State-of-the-art end-to-end automatic speech recognition (ASR) extracts acoustic features from input speech signal every 10 ms which corresponds to a frame rate of 100 frames second. In this report, we investigate the use of high-frame-rate features extraction in end-to-end ASR. High frame rates of 200 and 400 frames second are used in the features extraction and provide additional information for end-to-end ASR. The effectiveness of high-frame-rate features extraction is evaluated independently and in combination with speed perturbation based data augmentation. Experiments performed on two speech corpora, Wall Street Journal (WSJ) and CHiME-5, show that using high-frame-rate features extraction yields improved performance for end-to-end ASR, both independently and in combination with speed perturbation. On WSJ corpus, the relative reduction of word error rate (WER) yielded by high-frame-rate features extraction independently and in combination with speed perturbation are up to 21.3 and 24.1 , respectively. On CHiME-5 corpus, the corresponding relative WER reductions are up to 2.8 and 7.9 , respectively, on the test data recorded by microphone arrays and up to 11.8 and 21.2 , respectively, on the test data recorded by binaural microphones.
Speed perturbation @cite_5 is a data augmentation technique which creates warped time signals in addition to the original speech signals. Given an audio signal of length @math and a warping factor @math , speed perturbation creates a new signal with duration @math by resampling the original signal with a sampling rate of @math , where @math is the sampling rate of the original signal. Speed perturbation shifts the speech spectrum and also results in change in number of frames as the duration of the resulting signal is different @cite_5 .
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In this work, a novel deep learning approach to unfold nuclear power reactor signals is proposed. It includes a combination of convolutional neural networks (CNN), denoising autoencoders (DAE) and @math -means clustering of representations. Monitoring nuclear reactors while running at nominal conditions is critical. Based on analysis of the core reactor neutron flux, it is possible to derive useful information for building fault anomaly detection systems. By leveraging signal and image pre-processing techniques, the high and low energy spectra of the signals were appropriated into a compatible format for CNN training. Firstly, a CNN was employed to unfold the signal into either twelve or forty-eight perturbation location sources, followed by a @math -means clustering and @math -Nearest Neighbour coarse-to-fine procedure, which significantly increases the unfolding resolution. Secondly, a DAE was utilised to denoise and reconstruct power reactor signals at varying levels of noise and or corruption. The reconstructed signals were evaluated w.r.t. their original counter parts, by way of normalised cross correlation and unfolding metrics. The results illustrate that the origin of perturbations can be localised with high accuracy, despite limited training data and obscured @math noisy signals, across various levels of granularity. Data augmentation is a common strategy adopted to increase the quantity of training data, avoid overfitting and improve robustness of the models. In this paper, we investigate audio-level speech augmentation methods which directly process the raw signal. The method we particularly recommend is to change the speed of the audio signal, producing 3 versions of the original signal with speed factors of 0.9, 1.0 and 1.1. The proposed technique has a low implementation cost, making it easy to adopt. We present results on 4 different LVCSR tasks with training data ranging from 100 hours to 1000 hours, to examine the effectiveness of audio augmentation in a variety of data scenarios. An average relative improvement of 4.3 was observed across the 4 tasks. In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learns to take undersampled measurements and recover signals from them using a deep convolutional neural network. In other words, it learns a transformation from the original signals to a near-optimal number of undersampled measurements and the inverse transformation from measurements to signals. This is in contrast to traditional compressive sensing (CS) systems that use random linear measurements and convex optimization or iterative algorithms for signal recovery. We compare our new framework with @math -minimization from the phase transition point of view and demonstrate that it outperforms @math -minimization in the regions of phase transition plot where @math -minimization cannot recover the exact solution. In addition, we experimentally demonstrate how learning measurements enhances the overall recovery performance, speeds up training of recovery framework, and leads to having fewer parameters to learn. Multireference alignment (MRA) is the problem of estimating a signal from many noisy and cyclically shifted copies of itself. In this paper, we consider an extension called heterogeneous MRA, where @math signals must be estimated, and each observation comes