Self-Supervised Learning for Speech Enhancement through Synthesis

Modern speech enhancement (SE) networks typically implement noise suppression through time-frequency masking, latent representation masking, or discriminative signal prediction. In contrast, some recent works explore SE via generative speech synthesis, where the system's output is synthesized by a neural vocoder after an inherently lossy feature-denoising step. In this paper, we propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We leverage rich representations from self-supervised learning (SSL) speech models to discover relevant features. We conduct a candidate search across 15 potential SSL front-ends and subsequently train our vocoder adversarially with the best SSL configuration. Additionally, we demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation. Finally, we conduct both objective evaluations and subjective listening studies to show our system improves objective metrics and outperforms an existing state-of-the-art SE model subjectively

CCATMos: Convolutional Context-aware Transformer Network for Non-intrusive Speech Quality Assessment

Speech quality assessment has been a critical component in many voice communication related applications such as telephony and online conferencing. Traditional intrusive speech quality assessment requires the clean reference of the degraded utterance to provide an accurate quality measurement. This requirement limits the usability of these methods in real-world scenarios. On the other hand, non-intrusive subjective measurement is the ``golden standard" in evaluating speech quality as human listeners can intrinsically evaluate the quality of any degraded speech with ease. In this paper, we propose a novel end-to-end model structure called Convolutional Context-Aware Transformer (CCAT) network to predict the mean opinion score (MOS) of human raters. We evaluate our model on three MOS-annotated datasets spanning multiple languages and distortion types and submit our results to the ConferencingSpeech 2022 Challenge. Our experiments show that CCAT provides promising MOS predictions compared to current state-of-art non-intrusive speech assessment models with average Pearson correlation coefficient (PCC) increasing from 0.530 to 0.697 and average RMSE decreasing from 0.768 to 0.570 compared to the baseline model on the challenge evaluation test set

Effect of alkaline solutions on the tensile properties of glass-polyester pipes

Construction materials, traditionally used in process equipment, are today successfully replaced by composite materials. Hence, many pipes are made of these materials. The subject of this study was the influence of liquids on the state of stresses and tensile strengths in the longitudinal and circumferential direction of glass-polyester pipes of a definite structure and known fabrication process. These analyses are of great importance for the use of glass-polyester pipes in the chemical industry. The tensile properties (the ultimate tensile strength and the modulus of elasticity) were tested and determined for specimens cut out of the pipes; flat specimens for the tensile properties in the longitudinal direction and ring specimens for the tensile properties in the circumferential direction. First, the tension test was performed on virgin samples (without the influence of any liquid), to obtain knowledge about the original tensile properties of the material composite studied. Subsequently, the specimens were soaked in alkaline solutions: sodium hydroxide (strong alkali) and ammonium hydroxide (weak alkali). These solutions were selected because of their considerable difference in pH values. The specimens and rings were left for 3, 10, 30 and 60 days in each liquid at room temperature. Then, the samples were tested on tension by the standard testing procedure. A comparison of the obtained results was made based on the pH values of the aggressive media in which the examined material had been soaked, as well as based on the original tensile properties and the number of days of treatment. Micromechanical analyses of sample breakage helped in the elucidation of the influence of the liquids on the structure of the composite pipe and enabled models and mechanisms that produced the change of strength to be proposed.Konstrukcioni materijali tradicionalno korišćeni u procesnoj opremi su danas uspešno zamenjeni kompozitnim materijalima, tako da su i mnoge cevi izrađene od ovih materijala. Uticaj baznih tečnosti na stanje napona i zatezne čvrstoće u uzdužnom i obimnom pravcu staklo-poliester kompozitnih cevi je tema ovog rada. Cevi su definisane strukture i poznatog procesa proizvodnje. Ove analize su od velikog značaja za korišćenje staklo-poliester cevi u hemijskoj industriji. Zatezne osobine (zatezna čvrstoća i modul elastičnosti) su eksperimentalno ispitivane na isečenim uzorcima; ravni uzorci u udužnom pravcu, a uzorci u obliku prstena u obimnom pravcu. Ispitivanje je prvo izvođeno na uzorcima koji nisu bili izloženi uticaju rastvora baza da bi se došlo do saznanja o prvobitnim zateznim svojstvima ispitivanih kompozitnih materijala. Nakon toga, uzorci su stavljeni u rastvore baza natrijum-hidroksida (jaka baza, pH=14) i amonijum hidroksida (slaba baza, pH=12) 3, 10, 30 i 60 dana na sobnoj temperaturi. Nakon toga su ispitivani na zatezanje prema standardnoj proceduri. Poređenje dobijenih rezultata je izvedeno na osnovu rN vrednosti rastvora i broja dana izlaganja rastvorima baza, a na osnovu originalnih zateznih osobina. Mikromehanička analiza se izvodila u odnosu na fotografije sa skening eloktronskog mikroskopa sa prelomnih površina čime se došlo do podataka o uticaju rastvora baza na strukturu kompozitnih cevi i modele i mehanizme koji su dovodili do promene zateznih svojstava