AN EFFICIENT SPEECH GENERATIVE MODEL BASED ON DETERMINISTIC/STOCHASTIC SEPARATION OF SPECTRAL ENVELOPES

The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach.The paper presents a speech generative model that provides an efficient way of generating speech waveform from its amplitude spectral envelopes. The model is based on hybrid speech representation that includes deterministic (harmonic) and stochastic (noise) components. The main idea behind the approach originates from the fact that speech signal has a determined spectral structure that is statistically bound with deterministic/stochastic energy distribution in the spectrum. The performance of the model is evaluated using an experimental low-bitrate wide-band speech coder. The quality of reconstructed speech is evaluated using objective and subjective methods. Two objective quality characteristics were calculated: Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ). Narrow-band and wide-band versions of the proposed solution were compared with MELP (Mixed Excitation Linear Prediction) speech coder and AMR (Adaptive Multi-Rate) speech coder, respectively. The speech base of two female and two male speakers were used for testing. The performed tests show that overall performance of the proposed approach is speaker-dependent and it is better for male voices. Supposedly, this difference indicates the influence of pitch highness on separation accuracy. In that way, using the proposed approach in experimental speech compression system provides decent MBSD values and comparable PESQ values with AMR speech coder at 6,6 kbit/s. Additional subjective listening testsdemonstrate that the implemented coding system retains phonetic content and speaker’s identity. It proves consistency of the proposed approach

Three-Dimensional Printing and Its Applications in Otorhinolaryngology–Head and Neck Surgery

Objective Three-dimensional (3D)-printing technology is being employed in a variety of medical and surgical specialties to improve patient care and advance resident physician training. As the costs of implementing 3D printing have declined, the use of this technology has expanded, especially within surgical specialties. This article explores the types of 3D printing available, highlights the benefits and drawbacks of each methodology, provides examples of how 3D printing has been applied within the field of otolaryngology–head and neck surgery, discusses future innovations, and explores the financial impact of these advances. Data Sources Articles were identified from PubMed and Ovid MEDLINE. Review Methods PubMed and Ovid Medline were queried for English articles published between 2011 and 2016, including a few articles prior to this time as relevant examples. Search terms included 3-dimensional printing, 3D printing, otolaryngology, additive manufacturing, craniofacial, reconstruction, temporal bone, airway, sinus, cost, and anatomic models. Conclusions Three-dimensional printing has been used in recent years in otolaryngology for preoperative planning, education, prostheses, grafting, and reconstruction. Emerging technologies include the printing of tissue scaffolds for the auricle and nose, more realistic training models, and personalized implantable medical devices. Implications for Practice After the up-front costs of 3D printing are accounted for, its utilization in surgical models, patient-specific implants, and custom instruments can reduce operating room time and thus decrease costs. Educational and training models provide an opportunity to better visualize anomalies, practice surgical technique, predict problems that might arise, and improve quality by reducing mistakes

Linoleic acid participates in the response to ischemic brain injury through oxidized metabolites that regulate neurotransmission.

Linoleic acid (LA; 18:2 n-6), the most abundant polyunsaturated fatty acid in the US diet, is a precursor to oxidized metabolites that have unknown roles in the brain. Here, we show that oxidized LA-derived metabolites accumulate in several rat brain regions during CO2-induced ischemia and that LA-derived 13-hydroxyoctadecadienoic acid, but not LA, increase somatic paired-pulse facilitation in rat hippocampus by 80%, suggesting bioactivity. This study provides new evidence that LA participates in the response to ischemia-induced brain injury through oxidized metabolites that regulate neurotransmission. Targeting this pathway may be therapeutically relevant for ischemia-related conditions such as stroke

Automatic quality control of cardiac MRI segmentation in large-scale population imaging

The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to detect when an automatic method fails to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. To overcome this challenge, we explore an approach for predicting segmentation quality based on reverse classification accuracy, which enables us to discriminate between successful and failed cases. We validate this approach on a large cohort of cardiac MRI for which manual QC scores were available. Our results on 7,425 cases demonstrate the potential for fully automatic QC in the context of large-scale population imaging such as the UK Biobank Imaging Study