Harmonic Differences Method for Robust Fundamental Frequency Detection in Wideband and Narrowband Speech Signals

In this article, a novel pitch determination algorithm based on harmonic differences method (HDM) is proposed. Most of the algorithms today rely on autocorrelation, cepstrum, and lastly convolutional neural networks, and they have some limitations (small datasets, wideband or narrowband, musical sounds, temporal smoothing, etc.), accuracy, and speed problems. There are very rare works exploiting the spacing between the harmonics. HDM is designed for both wideband and exclusively narrowband (telephone) speech and tries to find the most repeating difference between the harmonics of speech signal. We use three vowel databases in our experiments, namely, Hillenbrand Vowel Database, Texas Vowel Database, and Vowels from the TIMIT corpus. We compare HDM with autocorrelation, cepstrum, YIN, YAAPT, CREPE, and FCN algorithms. Results show that harmonic differences are reliable and fast choice for robust pitch detection. Also, it is superior to others in most cases.

Reconstruction of satellite time series with a dynamic smoother

Time series reconstruction methods---used to generate gap-free time series of satellite observations---were historically designed for sensors with frequent image acquisitions. Since 2008, interest in leveraging time series methods has shifted from sensors such as AVHRR and MODIS to Landsat because of free, higher-resolution data availability and improved access to high-performance compute systems. Existing methods are typically designed for specific applications such as land cover classification or for estimating the timing of phenology events.Moreover, approaches developed for specific ecological systems, such as tropical forests or temperate agriculture, often do not generalize well across land cover, vegetation, and climate types. In this study, we introduce a dynamic temporal smoothing (DTS) method to reconstruct sparse, noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with dynamic parameters that is applied over a signal. The smoother is intended to have wide applicability, with particular focus on applications in vegetation remote sensing. In this paper we present and illustrate the DTS over short- and long-term Landsat (TM, ETM+, and OLI) time series and demonstrate the effectiveness of robust gap-filling over a range of landscapes in the South American Southern Cone region.

Monitoring war destruction from space using machine learning

Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.

Nonstationary local time-frequency transform

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

Spatiotemporal wind energy potential estimation and analysis in Japan

<p>For an efficient integration of wind and solar resources toward sustainable energy systems, it is crucial to consider their fluctuations in space and time. Current spatial wind potential estimations in Japan are limited to the annual average of wind speed. In this study, we evaluate the spatial and temporal evolution of both onshore and offshore wind energy potential in Japan based on 5 km mesh and 1-hour sampling weather forecast data. We then demonstrate the benefits of cross-border sharing on the power output stability and identify important sites having high average potential and low average correlation with other sites for the temporal smoothing of power output.</p>

Effect of Temporal and Spatial Smoothing on Speckle–Tracking-Derived Strain in Neonates

Clinical application of strain in neonates requires an understanding of which image acquisition and processing parameters affect strain values. Previous studies have examined frame rate, transmitting frequency, and vendor heterogeneity. However, there is a lack of human studies on how user-regulated spatial and temporal smoothing affect strain values in 36 neonates. This study examined nine different combinations of spatial and temporal smoothing on peak systolic left ventricular longitudinal strain in 36 healthy neonates. Strain values were acquired from four-chamber echocardiographic images in the software-defined epicardial, midwall, and endocardial layers in the six standard segments and average four-chamber stain. Strain values were compared using repeated measure ANOVAs. Overall, spatial smoothing had a larger impact than temporal smoothing, and segmental strain values were more sensitive to smoothing settings than average four-chamber strain. Apicoseptal strain decreased by approximately 4% with increasing spatial smoothing, corresponding to a 13–19% proportional change (depending on wall layer). Therefore, we recommend clinicians be mindful of smoothing settings when assessing segmental strain values.

Coral Bleaching Detection in the Hawaiian Islands Using Spatio-Temporal Standardized Bottom Reflectance and Planet Dove Satellites

We present a new method for the detection of coral bleaching using satellite time-series data. While the detection of coral bleaching from satellite imagery is difficult due to the low signal-to-noise ratio of benthic reflectance, we overcame this difficulty using three approaches: 1) specialized pre-processing developed for Planet Dove satellites, 2) a time-series approach for determining baseline reflectance statistics, and 3) a regional filter based on a preexisting map of live coral. The time-series was divided into a baseline period (April-July 2019), when no coral bleaching was known to have taken place, and a bleaching period (August 2019-present), when the bleaching was known to have occurred based on field data. The identification of the bleaching period allowed the computation of a Standardized Bottom Reflectance (SBR) for each region. SBR transforms the weekly bottom reflectance into a value relative to the baseline reflectance distribution statistics, increasing the sensitivity to bleaching detection. We tested three scales of the temporal smoothing of the SBR (weekly, cumulative average, and three-week moving average). Our field verification of coral bleaching throughout the main Hawaiian Islands showed that the cumulative average and three-week moving average smoothing detected the highest proportion of coral bleaching locations, correctly identifying 11 and 10 out of 18 locations, respectively. However, the three-week moving average provided a better sensitivity in coral bleaching detection, with a performance increase of at least one standard deviation, which helps define the confidence level of a detected bleaching event.

A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.