Utilising temporal signal features in adverse noise conditions: Detection, estimation, and the reassigned spectrogram

Visual displays in passive sonar based on the Fourier spectrogram are underpinned by detection models that rely on signal and noise power statistics. Time-frequency representations specialised for sparse signals achieve a sharper signal representation, either by reassigning signal energy based on temporal structure or by conveying temporal structure directly. However, temporal representations involve nonlinear transformations that make it difficult to reason about how they respond to additive noise. This article analyses the effect of noise on temporal fine structure measurements such as zero crossings and instantaneous frequency. Detectors that rely on zero crossing intervals, intervals and peak amplitudes, and instantaneous frequency measurements are developed, and evaluated for the detection of a sinusoid in Gaussian noise, using the power detector as a baseline. Detectors that rely on fine structure outperform the power detector under certain circumstances; and detectors that rely on both fine structure and power measurements are superior. Reassigned spectrograms assume that the statistics used to reassign energy are reliable, but the derivation of the fine structure detectors indicates the opposite. The article closes by proposing and demonstrating the concept of a doubly reassigned spectrogram, wherein temporal measurements are reassigned according to a statistical model of the noise background

CPX based synthesis for binaural auralization of vehicle rolling noise to an arbitrary positioned stander-by receiver

Virtual reality is becoming an important tool for studying the interaction between pedestrians and road vehicles, by allowing the analysis of potentially hazard situations without placing subjects in real risk. However, most of the current simulators are unable to accurately recreate traffic sounds that are congruent with the visual scene. This has been recognized as a fault in the virtual audio-visual scenarios used in such contexts. This study proposes a method for delivering a binaural auralization of the noise generated by a moving vehicle to an arbitrarily located moving listener (pedestrian). Building on previously developed methods, the proposal presented here integrates in a novel way a dynamic auralization engine, thus enabling real-time update of the acoustic cues in the binaural signal delivered via headphones. Furthermore, the proposed auralization routine uses Close ProXimity (CPX) tyre-road noise signal as sound source input, facilitating the quick interchangeability of source signals, and easing the noise collection procedure. Two validation experiments were carried out, one to quantitatively compare field signals with CPX-derived virtual signal recordings, and another to assess these same signals through psychoacoustic models. The latter aims to assure that the reproduction of the synthesized signal is perceptually similar to one occurring on pedestrian/vehicle interactions during situations of street crossing. Discrepancies were detected, and emphasized when the vehicle is within close distance from the receiver (pedestrian). However, the analysis indicated that these pose no hindrance to the study of vehicle–pedestrian interaction. Improvements to the method are identified and further developments are proposed.This work was supported by the ‘‘Fundação para a Ciência e a Tecnologia” [PTDC/ECM-TRA/3568/2014, SFRH/BD/131638/2017, UIDB/04029/2020] This work is part of the activities of the research project AnPeB – ‘‘ANalysis of PEdestrians Behaviour based on simulated urban environments and its incorporation in risk modelling” (PTDC/ECM TRA/3568/2014), funded by the ‘‘Promover a Produção Científica e Desenvolvimento Tecnológico e a Constituição de Redes Temáti cas” (3599-PPCDT) project and supported by the ‘‘European Com munity Fund FEDER” and the doctoral scholarship SFRH/ BD/131638/2017, funded by ‘‘Fundação para a Ciência e a Tecnolo gia (FCT)”

On representing signals using only timing information

It is well known that only a special class of bandpass signals, called real-zero (RZ) signals can be uniquely represented (up to a scale factor) by their zero crossings, i.e., the time instants at which the signals change their sign. However, it is possible to invertibly map arbitrary bandpass signals into RZ signals, thereby, implicitly represent the bandpass signal using the mapped RZ signal’s zero crossings. This mapping is known as real-zero conversion (RZC). In this paper a class of novel signal-adaptive RZC algorithms is proposed. Specifically, algorithms that are analogs of well-known adaptive filtering methods to convert an arbitrary bandpass signal into other signals, whose zero crossings contain sufficient information to represent the bandpass signal’s phase and envelope are presented. Since the proposed zero crossings are not those of the original signal, but only indirectly related to it, they are called hidden or covert zero crossings (CoZeCs). The CoZeCs-based representations are developed first for analytic signals, and then extended to real-valued signals. Finally, the proposed algorithms are used to represent synthetic signals and speech signals processed through an analysis filter bank, and it is shown that they can be reconstructed given the CoZeCs. This signal representation has potential in many speech applications

The application of auditory signal processing principles to the detection, tracking and association of tonal components in sonar.

A steady signal exerts two complementary effects on a noisy acoustic environment: one is to add energy, the other is to create order. The ear has evolved mechanisms to detect both effects and encodes the fine temporal detail of a stimulus in sequences of auditory nerve discharges. Taking inspiration from these ideas, this thesis investigates the use of regular timing for sonar signal detection. Algorithms that operate on the temporal structure of a received signal are developed for the detection of merchant vessels. These ideas are explored by reappraising three areas traditionally associated with power-based detection. First of all, a time-frequency display based on timing instead of power is developed. Rather than inquiring of the display, "How much energy has been measured at this frequency? ", one would ask, "How structured is the signal at this frequency? Is this consistent with a target? " The auditory-motivated zero crossings with peak amplitudes (ZCPA) algorithm forms the starting-point for this study. Next, matters related to quantitative system performance analysis are addressed, such as how often a system will fail to detect a signal in particular conditions, or how much energy is required to guarantee a certain probability of detection. A suite of optimal temporal receivers is designed and is subsequently evaluated using the same kinds of synthetic signal used to assess power-based systems: Gaussian processes and sinusoids. The final area of work considers how discrete components on a sonar signal display, such as tonals and transients, can be identified and organised according to auditory scene analysis principles. Two algorithms are presented and evaluated using synthetic signals: one is designed to track a tonal through transient events, and the other attempts to identify groups of comodulated tonals against a noise background. A demonstration of each algorithm is provided for recorded sonar signals

An application of an auditory periphery model in speaker identification

The number of applications of automatic Speaker Identification (SID) is growing due to the advanced technologies for secure access and authentication in services and devices. In 2016, in a study, the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlear model achieved the best performance among seven recent cochlear models to fit a set of human auditory physiological data. Motivated by the performance of the CAR-FAC, I apply this cochlear model in an SID task for the first time to produce a similar performance to a human auditory system. This thesis investigates the potential of the CAR-FAC model in an SID task. I investigate the capability of the CAR-FAC in text-dependent and text-independent SID tasks. This thesis also investigates contributions of different parameters, nonlinearities, and stages of the CAR-FAC that enhance SID accuracy. The performance of the CAR-FAC is compared with another recent cochlear model called the Auditory Nerve (AN) model. In addition, three FFT-based auditory features – Mel frequency Cepstral Coefficient (MFCC), Frequency Domain Linear Prediction (FDLP), and Gammatone Frequency Cepstral Coefficient (GFCC), are also included to compare their performance with cochlear features. This comparison allows me to investigate a better front-end for a noise-robust SID system. Three different statistical classifiers: a Gaussian Mixture Model with Universal Background Model (GMM-UBM), a Support Vector Machine (SVM), and an I-vector were used to evaluate the performance. These statistical classifiers allow me to investigate nonlinearities in the cochlear front-ends. The performance is evaluated under clean and noisy conditions for a wide range of noise levels. Techniques to improve the performance of a cochlear algorithm are also investigated in this thesis. It was found that the application of a cube root and DCT on cochlear output enhances the SID accuracy substantially