from one of those signals, unknown to us. This is a simplified model for the heterogeneity problem notably arising in cryo-electron microscopy. We propose an algorithm which estimates the @math signals without estimating either the shifts or the classes of the observations. It requires only one pass over the data and is based on low-order moments that are invariant under cyclic shifts. Given sufficiently many measurements, one can estimate these invariant features averaged over the @math signals. We then design a smooth, non-convex optimization problem to compute a set of signals which are consistent with the estimated averaged features. We find that, in many cases, the proposed approach estimates the set of signals accurately despite non-convexity, and conjecture the number of signals @math that can be resolved as a function of the signal length @math is @math . (MATH) We give an algorithm for finding a Fourier representation R of B terms for a given discrete signal signal A of length N, such that @math is within the factor (1 +e) of best possible @math . Our algorithm can access A by reading its values on a sample set T ⊆[0,N), chosen randomly from a (non-product) distribution of our choice, independent of A. That is, we sample non-adaptively. The total time cost of the algorithm is polynomial in B log(N)log(M)e (where M is the ratio of largest to smallest numerical quantity encountered), which implies a similar bound for the number of samples. In a recent paper, Hou and Shi introduced a new adaptive data analysis method to analyze nonlinear and non-stationary data. The main idea is to look for the sparsest representation of multiscale data within the largest possible dictionary consisting of intrinsic mode functions of the form @math , where @math , @math consists of the functions smoother than @math and @math . This problem was formulated as a nonlinear @math optimization problem and an iterative nonlinear matching pursuit method was proposed to solve this nonlinear optimization problem. In this paper, we prove the convergence of this nonlinear matching pursuit method under some sparsity assumption on the signal. We consider both well-resolved and sparse sampled signals. In the case without noise, we prove that our method gives exact recovery of the original signal. We define the relevant information in a signal @math as being the information that this signal provides about another signal @math . Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal @math requires more than just predicting @math , it also requires specifying which features of @math play a role in the prediction. We formalize this problem as that of finding a short code for @math that preserves the maximum information about @math . That is, we squeeze the information that @math provides about @math through a bottleneck' formed by a limited set of codewords @math . This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure @math emerges from the joint statistics of @math and @math . This approach yields an exact set of self consistent equations for the coding rules @math and @math . Solutions to these equations can be found by a convergent re-estimation method that generalizes the Blahut-Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere. The massive amount of digital music data available necessitates automated methods for processing, classifying and organizing large volumes of songs. As music discovery and interactive music applications become commonplace, the ability to synchronize lyric text information with an audio recording has gained interest. This paper presents an approach for lyric-audio alignment by comparing synthesized speech with a vocal track removed from an instrument mixture using source separation. We take a hierarchical approach to solve the problem, assuming a set of paragraph-music segment pairs is given and focus on within-segment lyric alignment at the word level. A synthesized speech signal is generated to reflect the properties of the music signal by controlling the speech rate and gender. Dynamic time warping finds the shortest path between the synthesized speech and separated vocal. The resulting path is used to calculate the timestamps of words in the original signal. The system results in approximately half a second of misalignment error on average. Finally, we discuss the challenges and suggest improvements to the method. In recent years, there is a growing need for processing methods aimed at extracting useful information from large datasets. In many cases the challenge is to discover a low-dimensional structure in the data, often concealed by the existence of nuisance parameters and noise. Motivated by such challenges, we consider the problem of estimating a signal from its scaled, cyclically-shifted and noisy observations. We focus on the particularly challenging regime of low signal-to-noise ratio (SNR), where different observations cannot be shift-aligned. We show that an accurate estimation of the signal from its noisy observations is possible, and derive a procedure which is proved to consistently estimate the signal. The asymptotic sample complexity (the number of observations required