Research on Evolution Characteristics of Shale Crack Based on Simultaneous Monitoring of Multi-Parameters

Meso-crack evolution mechanism of shale is a key factor affecting the mechanical properties of shale. In order to explore evolution laws of cracks in shale during loading, a meso-crack monitoring system, loading test equipment and an automatic ultrasonic data acquisition system were set up. On this basis, a set of experimental apparatus simultaneous monitoring multi-parameters of shale micro-crack was designed, and destruction experiments of shale samples with different bedding angles were carried out to find out evolution characteristics of cracks. The results show the following: (1) The designed apparatus can monitor ultrasonic, mechanical and video information simultaneously of crack evolution in the entire process of shale destruction under load to provide information for analyzing acoustic and mechanical characteristic responses of crack propagation at key time nodes. (2) With an increase in load, shale will undergo four stages of destruction: crack initiation, propagation, penetration and overall failure. In the course of these stages, acoustic characteristics and mechanical characteristics are in good agreement, which proves the validity of predicting rock mechanical parameters with acoustic data. (3) During the loading process of shale, the main amplitude of acoustic wave is more sensitive than mechanical parameters to the change of rock cracks. Research results have important theoretical reference value for evaluating wall stability of shale gas horizontal well with ultrasonic data.

MLLP-VRAIN Spanish ASR Systems for the Albayzín-RTVE 2020 Speech-To-Text Challenge: Extension

This paper describes the automatic speech recognition (ASR) systems built by the MLLP-VRAIN research group of Universitat Politècnica de València for the Albayzín-RTVE 2020 Speech-to-Text Challenge, and includes an extension of the work consisting of building and evaluating equivalent systems under the closed data conditions from the 2018 challenge. The primary system (p-streaming_1500ms_nlt) was a hybrid ASR system using streaming one-pass decoding with a context window of 1.5 seconds. This system achieved 16.0% WER on the test-2020 set. We also submitted three contrastive systems. From these, we highlight the system c2-streaming_600ms_t which, following a similar configuration as the primary system with a smaller context window of 0.6 s, scored 16.9% WER points on the same test set, with a measured empirical latency of 0.81 ± 0.09 s (mean ± stdev). That is, we obtained state-of-the-art latencies for high-quality automatic live captioning with a small WER degradation of 6% relative. As an extension, the equivalent closed-condition systems obtained 23.3% WER and 23.5% WER, respectively. When evaluated with an unconstrained language model, we obtained 19.9% WER and 20.4% WER; i.e., not far behind the top-performing systems with only 5% of the full acoustic data and with the extra ability of being streaming-capable. Indeed, all of these streaming systems could be put into production environments for automatic captioning of live media streams.

Spatial–Temporal Distribution of the Euphausiid Euphausia pacifica and Fish Schools in the Coastal Southwestern East Sea

The spatial and temporal distribution of euphausiid Euphausia pacifica and fish schools were observed along acoustic transects in the coastal southwestern East Sea. Two-frequency (38- and 120-kHz) acoustic backscatter data were examined from April to July 2010. A dB identification window (SV120–38) and school detection algorithm identified E. pacifica and fish schools in the acoustic backscatter, respectively. The E. pacifica was regularly observed in middle of southern waters, where phytoplankton was abundant during spring, and irregularly during summer, when phytoplankton was homogeneously distributed. Using the distorted-wave Born approximation model, the acoustic density of E. pacifica calculated was higher in spring (April: 75.9 mg m−2, May: 85.3 mg m−2) than in summer (June: 71.4 mg m−2, July: 54.1 mg m−2). The fish schools in the acoustic data tended to significantly increase from spring to summer. Although major fish species, such as anchovies and herring, fed on copepods and euphausiids in the survey area, the temporal and spatial distribution of E. pacifica was weakly correlated with the distribution of the fish schools. These findings aid in our understanding of the temporal and spatial distribution dynamics of euphausiids and fish schools in the food web of the coastal southwestern East Sea.

A Nonlinear Data-Driven Towed Array Shape Estimation Method Using Passive Underwater Acoustic Data

Large-aperture towed linear hydrophone array has been widely used for beamforming-based signal enhancement in passive sonar systems; however, its performance can drastically decrease due to the array distortion caused by rapid tactical maneuvers of the towed platform, oceanic currents, hydrodynamic effects, etc. In this paper, an enhanced data-driven shape array estimation scheme is provided in the passive underwater acoustic data, and a novel nonlinear outlier-robust particle filter (ORPF) method is proposed to acquire enhanced estimates of time delays in the presence of distorted hydrophone array. A conventional beamforming technique based on a hypothetical array is first used, and the detection of the narrow-band components is sequentially carried out so that the corresponding amplitudes and phases at these narrow-band components can be acquired. We convert the towed array estimation problem into a nonlinear discrete-time filtering problem with the joint estimates of amplitudes and time-delay differences, and then propose the ORPF method to acquire enhanced estimates of the time delays by exploiting the underlying properties of slowly changing time-delay differences across sensors. The proposed scheme fully exploits directional radiated noise targets as sources of opportunity for online array shape estimation, and thus it requires neither the number nor direction of sources to be known in advance. Both simulations and real experimental data show the effectiveness of the proposed method.

