Linear and Nonlinear Encoding Properties of an Identified Mechanoreceptor on the Fly wing Measured with Mechanical Noise Stimuli

The wing blades of most flies contain a small set of distal campaniform sensilla, mechanoreceptors that respond to deformations of the cuticle. This paper describes a method of analysis based upon mechanical noise stimuli which is used to quantify the encoding properties of one of these sensilla (the d-HCV cell) on the wing of the blowfly Calliphora vomitoria (L.). The neurone is modelled as two components, a linear filter that accounts for the frequency response and phase characteristics of the cell, followed by a static nonlinearity that limits the spike discharge to a narrow portion of the stimulus cycle. The model is successful in predicting the response of campaniform neurones to arbitrary stimuli, and provides a convenient method for quantifying the encoding properties of the sensilla. The d-HCV neurone is only broadly frequency tuned, but its maximal response near 150 Hz corresponds to the wingbeat frequency of Calliphora. In the range of frequencies likely to be encountered during flight, the d-HCV neurone fires a single phase-locked action potential for each stimulus cycle. The phase lag of the cell decreases linearly with increasing frequency such that the absolute delay between stimulus and response remains nearly constant. Thus, during flight the neurone is capable of firing one precisely timed action potential during each wingbeat, and might be used to modulate motor activity that requires afferent input on a cycle-by-cycle basis

Rank-1 Constrained Multichannel Wiener Filter for Speech Recognition in Noisy Environments

Multichannel linear filters, such as the Multichannel Wiener Filter (MWF) and the Generalized Eigenvalue (GEV) beamformer are popular signal processing techniques which can improve speech recognition performance. In this paper, we present an experimental study on these linear filters in a specific speech recognition task, namely the CHiME-4 challenge, which features real recordings in multiple noisy environments. Specifically, the rank-1 MWF is employed for noise reduction and a new constant residual noise power constraint is derived which enhances the recognition performance. To fulfill the underlying rank-1 assumption, the speech covariance matrix is reconstructed based on eigenvectors or generalized eigenvectors. Then the rank-1 constrained MWF is evaluated with alternative multichannel linear filters under the same framework, which involves a Bidirectional Long Short-Term Memory (BLSTM) network for mask estimation. The proposed filter outperforms alternative ones, leading to a 40% relative Word Error Rate (WER) reduction compared with the baseline Weighted Delay and Sum (WDAS) beamformer on the real test set, and a 15% relative WER reduction compared with the GEV-BAN method. The results also suggest that the speech recognition accuracy correlates more with the Mel-frequency cepstral coefficients (MFCC) feature variance than with the noise reduction or the speech distortion level.Comment: for Computer Speech and Languag

Data Unfolding with Wiener-SVD Method

Data unfolding is a common analysis technique used in HEP data analysis. Inspired by the deconvolution technique in the digital signal processing, a new unfolding technique based on the SVD technique and the well-known Wiener filter is introduced. The Wiener-SVD unfolding approach achieves the unfolding by maximizing the signal to noise ratios in the effective frequency domain given expectations of signal and noise and is free from regularization parameter. Through a couple examples, the pros and cons of the Wiener-SVD approach as well as the nature of the unfolded results are discussed.Comment: 26 pages, 12 figures, match the accepted version by JINS

Quantum theory of optical temporal phase and instantaneous frequency. II. Continuous time limit and state-variable approach to phase-locked loop design

We consider the continuous-time version of our recently proposed quantum theory of optical temporal phase and instantaneous frequency [Tsang, Shapiro, and Lloyd, Phys. Rev. A 78, 053820 (2008)]. Using a state-variable approach to estimation, we design homodyne phase-locked loops that can measure the temporal phase with quantum-limited accuracy. We show that post-processing can further improve the estimation performance, if delay is allowed in the estimation. We also investigate the fundamental uncertainties in the simultaneous estimation of harmonic-oscillator position and momentum via continuous optical phase measurements from the classical estimation theory perspective. In the case of delayed estimation, we find that the inferred uncertainty product can drop below that allowed by the Heisenberg uncertainty relation. Although this result seems counter-intuitive, we argue that it does not violate any basic principle of quantum mechanics.Comment: 11 pages, 6 figures, v2: accepted by PR

Deep Denoising for Hearing Aid Applications

Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. In this work, we propose a denoising approach based on a three hidden layer fully connected deep learning network that aims to predict a Wiener filtering gain with an asymmetric input context, enabling real-time applications with high constraints on signal delay. The approach is employing a hearing instrument-grade filter bank and complies with typical hearing aid demands, such as low latency and on-line processing. It can further be well integrated with other algorithms in an existing HA signal processing chain. We can show on a database of real world noise signals that our algorithm is able to outperform a state of the art baseline approach, both using objective metrics and subject tests.Comment: submitted to IWAENC 201