Toward an interpretive framework of two-dimensional speech-signal processing

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 177-179).Traditional representations of speech are derived from short-time segments of the signal and result in time-frequency distributions of energy such as the short-time Fourier transform and spectrogram. Speech-signal models of such representations have had utility in a variety of applications such as speech analysis, recognition, and synthesis. Nonetheless, they do not capture spectral, temporal, and joint spectrotemporal energy fluctuations (or "modulations") present in local time-frequency regions of the time-frequency distribution. Inspired by principles from image processing and evidence from auditory neurophysiological models, a variety of twodimensional (2-D) processing techniques have been explored in the literature as alternative representations of speech; however, speech-based models are lacking in this framework. This thesis develops speech-signal models for a particular 2-D processing approach in which 2-D Fourier transforms are computed on local time-frequency regions of the canonical narrowband or wideband spectrogram; we refer to the resulting transformed space as the Grating Compression Transform (GCT). We argue for a 2-D sinusoidal-series amplitude modulation model of speech content in the spectrogram domain that relates to speech production characteristics such as pitch/noise of the source, pitch dynamics, formant structure and dynamics, and offset/onset content. Narrowband- and wideband-based models are shown to exhibit important distinctions in interpretation and oftentimes "dual" behavior. In the transformed GCT space, the modeling results in a novel taxonomy of signal behavior based on the distribution of formant and onset/offset content in the transformed space via source characteristics. Our formulation provides a speech-specific interpretation of the concept of "modulation" in 2-D processing in contrast to existing approaches that have done so either phenomenologically through qualitative analyses and/or implicitly through data-driven machine learning approaches. One implication of the proposed taxonomy is its potential for interpreting transformations of other time-frequency distributions such as the auditory spectrogram which is generally viewed as being "narrowband"/"wideband" in its low/high-frequency regions. The proposed signal model is evaluated in several ways. First, we perform analysis of synthetic speech signals to characterize its properties and limitations. Next, we develop an algorithm for analysis/synthesis of spectrograms using the model and demonstrate its ability to accurately represent real speech content. As an example application, we further apply the models in cochannel speaker separation, exploiting the GCT's ability to distribute speaker-specific content and often recover overlapping information through demodulation and interpolation in the 2-D GCT space. Specifically, in multi-pitch estimation, we demonstrate the GCT's ability to accurately estimate separate and crossing pitch tracks under certain conditions. Finally, we demonstrate the model's ability to separate mixtures of speech signals using both prior and estimated pitch information. Generalization to other speech-signal processing applications is proposed.by Tianyu Tom Wang.Ph.D

Auditory Streaming: Behavior, Physiology, and Modeling

Auditory streaming is a fundamental aspect of auditory perception. It refers to the ability to parse mixed acoustic events into meaningful streams where each stream is assumed to originate from a separate source. Despite wide interest and increasing scientific investigations over the last decade, the neural mechanisms underlying streaming still remain largely unknown. A simple example of this mystery concerns the streaming of simple tone sequences, and the general assumption that separation along the tonotopic axis is sufficient for stream segregation. However, this dissertation research casts doubt on the validity of this assumption. First, behavioral measures of auditory streaming in ferrets prove that they can be used as an animal model to study auditory streaming. Second, responses from neurons in the primary auditory cortex (A1) of ferrets show that spectral components that are well-separated in frequency produce comparably segregated responses along the tonotopic axis, no matter whether presented synchronously or consecutively, despite the substantial differences in their streaming percepts when measured psychoacoustically in humans. These results argue against the notion that tonotopic separation per se is a sufficient neural correlate of stream segregation. Thirdly, comparing responses during behavior to those during the passive condition, the temporal correlations of spiking activity between neurons belonging to the same stream display an increased correlation, while responses among neurons belonging to different streams become less correlated. Rapid task-related plasticity of neural receptive fields shows a pattern that is consistent with the changes in correlation. Taken together these results indicate that temporal coherence is a plausible neural correlate of auditory streaming. Finally, inspired by the above biological findings, we propose a computational model of auditory scene analysis, which uses temporal coherence as the primary criterion for predicting stream formation. The promising results of this dissertation research significantly advance our understanding of auditory streaming and perception

Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends

Among various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems

Spoofing Detection in Voice Biometrics: Cochlear Modelling and Perceptually Motivated Features

