Predicting Speech Intelligibility

Hearing impairment, and specifically sensorineural hearing loss, is an increasingly prevalent condition, especially amongst the ageing population. It occurs primarily as a result of damage to hair cells that act as sound receptors in the inner ear and causes a variety of hearing perception problems, most notably a reduction in speech intelligibility. Accurate diagnosis of hearing impairments is a time consuming process and is complicated by the reliance on indirect measurements based on patient feedback due to the inaccessible nature of the inner ear. The challenges of designing hearing aids to counteract sensorineural hearing losses are further compounded by the wide range of severities and symptoms experienced by hearing impaired listeners. Computer models of the auditory periphery have been developed, based on phenomenological measurements from auditory-nerve fibres using a range of test sounds and varied conditions. It has been demonstrated that auditory-nerve representations of vowels in normal and noisedamaged ears can be ranked by a subjective visual inspection of how the impaired representations differ from the normal. This thesis seeks to expand on this procedure to use full word tests rather than single vowels, and to replace manual inspection with an automated approach using a quantitative measure. It presents a measure that can predict speech intelligibility in a consistent and reproducible manner. This new approach has practical applications as it could allow speechprocessing algorithms for hearing aids to be objectively tested in early stage development without having to resort to extensive human trials. Simulated hearing tests were carried out by substituting real listeners with the auditory model. A range of signal processing techniques were used to measure the model’s auditory-nerve outputs by presenting them spectro-temporally as neurograms. A neurogram similarity index measure (NSIM) was developed that allowed the impaired outputs to be compared to a reference output from a normal hearing listener simulation. A simulated listener test was developed, using standard listener test material, and was validated for predicting normal hearing speech intelligibility in quiet and noisy conditions. Two types of neurograms were assessed: temporal fine structure (TFS) which retained spike timing information; and average discharge rate or temporal envelope (ENV). Tests were carried out to simulate a wide range of sensorineural hearing losses and the results were compared to real listeners’ unaided and aided performance. Simulations to predict speech intelligibility performance of NAL-RP and DSL 4.0 hearing aid fitting algorithms were undertaken. The NAL-RP hearing aid fitting algorithm was adapted using a chimaera sound algorithm which aimed to improve the TFS speech cues available to aided hearing impaired listeners. NSIM was shown to quantitatively rank neurograms with better performance than a relative mean squared error and other similar metrics. Simulated performance intensity functions predicted speech intelligibility for normal and hearing impaired listeners. The simulated listener tests demonstrated that NAL-RP and DSL 4.0 performed with similar speech intelligibility restoration levels. Using NSIM and a computational model of the auditory periphery, speech intelligibility can be predicted for both normal and hearing impaired listeners and novel hearing aids can be rapidly prototyped and evaluated prior to real listener tests

