Estimating Single-Channel Source Separation Masks: Relevance Vector Machine Classifiers vs. Pitch-Based Masking

Audio sources frequently concentrate much of their energy into a relatively small proportion of the available time-frequency cells in a short-time Fourier transform (STFT). This sparsity makes it possible to separate sources, to some degree, simply by selecting STFT cells dominated by the desired source, setting all others to zero (or to an estimate of the obscured target value), and inverting the STFT to a waveform. The problem of source separation then becomes identifying the cells containing good target information. We treat this as a classification problem, and train a Relevance Vector Machine (a probabilistic relative of the Support Vector Machine) to perform this task. We compare the performance of this classifier both against SVMs (it has similar accuracy but is not as efficient as RVMs), and against a traditional Computational Auditory Scene Analysis (CASA) technique based on a noise-robust pitch tracker, which the RVM outperforms significantly. Differences between the RVM- and pitch-tracker-based mask estimation suggest benefits to be obtained by combining both

Learning, Using, and Adapting Models in Scene Analysis

Discusses models of source behavior as the way to conquer uncertainty in mixtures

Monaural speech separation using source-adapted models

We propose a model-based source separation system for use on single channel speech mixtures where the precise source characteristics are not known a priori. We do this by representing the space of source variation with a parametric signal model based on the eigenvoice technique for rapid speaker adaptation. We present an algorithm to infer the characteristics of the sources present in a mixture, allowing for significantly improved separation performance over that obtained using unadapted source models. The algorithm is evaluated on the task defined in the 2006 Speech Separation Challenge [1] and compared with separation using source-dependent models

A variational EM algorithm for learning eigenvoice parameters in mixed signals

We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a priori. The sources are modeled using factor-analyzed hidden Markov models (HMM) where source specific characteristics are captured by an "eigenvoice" speaker subspace model. The proposed algorithm is able to learn adaptation parameters for two speech sources when only a mixture of signals is observed. We evaluate the algorithm on the 2006 speech separation challenge data set and show that it is significantly faster than our earlier system at a small cost in terms of performance

‘It’s not in my job description’: An exploration of trainee clinical psychologists’ attitudes towards research and perceptions of DClinPsy research culture

© 2023 The British Psychological Society. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.53841/bpscpf.2023.1.366.20This project aimed to investigate attitudes towards research and perceived research culture among trainee clinicalpsychologists across the UK. This was achieved by exploring factors such as: research training environment,research attitudes, research self-efficacy, and professional identity. An online survey was completed by 44 traineeclinical psychologists who started training in 2020. The findings showed that UK trainee clinical psychologistsdid not perceive a strong research training environment, they did not hold strong attitudes towards research,or have positive research self-efficacy as indicated in previous research. It is of some concern that the role ofresearcher, as part of the identity of a clinical psychologist, was not seen to be instrumental by most trainees.Important differences in the results of this research compared to previous published literature are discussed, inaddition to a consideration of the implications of these findings for training and the post-qualification role ofclinical psychologists.Peer reviewe

Combining Localization Cues and Source Model Constraints for Binaural Source Separation

We describe a system for separating multiple sources from a two-channel recording based on interaural cues and prior knowledge of the statistics of the underlying source signals. The proposed algorithm effectively combines information derived from low level perceptual cues, similar to those used by the human auditory system, with higher level information related to speaker identity. We combine a probabilistic model of the observed interaural level and phase differences with a prior model of the source statistics and derive an EM algorithm for finding the maximum likelihood parameters of the joint model. The system is able to separate more sound sources than there are observed channels in the presence of reverberation. In simulated mixtures of speech from two and three speakers the proposed algorithm gives a signal-to-noise ratio improvement of 1.7 dB over a baseline algorithm which uses only interaural cues. Further improvement is obtained by incorporating eigenvoice speaker adaptation to enable the source model to better match the sources present in the signal. This improves performance over the baseline by 2.7 dB when the speakers used for training and testing are matched. However, the improvement is minimal when the test data is very different from that used in training

Сравнительная оценка экспрессии молекул главного комплекса гистосовместимости в тканях пародонта и периферической крови больных генерализованным пародонтитом

Проведено порівняльні клініко-іммунологічні аналізи стану адгезивних молекул HLA-A, B, C і HLA-DR головного комплексу гістосумісності на місцевому рівні - в тканинах пародонта і периферичної крові хворих на ГП і відповідних антигенів моноклональних антитіл T і В-лімфоцитів. Не виявлено прямого кореляційного зв’язку між клінічним проявом запалення пародонту і загальносоматичним імунним статусом, що розкриває механізми локальних Т-клітинних характеристик імунних змін і зумовлює корекцію місцевої терапії.A comparative clinical and immunological analysis of adhesion molecules HLA-A, B, C and HLA-DR major histocompatibility complex at the local level - in periodontal tissues and peripheral blood of patients with SE and related antigen antibody monoklialnyh T and B lymphocytes. There were no direct connection between korellyatsionnoy clinical manifestation of periodontal inflammation and the immune status of the somatic, that reveals the mechanisms of local T-cell characteristics of the immune changes and determines the correction of local therapy