Quantum entanglement and fixed-point bifurcations

How does the classical phase space structure for a composite system relate to the entanglement characteristics of the corresponding quantum system? We demonstrate how the entanglement in nonlinear bipartite systems can be associated with a fixed point bifurcation in the classical dynamics. Using the example of coupled giant spins we show that when a fixed point undergoes a supercritical pitchfork bifurcation, the corresponding quantum state - the ground state - achieves its maximum amount of entanglement near the critical point. We conjecture that this will be a generic feature of systems whose classical limit exhibits such a bifurcation.Comment: v2: Structure of the paper changed for clarity, reduced length, now 9 pages with 6 figure

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

Evaluating Sensorineural Hearing Loss With An Auditory Nerve Model Using A Mean Structural Similarity Measure

Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects. This work seeks to create an objective measure to automate this inspection process and ranks hearing losses based on auditory-nerve discharge patterns. A systematic way of assessing phonemic degradation using the outputs of an auditory nerve model for a range of sensorineural hearing losses would aid in rapid prototyping development of speech-processing algorithms for digital hearing aids. The effect of sensorineural hearing loss (SNHL) on phonemic structure was evaluated in this study using two types of neurograms: temporal fine structure (TFS) and average discharge rate or temporal envelope. The mean structural similarity index (MSSIM) is an objective measure originally developed to assess perceptual image quality. The measure is adapted here for use in measuring the phonemic degradation in neurograms derived from impaired auditory nerve outputs. A full evaluation of the choice of parameters for the metric is presented using a large amount of natural human speech. The metric’s boundedness and the results for TFS neurograms indicate it is a superior metric to standard point to point metrics of relative mean absolute error and relative mean squared error

Speech Intelligibility from Image Processing

Hearing loss research has traditionally been based on perceptual criteria, speech intelligibility and threshold levels. The development of computational models of the auditory-periphery has allowed experimentation via simulation to provide quantitative, repeatable results at a more granular level than would be practical with clinical research on human subjects

Measuring and Monitoring Speech Quality for Voice over IP with POLQA, ViSQOL and P.563

There are many types of degradation which can occur in Voice over IP (VoIP) calls. Of interest in this work are degradations which occur independently of the codec, hardware or network in use. Specifically, their effect on the subjective and objec- tive quality of the speech is examined. Since no dataset suit- able for this purpose exists, a new dataset (TCD-VoIP) has been created and has been made publicly available. The dataset con- tains speech clips suffering from a range of common call qual- ity degradations, as well as a set of subjective opinion scores on the clips from 24 listeners. The performances of three ob- jective quality metrics: POLQA, ViSQOL and P.563, have been evaluated using the dataset. The results show that full reference metrics are capable of accurately predicting a variety of com- mon VoIP degradations. They also highlight the outstanding need for a wideband, single-ended, no-reference metric to mon- itor accurately speech quality for degradations common in VoIP scenarios