Frequency-warped autoregressive modeling and filtering

This thesis consists of an introduction and nine articles. The articles are related to the application of frequency-warping techniques to audio signal processing, and in particular, predictive coding of wideband audio signals. The introduction reviews the literature and summarizes the results of the articles. Frequency-warping, or simply warping techniques are based on a modification of a conventional signal processing system so that the inherent frequency representation in the system is changed. It is demonstrated that this may be done for basically all traditional signal processing algorithms. In audio applications it is beneficial to modify the system so that the new frequency representation is close to that of human hearing. One of the articles is a tutorial paper on the use of warping techniques in audio applications. Majority of the articles studies warped linear prediction, WLP, and its use in wideband audio coding. It is proposed that warped linear prediction would be particularly attractive method for low-delay wideband audio coding. Warping techniques are also applied to various modifications of classical linear predictive coding techniques. This was made possible partly by the introduction of a class of new implementation techniques for recursive filters in one of the articles. The proposed implementation algorithm for recursive filters having delay-free loops is a generic technique. This inspired to write an article which introduces a generalized warped linear predictive coding scheme. One example of the generalized approach is a linear predictive algorithm using almost logarithmic frequency representation.reviewe

Visualization and categorization of ecological acoustic events based on discriminant features

Although sound classification in soundscape studies are generally performed by experts, the large growth of acoustic data presents a major challenge for performing such task. At the same time, the identification of more discriminating features becomes crucial when analyzing soundscapes, and this occurs because natural and anthropogenic sounds are very complex, particularly in Neotropical regions, where the biodiversity level is very high. In this scenario, the need for research addressing the discriminatory capability of acoustic features is of utmost importance to work towards automating these processes. In this study we present a method to identify the most discriminant features for categorizing sound events in soundscapes. Such identification is key to classification of sound events. Our experimental findings validate our method, showing high discriminatory capability of certain extracted features from sound data, reaching an accuracy of 89.91% for classification of frogs, birds and insects simultaneously. An extension of these experiments to simulate binary classification reached accuracy of 82.64%,100.0% and 99.40% for the classification between combinations of frogs-birds, frogs-insects and birds-insects, respectively

Spatial sound generation and perception by amplitude panning techniques

Spatial audio aims to recreate or synthesize spatial attributes when reproducing audio over loudspeakers or headphones. Such spatial attributes include, for example, locations of perceived sound sources and an auditory sense of space. This thesis focuses on new methods of spatial audio for loudspeaker listening and on measuring the quality of spatial audio by subjective and objective tests. In this thesis the vector base amplitude panning (VBAP) method, which is an amplitude panning method to position virtual sources in arbitrary 2-D or 3-D loudspeaker setups, is introduced. In amplitude panning the same sound signal is applied to a number of loudspeakers with appropriate non-zero amplitudes. With 2-D setups VBAP is a reformulation of the existing pair-wise panning method. However, differing from earlier solutions it can be generalized for 3-D loudspeaker setups as a triplet-wise panning method. A sound signal is then applied to one, two, or three loudspeakers simultaneously. VBAP has certain advantages compared to earlier virtual source positioning methods in arbitrary layouts. Previous methods either used all loudspeakers to produce virtual sources, which results in some artefacts, or they used loudspeaker triplets with a non-generalizable 2-D user interface. The virtual sources generated with VBAP are investigated. The human directional hearing is simulated with a binaural auditory model adapted from the literature. The interaural time difference (ITD) cue and the interaural level difference (ILD) cue which are the main localization cues are simulated for amplitude-panned virtual sources and for real sources. Psychoacoustic listening tests are conducted to study the subjective quality of virtual sources. Statistically significant phenomena found in listening test data are explained by auditory model simulation results. To obtain a generic view of directional quality in arbitrary loudspeaker setups, directional cues are simulated for virtual sources with loudspeaker pairs and triplets in various setups. The directional qualities of virtual sources generated with VBAP can be stated as follows. Directional coordinates used for this purpose are the angle between a position vector and the median plane (θcc), and the angle between a projection of a position vector to the median plane and frontal direction (Φcc). The perceived θcc direction of a virtual source coincides well with the VBAP panning direction when a loudspeaker set is near the median plane. When the loudspeaker set is moved towards a side of a listener, the perceived θcc direction is biased towards the median plane. The perceived Φcc direction of an amplitude-panned virtual source is individual and cannot be predicted with any panning law.reviewe