Methods of Optimizing Speech Enhancement for Hearing Applications

Speech intelligibility in hearing applications suffers from background noise. One of the most effective solutions is to develop speech enhancement algorithms based on the biological traits of the auditory system. In humans, the medial olivocochlear (MOC) reflex, which is an auditory neural feedback loop, increases signal-in-noise detection by suppressing cochlear response to noise. The time constant is one of the key attributes of the MOC reflex as it regulates the variation of suppression over time. Different time constants have been measured in nonhuman mammalian and human auditory systems. Physiological studies reported that the time constant of nonhuman mammalian MOC reflex varies with the properties (e.g. frequency, bandwidth) changes of the stimulation. A human based study suggests that time constant could vary when the bandwidth of the noise is changed. Previous works have developed MOC reflex models and successfully demonstrated the benefits of simulating the MOC reflex for speech-in-noise recognition. However, they often used fixed time constants. The effect of the different time constants on speech perception remains unclear. The main objectives of the present study are (1) to study the effect of the MOC reflex time constant on speech perception in different noise conditions; (2) to develop a speech enhancement algorithm with dynamic time constant optimization to adapt to varying noise conditions for improving speech intelligibility. The first part of this thesis studies the effect of the MOC reflex time constants on speech-in-noise perception. Conventional studies do not consider the relationship between the time constants and speech perception as it is difficult to measure the speech intelligibility changes due to varying time constants in human subjects. We use a model to investigate the relationship by incorporating Meddis’ peripheral auditory model (which includes a MOC reflex) with an automatic speech recognition (ASR) system. The effect of the MOC reflex time constant is studied by adjusting the time constant parameter of the model and testing the speech recognition accuracy of the ASR. Different time constants derived from human data are evaluated in both speech-like and non-speech like noise at the SNR levels from -10 dB to 20 dB and clean speech condition. The results show that the long time constants (≥1000 ms) provide a greater improvement of speech recognition accuracy at SNR levels≤10 dB. Maximum accuracy improvement of 40% (compared to no MOC condition) is shown in pink noise at the SNR of 10 dB. Short time constants (<1000 ms) show recognition accuracy over 5% higher than the longer ones at SNR levels ≥15 dB. The second part of the thesis develops a novel speech enhancement algorithm based on the MOC reflex with a time constant that is dynamically optimized, according to a lookup table for varying SNRs. The main contributions of this part include: (1) So far, the existing SNR estimation methods are challenged in cases of low SNR, nonstationary noise, and computational complexity. High computational complexity would increase processing delay that causes intelligibility degradation. A variance of spectral entropy (VSE) based SNR estimation method is developed as entropy based features have been shown to be more robust in the cases of low SNR and nonstationary noise. The SNR is estimated according to the estimated VSE-SNR relationship functions by measuring VSE of noisy speech. Our proposed method has an accuracy of 5 dB higher than other methods especially in the babble noise with fewer talkers (2 talkers) and low SNR levels (< 0 dB), with averaging processing time only about 30% of the noise power estimation based method. The proposed SNR estimation method is further improved by implementing a nonlinear filter-bank. The compression of the nonlinear filter-bank is shown to increase the stability of the relationship functions. As a result, the accuracy is improved by up to 2 dB in all types of tested noise. (2) A modification of Meddis’ MOC reflex model with a time constant dynamically optimized against varying SNRs is developed. The model incudes simulated inner hair cell response to reduce the model complexity, and now includes the SNR estimation method. Previous MOC reflex models often have fixed time constants that do not adapt to varying noise conditions, whilst our modified MOC reflex model has a time constant dynamically optimized according to the estimated SNRs. The results show a speech recognition accuracy of 8 % higher than the model using a fixed time constant of 2000 ms in different types of noise. (3) A speech enhancement algorithm is developed based on the modified MOC reflex model and implemented in an existing hearing aid system. The performance is evaluated by measuring the objective speech intelligibility metric of processed noisy speech. In different types of noise, the proposed algorithm increases intelligibility at least 20% in comparison to unprocessed noisy speech at SNRs between 0 dB and 20 dB, and over 15 % in comparison to processed noisy speech using the original MOC based algorithm in the hearing aid

Movements in Binaural Space: Issues in HRTF Interpolation and Reverberation, with applications to Computer Music

This thesis deals broadly with the topic of Binaural Audio. After reviewing the literature, a reappraisal of the minimum-phase plus linear delay model for HRTF representation and interpolation is offered. A rigorous analysis of threshold based phase unwrapping is also performed. The results and conclusions drawn from these analyses motivate the development of two novel methods for HRTF representation and interpolation. Empirical data is used directly in a Phase Truncation method. A Functional Model for phase is used in the second method based on the psychoacoustical nature of Interaural Time Differences. Both methods are validated; most significantly, both perform better than a minimum-phase method in subjective testing. The accurate, artefact-free dynamic source processing afforded by the above methods is harnessed in a binaural reverberation model, based on an early reflection image model and Feedback Delay Network diffuse field, with accurate interaural coherence. In turn, these flexible environmental processing algorithms are used in the development of a multi-channel binaural application, which allows the audition of multi-channel setups in headphones. Both source and listener are dynamic in this paradigm. A GUI is offered for intuitive use of the application. HRTF processing is thus re-evaluated and updated after a review of accepted practice. Novel solutions are presented and validated. Binaural reverberation is recognised as a crucial tool for convincing artificial spatialisation, and is developed on similar principles. Emphasis is placed on transparency of development practices, with the aim of wider dissemination and uptake of binaural technology

Algorithmic Compositional Methods and their Role in Genesis: A Multi-Functional Real-Time Computer Music System