to recover the signal) of the procedure is @math . Additionally, we propose a procedure which is experimentally shown to improve the sample complexity by a factor equal to the signal's length. Finally, we present numerical experiments which demonstrate the performance of our algorithms, and corroborate our theoretical findings. We study the compressed sensing reconstruction problem for a broad class of random, band-diagonal sensing matrices. This construction is inspired by the idea of spatial coupling in coding theory. As demonstrated heuristically and numerically by KrzakalaEtAl , message passing algorithms can effectively solve the reconstruction problem for spatially coupled measurements with undersampling rates close to the fraction of non-zero coordinates. We use an approximate message passing (AMP) algorithm and analyze it through the state evolution method. We give a rigorous proof that this approach is successful as soon as the undersampling rate @math exceeds the (upper) R 'enyi information dimension of the signal, @math . More precisely, for a sequence of signals of diverging dimension @math whose empirical distribution converges to @math , reconstruction is with high probability successful from @math measurements taken according to a band diagonal matrix. For sparse signals, i.e., sequences of dimension @math and @math non-zero entries, this implies reconstruction from @math measurements. For discrete' signals, i.e., signals whose coordinates take a fixed finite set of values, this implies reconstruction from @math measurements. The result is robust with respect to noise, does not apply uniquely to random signals, but requires the knowledge of the empirical distribution of the signal @math . Suppose that we have @math sensors and each one intends to send a function @math (e.g., a signal or an image) to a receiver common to all @math sensors. During transmission, each @math gets convolved with a function @math . The receiver records the function @math , given by the sum of all these convolved signals. When and under which conditions is it possible to recover the individual signals @math and the blurring functions @math from just one received signal @math ? This challenging problem, which intertwines blind deconvolution with blind demixing, appears in a variety of applications, such as audio processing, image processing, neuroscience, spectroscopy, and astronomy. It is also expected to play a central role in connection with the future Internet-of-Things. We will prove that under reasonable and practical assumptions, it is possible to solve this, otherwise, highly ill-posed problem and recover the @math transmitted functions @math and the impulse responses @math in a robust, reliable, and efficient manner, from just one single received function @math by solving a semidefinite program. We derive explicit bounds on the number of measurements needed for successful recovery and prove that our method is robust in the presence of noise. Our theory is actually suboptimal, since numerical experiments demonstrate that, quite remarkably, recovery is still possible if the number of measurements is close to the number of degrees of freedom. In an apparatus having at least one rotary head for reproducing video signals recorded in successive parallel tracks extending obliquely across a tape which is advanced longitudinally and also has control signals recorded therein in predetermined positional relationship to the respective record tracks to be reproduced by a fixed transducer or head with a repetition rate that is dependent on the speed of advancement of the tape: a speed and phase control system for the rotary head or heads has a rotational speed detector for producing pulse signals representative of the rotational position of each rotary head and having a repetition rate that is a function of the rotational speed of the rotary head; a phase comparator for producing a phase responsive signal having a voltage level that is varied in response to changes in the relative timing of the reproduced control signals and the pulse signals; a digital device for producing a speed responsive signal having a voltage level that generally corresponds to the repetition rate of the pulse signals, but which is varied in response to changes in the repetition rate of the pulse signals only when such changes occur without corresponding changes in the repetition rate of the reproduced control signals; and a brake or other means for controlling the rotation of the rotary head in response to the voltage levels of the phase responsive signal and the speed responsive signal. In a preferred embodiment, the digital device for producing the speed responsive signal includes a source of clock pulses, a counter for counting the clock pulses for time intervals determined by the repetition rate of the pulse signals from the rotational speed detector, a digital-analog converter for converting the count of the counter, at the end of the mentioned time intervals, to a corresponding voltage level of the speed responsive signal, and an arrangement for establishing the initial count of the counter at the commencement of each of the time