The automatic speaker verification (ASV) system is one of the most widely adopted biometric technology. However, ASV is vulnerable to spoofing attacks that can significantly affect its reliability. Among the different variants of spoofing attacks, replay attacks pose a major threat as they do not require any expert knowledge to implement and are difficult to detect. The primary focus of this thesis is on understanding and developing biologically inspired models and techniques to detect replay attacks. This thesis develops a novel framework for implementing an active cochlear filter model as a frontend spectral analyser for spoofing attack detection to leverage the remarkable sensitivity and selectivity of the mammalian auditory system over a broad range of intensities and frequencies. In particular, the developed model aims to mimic the active mechanism in the cochlea, enabling sharp frequency tuning and level-dependent compression, which amplifies and tune to low energy signal to make a broad dynamic range of signals audible. Experimental evaluations of the developed models in the context of replay detection systems exhibit a significant performance improvement, highlighting the potential benefits of the use of biologically inspired front ends. In addition, since replay detection relies on the discerning channel characteristics and the effect of the acoustic environment, acoustic cues essential for speech perception such as amplitude- and frequency-modulation (AM, FM) features are also investigated. Finally, to capture discriminative cues present in the temporal domain, the temporal masking psychoacoustic phenomenon in auditory processing is exploited, and the usefulness of the masking pattern is investigated. This led to a novel feature parameterisation which helps improve replay attack detection

Automatic voice disorder recognition using acoustic amplitude modulation features

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 114-117).An automatic dysphonia recognition system is designed that exploits amplitude modulations (AM) in voice using biologically-inspired models. This system recognizes general dysphonia and four subclasses: hyperfunction, A-P squeezing, paralysis, and vocal fold lesions. The models developed represent processing in the auditory system at the level of the cochlea, auditory nerve, and inferior colliculus. Recognition experiments using dysphonic sentence data obtained from the Kay Elemetrics Disordered Voice Database suggest that our system provides complementary information to state-of-the-art mel-cepstral features. A model for analyzing AM in dysphonic speech is also developed from a traditional communications engineering perspective. Through a case study of seven disordered voices, we show that different AM patterns occur in different frequency bands. This perspective challenges current dysphonia analysis methods that analyze AM in the time-domain signal.by Nicolas Malyska.S.M

Trennung und Schätzung der Anzahl von Audiosignalquellen mit Zeit- und Frequenzüberlappung