Coordination dynamics of multi-agent interaction in a musical ensemble

Humans interact with other humans at a variety of timescales and in a variety of social contexts. We exhibit patterns of coordination that may differ depending on whether we are genuinely interacting as part of a coordinated group of individuals vs merely co-existing within the same physical space. Moreover, the local coordination dynamics of an interacting pair of individuals in an otherwise non-interacting group may spread, propagating change in the global coordination dynamics and interaction of an entire crowd. Dynamical systems analyses, such as Recurrence Quantification Analysis (RQA), can shed light on some of the underlying coordination dynamics of multi-agent human interaction. We used RQA to examine the coordination dynamics of a performance of “Welcome to the Imagination World”, composed for wind orchestra. This performance enacts a real-life simulation of the transition from uncoordinated, non-interacting individuals to a coordinated, interacting multi-agent group. Unlike previous studies of social interaction in musical performance which rely on different aspects of video and/or acoustic data recorded from each individual, this project analyzes group-level coordination patterns solely from the group-level acoustic data of an audio recording of the performance. Recurrence and stability measures extracted from the audio recording increased when musicians coordinated as an interacting group. Variability in these measures also increased, indicating that the interacting ensemble of musicians were able to explore a greater variety of behavior than when they performed as non-interacting individuals. As an orchestrated (non-emergent) example of coordination, we believe these analyses provide an indication of approximate expected distributions for recurrence patterns that may be measurable before and after truly emergent coordination.

Hong Kong Women Project a Larger Body When Speaking to Attractive Men

In this pilot study we investigated the vocal strategies of Cantonese women when addressing an attractive vs. unattractive male. We recruited 19 young female native speakers of Hong Kong Cantonese who completed an attractiveness rating task, followed by a speech production task where they were presented a subset of the same faces. By comparing the rating results and corresponding acoustic data of the facial stimuli, we found that when young Cantonese women spoke to an attractive male, they were less breathy, lower in fundamental frequency, and with denser formants, all of which are considered to project a larger body. Participants who were more satisfied with their own height used these vocal strategies more actively. These results are discussed in terms of the body size projection principle.

Acoustic and Genetic Data Can Reduce Uncertainty Regarding Populations of Migratory Tree-Roosting Bats Impacted by Wind Energy

Wind turbine-related mortality may pose a population-level threat for migratory tree-roosting bats, such as the hoary bat (Lasiurus cinereus) in North America. These species are dispersed within their range, making it impractical to estimate census populations size using traditional survey methods. Nonetheless, understanding population size and trends is essential for evaluating and mitigating risk from wind turbine mortality. Using various sampling techniques, including systematic acoustic sampling and genetic analyses, we argue that building a weight of evidence regarding bat population status and trends is possible to (1) assess the sustainability of mortality associated with wind turbines; (2) determine the level of mitigation required; and (3) evaluate the effectiveness of mitigation measures to ensure population viability for these species. Long-term, systematic data collection remains the most viable option for reducing uncertainty regarding population trends for migratory tree-roosting bats. We recommend collecting acoustic data using the statistically robust North American Bat Monitoring Program (NABat) protocols and that genetic diversity is monitored at repeated time intervals to show species trends. There are no short-term actions to resolve these population-level questions; however, we discuss opportunities for relatively short-term investments that will lead to long-term success in reducing uncertainty.

Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. In this study, evolutionary optimized DNNs were analyzed to classify three common airway-related symptoms, namely coughs, throat clears and dry swallows. As opposed to typical microphone-acoustic signals, mechano-acoustic data signals, which did not contain identifiable speech information for better privacy protection, were acquired from laboratory-generated and publicly available datasets. The optimized DNNs had a low footprint of less than 150 kB and predicted airway symptoms of interests with 83.7% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel-frequency bands up to 8,000 Hz were found as the most important feature for the classification. We further found that DNN decisions were consistently relying on these specific features, fostering trust and transparency of proposed DNNs. Our proposed efficient and explainable DNN is expected to support edge computing on mechano-acoustic sensing wearables for remote, long-term monitoring of airway symptoms.

Bubble vent localization for marine hydrocarbon seep surveys

Commercial success of marine seep hunting exploration campaigns involves acquisition of high-quality bathymetry and backscatter along with targeted coring of seep sediments. The sharp lateral chemical gradient encompassing seafloor seeps requires accurate identification of seep sites from high-resolution acoustic data. Active seeps featuring plumes of gas bubbles and oil droplets rising into the water column can be imaged in modern multibeam echosounders providing an effective approach to remotely characterizing seafloor seeps. Interpreting the seafloor position of gas plume emissions in multibeam data using existing mapping methodology is hindered by slow processing due to large files sizes, a manual “by eye” qualitative assessment of each sonar ping searching for plume anomalies, skill and fatigue of the geoscientist, and environmental or acquisition artifacts that can mask the precise location of gas emission on the seafloor. These limitations of midwater backscatter mapping create a qualitative dataset with varying inherent positional errors that can lead to missed or incorrect observations about seep-related seafloor features and processes. By vertically integrating midwater multibeam amplitude samples, a two-dimensional midwater backscatter raster can be generated and draped over seafloor morphology, providing a synoptic overview of the spatial distribution of gas plume emission sites for improved interpretation. A multibeam midwater dataset from NOAA Cruise EX1402L2 in the northwestern Gulf of Mexico is reprocessed using a vertical amplitude stacking technique. Midwater backscatter surfaces are compared to digitized plume positions collected during the survey for a comparison into assessing uncertainty in mapping approaches and an assessment of uncertainty. Results show that the accuracy of digitized geopicks over selected plume clusters vary considerably when compared to the midwater backscatter amplitude maps. This mapping technique offers multiple advantages over traditional geopicking from cost-effectiveness, offshore efficiency, repeatability, and higher accuracy, ultimately improving the detectability and sampling of active seafloor seeps through precisely located cores.