intervals in dependence on the repetition rate of the reproduced control signals. In this paper, we present methods in deep multimodal learning for fusing speech and visual modalities for Audio-Visual Automatic Speech Recognition (AV-ASR). First, we study an approach where uni-modal deep networks are trained separately and their final hidden layers fused to obtain a joint feature space in which another deep network is built. While the audio network alone achieves a phone error rate (PER) of @math under clean condition on the IBM large vocabulary audio-visual studio dataset, this fusion model achieves a PER of @math demonstrating the tremendous value of the visual channel in phone classification even in audio with high signal to noise ratio. Second, we present a new deep network architecture that uses a bilinear softmax layer to account for class specific correlations between modalities. We show that combining the posteriors from the bilinear networks with those from the fused model mentioned above results in a further significant phone error rate reduction, yielding a final PER of @math . The fast growing field of compressed sensing is founded on the fact that if a signal is 'simple' and has some 'structure', then it can be reconstructed accurately with far fewer samples than its ambient dimension. Many different plausible structures have been explored in this field, ranging from sparsity to low-rankness and to finite rate of innovation. However, there are important abstract questions that are yet to be answered. For instance, what are the general abstract meanings of 'structure' and 'simplicity'? Do there exist universal algorithms for recovering such simple structured objects from fewer samples than their ambient dimension? In this paper, we aim to address these two questions. Using algorithmic information theory tools such as Kolmogorov complexity, we provide a unified method of describing 'simplicity' and 'structure'. We then explore the performance of an algorithm motivated by Ocam's Razor (called MCP for minimum complexity pursuit) and show that it requires @math number of samples to recover a signal, where @math and @math represent its complexity and ambient dimension, respectively. Finally, we discuss more general classes of signals and provide guarantees on the performance of MCP. Binary measurements arise naturally in a variety of statistics and engineering applications. They may be inherent to the problem—for example, in determining the relationship between genetics and the presence or absence of a disease—or they may be a result of extreme quantization. A recent influx of literature has suggested that using prior signal information can greatly improve the ability to reconstruct a signal from binary measurements. This is exemplified by one-bit compressed sensing , which takes the compressed sensing model but assumes that only the sign of each measurement is retained. It has recently been shown that the number of one-bit measurements required for signal estimation mirrors that of unquantized compressed sensing. Indeed, @math -sparse signals in @math can be estimated (up to normalization) from @math one-bit measurements. Nevertheless, controlling the precise accuracy of the error estimate remains an open challenge. In this paper, we focus on optimizing the decay of the error as a function of the oversampling factor @math , where @math is the number of measurements. It is known that the error in reconstructing sparse signals from standard one-bit measurements is bounded below by @math . Without adjusting the measurement procedure, reducing this polynomial error decay rate is impossible. However, we show that an adaptive choice of the thresholds used for quantization can lower the error rate to @math . This improves upon guarantees for other methods of adaptive thresholding, such as sigma–delta quantization. We develop a general recursive strategy to achieve this exponential decay and two specific polynomial-time algorithms, which fall into this framework, one based on convex programming and one on hard thresholding. Our work bridges the one-bit compressed sensing model, in which the engineer controls the measurement procedure, to sigma–delta and successive approximation quantization. Moreover, the principle is extendable to signal reconstruction problems in a variety of binary statistical models as well as statistical estimation problems like logistic regression. We consider streaming, one-pass principal component analysis (PCA), in the high-dimensional regime, with limited memory. Here, @math -dimensional samples are presented sequentially, and the goal is to produce the @math -dimensional subspace that best approximates these points. Standard algorithms require @math memory; meanwhile no algorithm can do better than @math memory, since this is what the output itself requires. Memory (or storage) complexity is most meaningful when understood in the context of computational and sample complexity. Sample complexity for high-dimensional PCA is typically studied in the setting of the spiked covariance model , where @math -dimensional points are generated from a population covariance equal to