Everyday audio recordings involve mixture signals: music contains a mixture of instruments; in a meeting or conference, there is a mixture of human voices. For these mixtures, automatically separating or estimating the number of sources is a challenging task. A common assumption when processing mixtures in the time-frequency domain is that sources are not fully overlapped. However, in this work we consider some cases where the overlap is severe — for instance, when instruments play the same note (unison) or when many people speak concurrently ("cocktail party") — highlighting the need for new representations and more powerful models. To address the problems of source separation and count estimation, we use conventional signal processing techniques as well as deep neural networks (DNN). We first address the source separation problem for unison instrument mixtures, studying the distinct spectro-temporal modulations caused by vibrato. To exploit these modulations, we developed a method based on time warping, informed by an estimate of the fundamental frequency. For cases where such estimates are not available, we present an unsupervised model, inspired by the way humans group time-varying sources (common fate). This contribution comes with a novel representation that improves separation for overlapped and modulated sources on unison mixtures but also improves vocal and accompaniment separation when used as an input for a DNN model. Then, we focus on estimating the number of sources in a mixture, which is important for real-world scenarios. Our work on count estimation was motivated by a study on how humans can address this task, which lead us to conduct listening experiments, confirming that humans are only able to estimate the number of up to four sources correctly. To answer the question of whether machines can perform similarly, we present a DNN architecture, trained to estimate the number of concurrent speakers. Our results show improvements compared to other methods, and the model even outperformed humans on the same task. In both the source separation and source count estimation tasks, the key contribution of this thesis is the concept of “modulation”, which is important to computationally mimic human performance. Our proposed Common Fate Transform is an adequate representation to disentangle overlapping signals for separation, and an inspection of our DNN count estimation model revealed that it proceeds to find modulation-like intermediate features.Im Alltag sind wir von gemischten Signalen umgeben: Musik besteht aus einer Mischung von Instrumenten; in einem Meeting oder auf einer Konferenz sind wir einer Mischung menschlicher Stimmen ausgesetzt. Für diese Mischungen ist die automatische Quellentrennung oder die Bestimmung der Anzahl an Quellen eine anspruchsvolle Aufgabe. Eine häufige Annahme bei der Verarbeitung von gemischten Signalen im Zeit-Frequenzbereich ist, dass die Quellen sich nicht vollständig überlappen. In dieser Arbeit betrachten wir jedoch einige Fälle, in denen die Überlappung immens ist zum Beispiel, wenn Instrumente den gleichen Ton spielen (unisono) oder wenn viele Menschen gleichzeitig sprechen (Cocktailparty) —, so dass neue Signal-Repräsentationen und leistungsfähigere Modelle notwendig sind. Um die zwei genannten Probleme zu bewältigen, verwenden wir sowohl konventionelle Signalverbeitungsmethoden als auch tiefgehende neuronale Netze (DNN). Wir gehen zunächst auf das Problem der Quellentrennung für Unisono-Instrumentenmischungen ein und untersuchen die speziellen, durch Vibrato ausgelösten, zeitlich-spektralen Modulationen. Um diese Modulationen auszunutzen entwickelten wir eine Methode, die auf Zeitverzerrung basiert und eine Schätzung der Grundfrequenz als zusätzliche Information nutzt. Für Fälle, in denen diese Schätzungen nicht verfügbar sind, stellen wir ein unüberwachtes Modell vor, das inspiriert ist von der Art und Weise, wie Menschen zeitveränderliche Quellen gruppieren (Common Fate). Dieser Beitrag enthält eine neuartige Repräsentation, die die Separierbarkeit für überlappte und modulierte Quellen in Unisono-Mischungen erhöht, aber auch die Trennung in Gesang und Begleitung verbessert, wenn sie in einem DNN-Modell verwendet wird. Im Weiteren beschäftigen wir uns mit der Schätzung der Anzahl von Quellen in einer Mischung, was für reale Szenarien wichtig ist. Unsere Arbeit an der Schätzung der Anzahl war motiviert durch eine Studie, die zeigt, wie wir Menschen diese Aufgabe angehen. Dies hat uns dazu veranlasst, eigene Hörexperimente durchzuführen, die bestätigten, dass Menschen nur in der Lage sind, die Anzahl von bis zu vier Quellen korrekt abzuschätzen. Um nun die Frage zu beantworten, ob Maschinen dies ähnlich gut können, stellen wir eine DNN-Architektur vor, die erlernt hat, die Anzahl der gleichzeitig sprechenden Sprecher zu ermitteln. Die Ergebnisse zeigen Verbesserungen im Vergleich zu anderen Methoden, aber vor allem auch im Vergleich zu menschlichen Hörern. Sowohl bei der Quellentrennung als auch bei der Schätzung der Anzahl an Quellen ist ein Kernbeitrag dieser Arbeit das Konzept der “Modulation”, welches wichtig ist, um die Strategien von Menschen mittels Computern nachzuahmen. Unsere vorgeschlagene Common Fate Transformation ist eine adäquate Darstellung, um die Überlappung von Signalen für die Trennung zugänglich zu machen und eine Inspektion unseres DNN-Zählmodells ergab schließlich, dass sich auch hier modulationsähnliche Merkmale finden lassen

Impact detection techniques using fibre-optic sensors for aerospace & defence

Impact detection techniques are developed for application in the aerospace and defence industries. Optical fibre sensors hold great promise for structural health monitoring systems and methods of interrogating fibre Bragg gratings (FBG) are investigated given the need for dynamic strain capture and multiplexed sensors. An arrayed waveguide grating based interrogator is developed. The relationships between key performance indicators, such as strain range and linearity of response, and parameters such as the FBG length and spectral width are determined. It was found that the inclusion of a semiconductor optical amplifier could increase the signal-to-noise ratio by ~300% as the system moves to its least sensitive. An alternative interrogator is investigated utilising two wave mixing in erbium-doped fibre in order to create an adaptive system insensitive to quasistatic strain and temperature drifts. Dynamic strain sensing was demonstrated at 200 Hz which remained functional while undergoing a temperature shift of 8.5 °C. In addition, software techniques are investigated for locating impact events on a curved composite structure using both time-of-flight triangulation and neural networks. A feature characteristic of composite damage creation is identified in dynamic signals captured during impact. An algorithm is developed which successfully distinguishes between signals characteristic of a non-damaging impact with those from a damaging impact with a classification accuracy of 93 – 96%. Finally, a demonstrator system is produced to exhibit some of the techniques developed in this thesis

Statistical models for natural sounds

It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds