Fundamental frequency estimation, known as pitch estimation in speech signals is of interest both to the research community and to industry. Meanwhile, the particle filter is known to be a powerful Bayesian inference method to track dynamic parameters in non-linear state-space models. In this paper, we propose a speech model under a time-varying sourcefilter speech model, and use variable rate particle filters (VRPF) to develop methods for estimation of pitch periods in speech signals. A Rao-Blackwellised variable rate particle filter (RB-VRPF) is also implemented. The proposed VRPF and RBVRPF are compared with a state-of-the-art pitch estimation algorithm, the YIN algorithm. Simulation results show that more accurate estimation of pitch can be obtained by VRPF and RBVRPF even under strong background noise conditions.

In recent years, ionic conductive hydrogels have been developed and applied in the field of energy storage devices owing to their unique properties, such as high ionic conductivity, mechanical flexibility,...

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Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to × 2) and energy (up to × 1.07) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.

Abstract.A previously published and widely quoted paper, "Price Dynamics in a Markovian Limit Order Market," [SIAM J. Financial Math., 4 (2013), pp. 1-25] has provided analytic solutions for many interesting quantities for the price dynamics of the limit order book. However, for unbalanced order flow cases, one of its major results, the price increase probability conditional on the state of the limit order book, is found to be incorrect due to its citing an erroneous formula from another paper. We correct this error by proposing another analytical solution using the characteristic functions of probability distributions. The final analytical solution presented in this paper is of a simple form and easy to calculate.Key words. limit order book, random walk, price dynamics, hitting probability AMS subject classifications. 60J27, 60E10, 60K25DOI. 10.1137/16M10574371. Introduction. The limit order book (LOB) is a depository of resting limit orders that provides liquidity, allowing participants to trade in the market. Much effort has been put into trying to extract information from the LOB and predict the direction of price change in the near future.A recent paper [2] showed that several interesting conditional probabilities, including the direction of the next price change conditional on the state of the LOB, can be obtained analytically using a simplified model for the LOB based on a previous model proposed in [4]. This simplified model can capture the dynamics of market orders and limit orders and their influence on price dynamics in a more analytically tractable way. The paper brought new insights into the LOB, helped people understand its dynamics [1], [8], [6], and inspired applications based on it, for example, building optimal trading strategies [9].However, we find that one of its major results, Proposition 3, which provides the analytic solution to the conditional probability of price movement of the LOB when the order flow is unbalanced, is incorrect. This proposition itself is important because nowadays much attention has been focused on the study of the order flow imbalance (OFI) process, which is thought to be more sensitive to market information than price [3], [10]. Also, Proposition 3 gives the short term price dynamics of the LOB driven by this OFI process under the Markovian assumption in [2].A numerical and mathematical proof for this mistake is provided in this correction note,