the identity (white noise) plus a low-dimensional perturbation (the spike) which is the signal to be recovered. It is now well-understood that the spike can be recovered when the number of samples, @math , scales proportionally with the dimension, @math . Yet, all algorithms that provably achieve this, have memory complexity @math . Meanwhile, algorithms with memory-complexity @math do not have provable bounds on sample complexity comparable to @math . We present an algorithm that achieves both: it uses @math memory (meaning storage of any kind) and is able to compute the @math -dimensional spike with @math sample-complexity -- the first algorithm of its kind. While our theoretical analysis focuses on the spiked covariance model, our simulations show that our algorithm is successful on much more general models for the data. Binary measurements arise naturally in a variety of statistical and engineering applications. They may be inherent to the problem---e.g., in determining the relationship between genetics and the presence or absence of a disease---or they may be a result of extreme quantization. In one-bit compressed sensing it has recently been shown that the number of one-bit measurements required for signal estimation mirrors that of unquantized compressed sensing. Indeed, @math -sparse signals in @math can be estimated (up to normalization) from @math one-bit measurements. Nevertheless, controlling the precise accuracy of the error estimate remains an open challenge. In this paper, we focus on optimizing the decay of the error as a function of the oversampling factor @math , where @math is the number of measurements. It is known that the error in reconstructing sparse signals from standard one-bit measurements is bounded below by @math . Without adjusting the measurement procedure, reducing this polynomial error decay rate is impossible. However, we show that an adaptive choice of the thresholds used for quantization may lower the error rate to @math . This improves upon guarantees for other methods of adaptive thresholding as proposed in Sigma-Delta quantization. We develop a general recursive strategy to achieve this exponential decay and two specific polynomial-time algorithms which fall into this framework, one based on convex programming and one on hard thresholding. This work is inspired by the one-bit compressed sensing model, in which the engineer controls the measurement procedure. Nevertheless, the principle is extendable to signal reconstruction problems in a variety of binary statistical models as well as statistical estimation problems like logistic regression. Consider the problem of recovering an unknown signal from undersampled measurements, given the knowledge that the signal has a sparse representation in a specified dictionary @math . This problem is now understood to be well-posed and efficiently solvable under suitable assumptions on the measurements and dictionary if the number of measurements scales roughly with the sparsity level. One sufficient condition for such is the @math -restricted isometry property ( @math -RIP), which asks that the sampling matrix approximately preserve the norm of all signals which are sufficiently sparse in @math . While many classes of random matrices are known to satisfy such conditions, such matrices are not representative of the structural constraints imposed by practical sensing systems. We close this gap in the theory by demonstrating that one can subsample a fixed orthogonal matrix in such a way that the @math -RIP will hold, provided this basis is sufficiently incoherent with the sparsifying dictionary $ ... Suppose we are given a time series or a signal @math for @math . We consider the problem of predicting the signal in the interval @math based on a knowledge of its history and nothing more. We ask the following question: what is the largest value of @math for which a prediction can be made? We show that the answer to this question is contained in a fundamental result of information theory due to Wyner, Ziv, Ornstein, and Weiss. In particular, for the class of chaotic signals, the upper bound is @math in the limit @math , with @math being entropy in a sense that is explained in the text. If @math is small for @math , where @math is of the order of a characteristic time scale, the pattern of events leading up to @math is similar to the pattern of events leading up to @math . It is reasonable to expect @math to be a good predictor of @math . All existing methods for prediction use this idea... In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers. Compared to the first-order gradient-based online algorithm, we show that ZOO-ADMM requires @math times more iterations, leading to a convergence rate of @math , where @math is the number of optimization variables, and @math is the number of iterations. To accelerate ZOO-ADMM, we propose two minibatch strategies: gradient sample averaging and observation averaging, resulting in an improved convergence rate of @math , where @math is the minibatch size. In addition to convergence analysis, we also demonstrate ZOO-ADMM to applications in signal processing, statistics, and machine learning.