<i title="Public Library of Science (PLoS)">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ch57atmlprauhhbqdf7x4ytejm" style="color: black;">PLoS Computational Biology</a>
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a,1 , Joakim Andén b,1 , Bomyi Lim a,c , Hang Lu d,e , Ioannis G. Kevrekidis b,c , Amit Singer b,f , and Stanislav Y. ...
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Deep Scattering Spectrum
<span title="">2014</span>
<i title="Institute of Electrical and Electronics Engineers (IEEE)">
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<span title="2013-04-24">2013</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1304.6763v1">arXiv:1304.6763v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n7hydaincnevlmilk6pgji2sy4">fatcat:n7hydaincnevlmilk6pgji2sy4</a> </span>Other Versions
<span title="2014-01-10">2014</span>
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A scattering transform defines a locally translation invariant representation which is stable to time-warping deformations. It extends MFCC representations by computing modulation spectrum coefficients of multiple orders, through cascades of wavelet convolutions and modulus operators. Second-order scattering coefficients characterize transient phenomena such as attacks and amplitude modulation. A frequency transposition invariant representation is obtained by applying a scattering transform
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... g log-frequency. State-the-of-art classification results are obtained for musical genre and phone classification on GTZAN and TIMIT databases, respectively.
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APPLE picker: Automatic particle picking, a low-effort cryo-EM framework
<span title="">2018</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jd46qpwqmngwvbsru66azubidu" style="color: black;">Journal of Structural Biology</a>
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Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.jsb.2018.08.012">doi:10.1016/j.jsb.2018.08.012</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30134153">pmid:30134153</a>
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... nto the selection process. In addition, this approach is still somewhat timeconsuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of β-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.
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Factorization of the translation kernel for fast rigid image alignment
[article]
<span title="2019-10-04">2019</span>
<i >
arXiv
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An important component of many image alignment methods is the calculation of inner products (correlations) between an image of n× n pixels and another image translated by some shift and rotated by some angle. For robust alignment of an image pair, the number of considered shifts and angles is typically high, thus the inner product calculation becomes a bottleneck. Existing methods, based on fast Fourier transforms (FFTs), compute all such inner products with computational complexity O(n^3 log
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1905.12317v2">arXiv:1905.12317v2</a>
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... per image pair, which is reduced to O(N n^2) if only N distinct shifts are needed. We propose to use a factorization of the translation kernel (FTK), an optimal interpolation method which represents images in a Fourier–Bessel basis and uses a rank-H approximation of the translation kernel via an operator singular value decomposition (SVD). Its complexity is O(Hn(n + N)) per image pair. We prove that H = O((W + log(1/ϵ))^2), where 2W is the magnitude of the maximum desired shift in pixels and ϵ is the desired accuracy. For fixed W this leads to an acceleration when N is large, such as when sub-pixel shift grids are considered. Finally, we present numerical results in an electron cryomicroscopy application showing speedup factors of 3-10 with respect to the state of the art.
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APPLE Picker: Automatic Particle Picking, a Low-Effort Cryo-EM Framework
[article]
<span title="2018-06-14">2018</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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<span title="2018-02-01">2018</span>
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Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While semi-automated particle picking is currently a popular approach, it may suffer from introducing manual bias
<span class="external-identifiers">
<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.00469v2">arXiv:1802.00469v2</a>
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... nto the selection process. In addition, semi-automated particle picking is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and fully automatic particle picking. While our approach was inspired by template matching, it is completely template-free. This approach is evaluated on publicly available datasets containing micrographs of β-galactosidase and keyhole limpet hemocyanin projections.
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Wavelet Moments for Cosmological Parameter Estimation
[article]
<span title="2022-04-15">2022</span>
<i >
arXiv
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<span class="release-stage" >pre-print</span>
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& Mallat 2014; Andén et al. 2019; Lostanlen & Cella 2017) , and biomedical data (Chudáček et al. 2014a,b; Villoutreix et al. 2017; Warrick et al. 2020) but was soon also shown to yield state-of-the-art ...
theoretical principle developed in Mallat (2012) was initially applied to natural images (Bruna & Mallat 2013; Oyallon & Mallat 2015; Oyallon et al. 2017) , textures (Sifre & Mallat 2013) , audio signals (Andén ...
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Bias and variance reduction and denoising for CTF Estimation
[article]
<span title="2020-01-28">2020</span>
<i >
arXiv
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These estimators have recently demonstrated their usefulness for noise power spectrum estimation in cryo-EM (Andén & Singer, 2017; Andén & Romero, 2019) . ...
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Representing environmental sounds using the separable scattering transform
<span title="">2013</span>
<i title="IEEE">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2013 IEEE International Conference on Acoustics, Speech and Signal Processing</a>
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Environmental sounds are an interesting subject of study for machine audition because of their wide variety of acoustical characteristics and their central presence in our everyday life. They are perceived effortlessly in the human auditory system whereas state-of-the-art computational systems are far from reaching the same efficiency. In this paper we propose a novel representation of such sounds based on the scattering transform which has the property of stability to time-warping deformations
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2013.6639358">doi:10.1109/icassp.2013.6639358</a>
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... and invariance to time-shift useful for classifications tasks. This representation is compared to several state-of-the-art approaches for the task of quantifying similarity between environmental sounds.
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