Algorithmic procedures have been applied in computer music systems to generate compositional products using conventional musical formalism, extensions of such musical formalism and extra-musical disciplines such as mathematical models. This research investigates the applicability of such algorithmic methodologies for real-time musical composition, culminating in Genesis, a multi-functional real-time computer music system written for Mac OS X in the SuperCollider object-oriented programming language, and contained in the accompanying DVD. Through an extensive graphical user interface, Genesis offers musicians the opportunity to explore the application of the sonic features of real-time sound-objects to designated generative processes via different models of interaction such as unsupervised musical composition by Genesis and networked control of external Genesis instances. As a result of the applied interactive, generative and analytical methods, Genesis forms a unique compositional process, with a compositional product that reflects the character of its interactions between the sonic features of real-time sound-objects and its selected algorithmic procedures. Within this thesis, the technologies involved in algorithmic methodologies used for compositional processes, and the concepts that define their constructs are described, with consequent detailing of their selection and application in Genesis, with audio examples of algorithmic compositional methods demonstrated on the accompanying DVD. To demonstrate the real-time compositional abilities of Genesis, free explorations with instrumentalists, along with studio recordings of the compositional processes available in Genesis are presented in audiovisual examples contained in the accompanying DVD. The evaluation of the Genesis system’s capability to form a real-time compositional process, thereby maintaining real-time interaction between the sonic features of real-time sound objects and its selected algorithmic compositional methods, focuses on existing evaluation techniques founded in HCI and the qualitative issues such evaluation methods present. In terms of the compositional products generated by Genesis, the challenges in quantifying and qualifying its compositional outputs are identified, demonstrating the intricacies of assessing generative methods of compositional processes, and their impact on a resulting compositional product. The thesis concludes by considering further advances and applications of Genesis, and inviting further dissemination of the Genesis system and promotion of research into evaluative methods of generative techniques, with the hope that this may provide additional insight into the relative success of products generated by real-time algorithmic compositional processes

Investigating the build-up of precedence effect using reflection masking

The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

A novel technique for high-resolution frequency discriminators and their application to pitch and onset detection in empirical musicology

This thesis presents and evaluates software for simultaneous, high-resolution time-frequency discrimination. Whilst this is a problem that arises in many areas of engineering, the software here is developed to assist musicological investigations. In order to analyse musical performances, we must first know what is happening and when; that is, at what time each note begins to sound (the note onset) and what frequencies are present (the pitch). The work presented here focusses on onset detection, although the representation of data used for this task could also be used to track the pitch. A potential method of determining pitch on a sample-to-sample basis is given in the final chapter. Extant software for onset detection uses standard signal processing techniques to search for changes in features like the spectrum or phase. These methods struggle somewhat, as they are constrained by the uncertainty principle, which states that, as time resolution is increased, frequency resolution must decrease and vice versa. However, we can hear changes in frequency to a far greater time resolution than the uncertainty principle would suggest is possible. There is an active process in the inner ear which adds energy and enables this perceptual acuity. The mathematical expression which describes this system is known as the Hopf bifurcation. By building a bank of tuned resonators in software, each of which operates at a Hopf bifurcation, and driving it with audio, changes in frequency can be detected in times that defy the uncertainty relation, as we are not seeking to directly measure the time-frequency features of a system, rather it is used to drive a system. Time and frequency information is then available from the internal state variables of the system. The characteristics of this bank of resonators - called a 'DetectorBank' - are investigated thoroughly. The bandwidth of each resonator ('detector') can be as narrow as 0.922Hz and the system bandwidth is extended to the Nyquist frequency. A nonlinear system may be expected to respond poorly when presented with multiple simultaneous input frequencies; however, the DetectorBank performs well under these circumstances. The data generated by the DetectorBank is then analysed by an OnsetDetector. Both the development and testing of this OnsetDetector are detailed. It is tested using a repository of recordings of individual notes played on a variety of instruments, with promising results. These results are discussed, problems with the current implementation are identified and potential solutions presented. This OnsetDetector can then be combined with a PitchTracker to create a NoteDetector, capable of detecting not only a single note onset time and pitch, but information about changes that occur within a note. Musical notes are not static entities: they contain much variation. Both the performer's intonation and the characteristics of the instrument itself have an effect on the frequency present, as well as features like vibrato. Knowledge of these frequency components, and how they appear or disappear over the course of the note, is valuable information and the software presented here enables the collection of this data