Fundamental frequency estimation in speech signals with variable rate particle filters

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 nonlinear state-space models. In this paper, we propose a speech model under a time-varying source-filter 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 (RBVRPF) 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.The authors would like to thank CSC Cambridge International Scholarship and Natural Science Foundation of China (No.61463035) for providing financial support.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TASLP.2016.253128

Signal processing techniques related to laboratory measurements of ultrasonic S-wave velocities in rocks

This thesis aims to evaluate the effect of signal processing techniques related to ultrasonic laboratory measurements of shear waves. Compressional and shear wave velocities play an important role in static elastic rock deformation behaviour estimation. Onsets of compressional and shear wave signal have to be determined in order to calculate the corresponding wave propagation velocity. Onset estimation by automation is especially problematic in shear wave signals due to noise caused by reflections and refractions, which results in inaccurate onset estimations and, therefore, requires manual onset picking which is time-inefficient and, hence, costly. Akaike Information Criterion (AIC) is the automated picking method applied to the ultrasonic signals in this thesis. By efficiently processing shear wave signals it was tried to optimize the results of the AIC. Ten processing techniques from biomedical engineering, statistical signal processing, audio and speech processing and RADAR applications were thoroughly researched. Their applicability to ultrasonic signals was reasoned based on literature. Six applicable signal processing techniques were eventually applied to 30 synthetic and 30 real ultrasonic signals. The mean and standard deviation of the error related to onset estimation before and after processing was used for evaluation. Visual comparison before and after processing was also executed to evaluate the visual impact of the processing techniques. Results showed that only a Butterworth high-pass filter visually enhances synthetic and real ultrasonic signals and improves the mean and standard deviation with respect to onset estimation. A Chebyshev high-pass filter also improved onset estimation results, but deteriorated the visual interpretation of the time signals. A simple amplitude filter unexpectedly provided the best results with respect to onset estimation. It is concluded from this studies that onset estimation by AIC can be improved by application of related signal processing techniques. This could be beneficial in estimation static deformation behaviour. Potential room for improvement is found within parameter optimisation and synthetic signal production

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Time-Varying Modeling of Glottal Source and Vocal Tract and Sequential Bayesian Estimation of Model Parameters for Speech Synthesis

abstract: Speech is generated by articulators acting on a phonatory source. Identification of this phonatory source and articulatory geometry are individually challenging and ill-posed problems, called speech separation and articulatory inversion, respectively. There exists a trade-off between decomposition and recovered articulatory geometry due to multiple possible mappings between an articulatory configuration and the speech produced. However, if measurements are obtained only from a microphone sensor, they lack any invasive insight and add additional challenge to an already difficult problem. A joint non-invasive estimation strategy that couples articulatory and phonatory knowledge would lead to better articulatory speech synthesis. In this thesis, a joint estimation strategy for speech separation and articulatory geometry recovery is studied. Unlike previous periodic/aperiodic decomposition methods that use stationary speech models within a frame, the proposed model presents a non-stationary speech decomposition method. A parametric glottal source model and an articulatory vocal tract response are represented in a dynamic state space formulation. The unknown parameters of the speech generation components are estimated using sequential Monte Carlo methods under some specific assumptions. The proposed approach is compared with other glottal inverse filtering methods, including iterative adaptive inverse filtering, state-space inverse filtering, and the quasi-closed phase method.Dissertation/ThesisMasters Thesis Electrical Engineering 201