Open-set Speaker Identification

This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition. The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material

Time-Resolved Method for Spectral Analysis based on Linear Predictive Coding, with Application to EEG Analysis

EEG (Electroencephalogram) signal is a biological signal in BCI (Brain-Computer Interface) systems to realise the information exchange between the brain and the external environment. It is characterised by a poor signal-to-noise ratio, is time-varying, is intermittent and contains multiple frequency components. This research work has developed a new parameterised time-frequency method called the Linear Predictive Coding Pole Processing (LPCPP) method which can be used for identifying and tracking the dominant frequency components of an EEG signal. The LPCPP method further processes LPC (Linear Predictive Coding) poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequencies. It is suited for processing high-noise multi-component signals and can directly give the corresponding frequency estimates unlike transform-based methods. Furthermore, a new EEG spectral analysis framework involving the LPCPP method is proposed to describe the EEG spectral activity. The EEG signal has been divided into different frequency bands (i.e. Delta, Theta, Alpha, Beta and Gamma). However, there is no consensus on the definitions of these band boundaries. A series of EEG centre frequencies are proposed in this thesis instead of fixed frequency boundaries, as they are better suited to describe the dominant EEG spectral activity

Object-based modelling for representing and processing speech corpora

This thesis deals with modelling data existing in large speech corpora using an object-oriented paradigm which captures important linguistic structures. Information from corpora is transformed into objects and are assigned properties regarding their behaviour. These objects, called speech units, are placed onto a multi-dimensional framework and have their relationships to other units explicitly defined through the use of links. Frameworks that model temporal utterances or atemporal information like speaker characteristics and recording conditions can be searched efficiently for contextual matches. Speech units that match desired contexts are the result of successful linguistically motivated queries and can be used in further speech processing tasks in the same computational environment. This allows for empirical studies of speech and its relation to linguistic structures to be carried out, and for the training and testing of applications like speech recognition and synthesis. Information residing in typical speech corpora is discussed first, followed by an overview of object-orientation which sets the tone for this thesis. Then the representation framework is introduced which is generated by a compiler and linker that rely on a set of domain-specific resources that transform corpus data into speech units. Operations on this framework are then presented along with a comparison between a relational and object-oriented model of identical speech data. The models described in this work are directly applicable to existing large speech corpora, and the methods developed here are tested against relational database methods. The object-oriented methods outperform the relational methods for typical linguistically relevant queries by about three orders of magnitude as measured by database search times. This improvement in simplicity of representation and search speed is crucial for the utilisation of large multi-lingual corpora in basic research on the detailed properties of speech, especially in relation to contextual variation.reviewe

Generalized linear-in-parameter models : theory and audio signal processing applications

This thesis presents a mathematically oriented perspective to some basic concepts of digital signal processing. A general framework for the development of alternative signal and system representations is attained by defining a generalized linear-in-parameter model (GLM) configuration. The GLM provides a direct view into the origins of many familiar methods in signal processing, implying a variety of generalizations, and it serves as a natural introduction to rational orthonormal model structures. In particular, the conventional division between finite impulse response (FIR) and infinite impulse response (IIR) filtering methods is reconsidered. The latter part of the thesis consists of audio oriented case studies, including loudspeaker equalization, musical instrument body modeling, and room response modeling. The proposed collection of IIR filter design techniques is submitted to challenging modeling tasks. The most important practical contribution of this thesis is the introduction of a procedure for the optimization of rational orthonormal filter structures, called the BU-method. More generally, the BU-method and its variants, including the (complex) warped extension, the (C)WBU-method, can be consider as entirely new IIR filter design strategies.reviewe

Linear predictive coding with modified filter structures

In conventional one-step forward linear prediction, an estimate for the current sample value is formed as a linear combination of previous sample values. In this paper, a generalized form of this scheme is studied. Here, the prediction is not based simply on the previous sample values but to the signal history as seen through an arbitrary filterbank. It is shown in the paper how the coefficients of a modified model can be obtained and how the inverse and synthesis filters can be implemented. Various properties of such systems are derived in this article. As an example, a novel linear predictive system using inherently logarithmic frequency representation is introduced.</p