NEW METHOD FOR ESTIMATION OF MULTI-HARMONIC POWER SIGNAL PARAMETERS

A systematic analytical procedure for simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves was proposed in this paper. In order to reduce complexity in the calculation of unknown parameters, a completely new reduced analytical expression is derived, which enabled fast and precise estimation with a small numerical error. Individual sinusoidal components stand out from the input complex-harmonic signal with the filter with a finite-impulse response (FIR) comb filters. The algorithm that is proposed in the operation is based on the application of partial derivate of the processed and filtered input signal, after which it is performed weighted estimation procedure to better estimate the values size of the fundamental frequency, amplitude and the multi-sinusoid signal phase. The proposed algorithm can be used in the signal reconstruction and estimation procedures, spectral processing, in procedures for the identification of the system that is observed, as well as other important signal processing areas. Through the simulation check, the effectiveness of the proposed algorithm was assessed, which confirmed its high performance.

Searching for Galactic H ii Regions from the LAMOST Spectroscopic Database

In this paper, we pioneer a new machine-learning method to search for H ii regions in spectra from The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). H ii regions are emission nebulae created when young and massive stars ionize nearby gas clouds with high-energy ultraviolet radiation. Having more H ii region samples will help us understand the formation and evolution of stars. Machine-learning methods are often applied to search for special celestial bodies such as H ii regions. LAMOST has conducted spectral surveys and provided a wealth of valuable spectra for the research of special and rare celestial bodies. To overcome the problem of sparse positive samples and diversification of negative samples, a novel method called the self-calibrated convolution network is introduced and implemented for spectral processing. A deep network classifier with a structure called a self-calibrated block provides a high precision rate, and the recall rate is improved by adding the strategy of positive-unlabeled bagging. Experimental results show that this method can achieve better performance than other current methods. Eighty-nine spectra are identified as Galactic H ii regions after cross-matching with the WISE Catalog of Galactic H ii Regions, confirming the effectiveness of the method proposed in this paper.

Fast-time and slow-time processing of the pseudorandom amplitude-phase-shift keyed signals

The two-dimensional raw data structure is used for modern pulse-Doppler radars. Fast-time and slow-time processing of radar return signals is performed. The matched filter compresses each received pulse in fast time. The FFT-based spectral processing of the compressed pulses is then performed in slow time. The two-dimensional structure of raw data has specific features in radars with the transmission and reception of pseudorandom amplitude-phase-shift keyed (APSK) signals to a common aerial. It is formed when the coherent processing interval of the APSK signal is divided into subintervals. The article describes the fast-time and slow-time processing of the APSK signal subintervals. The structure of the signal in the subintervals is also analyzed. The choice of the subinterval duration is discussed. The possible energy losses during the processing of the reflected signals are estimated. The results of the processing modeling of the additive sum of APSK signals with different Doppler frequencies are presented.

ERUO: a spectral processing routine for the MRR-PRO

The Micro Rain Radar (MRR) PRO is a K-band Doppler weather radar, using frequency modulated continuous wave (FMCW) signals, developed by Metek Meteorologische Messtechnik GmbH (Metek) as successor to the MRR-2. Benefiting from four datasets collected during two field campaigns in Antarctica and Switzerland, we developed a processing library for snowfall measurements, named ERUO (Enhancement and Reconstruction of the spectrUm for the MRR-PRO), with a two-fold objective. Firstly, the proposed method addresses a series of issues plaguing the radar variables, which include interference lines, power drops at the extremes of the Doppler spectrum and abrupt cutoff of the transfer function. Secondly, the algorithm aims to improve the quality of the final variables, by lowering the minimum detectable equivalent attenuated reflectivity factor and extending the valid Doppler velocity range through antialiasing. The performance of the algorithm has been tested against the measurements of a co-located W-band Doppler radar. Information from a close-by X-Band Doppler dual-polarization radar has been used to exclude unsuitable radar volumes from the comparison. Particular attention has been dedicated to verify the estimation of the meteorological signal in the spectra covered by interferences.

Detection and Identification of Organic Pollutants in Drinking Water from Fluorescence Spectra Based on Deep Learning Using Convolutional Autoencoder

Three dimensional fluorescence spectroscopy has become increasingly useful in the detection of organic pollutants. However, this approach is limited by decreased accuracy in identifying low concentration pollutants. In this research, a new identification method for organic pollutants in drinking water is accordingly proposed using three-dimensional fluorescence spectroscopy data and a deep learning algorithm. A novel application of a convolutional autoencoder was designed to process high-dimensional fluorescence data and extract multi-scale features from the spectrum of drinking water samples containing organic pollutants. Extreme Gradient Boosting (XGBoost), an implementation of gradient-boosted decision trees, was used to identify the organic pollutants based on the obtained features. Method identification performance was validated on three typical organic pollutants in different concentrations for the scenario of accidental pollution. Results showed that the proposed method achieved increasing accuracy, in the case of both high-(>10 μg/L) and low-(≤10 μg/L) concentration pollutant samples. Compared to traditional spectrum processing techniques, the convolutional autoencoder-based approach enabled obtaining features of enhanced detail from fluorescence spectral data. Moreover, evidence indicated that the proposed method maintained the detection ability in conditions whereby the background water changes. It can effectively reduce the rate of misjudgments associated with the fluctuation of drinking water quality. This study demonstrates the possibility of using deep learning algorithms for spectral processing and contamination detection in drinking water.

Computer Sound Design

The principles of sound design within a computational context are demonstrated through a series of examples and techniques. These include additive synthesis, which is the focus of the earlier part of the chapter, and is followed by source-modifier methods, which are complementary to it. The more advanced approaches of granular synthesis and streaming spectral processing complement the discussion, which is fully illustrated with code examples and spectrogram figures. The chapter concludes with an overview of design approaches.

Time-Frequency Processing

The idea of dynamic spectral processing, introduced at the end of the previous chapter is fully developed here. The principle of sub-band analysis and synthesis is shown as the basis for a time-varying frequency-domain approach. The short-time Fourier transform (STFT) is introduced as a sequence of time-ordered DFT frames from which amplitude and phase data can be obtained. Different methods for instantaneous frequency estimation are discussed. A streaming system for dynamic spectral processing is introduced, and various modification techniques are explored. The latter part of the chapter presents the Hilbert transform as yet another streaming spectral processing application. The chapter concludes with further additions to the notions of spectrum developed earlier in the volume.

Development of Mandarin Chinese Vowel Perception in Young Children With Normal Hearing and Cochlear Implants

Purpose Depicting the development pattern of vowel perception for children with normal hearing (NH) and cochlear implants (CIs) would be useful for clinicians and school teachers to monitor children's auditory rehabilitation. The study was to investigate the development of Mandarin Chinese vowel perception for Mandarin Chinese native–speaking children with the ages of 4–6 years. Method Vowel identification of children with NH and CIs were tested. All children with CIs received CIs before the age of 4 years. In a picture identification task with Mandarin Chinese speech stimuli, listeners identified the target consonant–vowel word among two to four contrastive words that differed only in vowels. Each target word represented a concrete object and was spoken by a young female native Mandarin Chinese talker. The target words included 16 monophthongs, 22 diphthongs, and nine triphthongs. Results Children with NH showed significantly better identification of monophthongs and diphthongs than children with CIs at the age of 6 years, whereas the two groups had comparable performance at age of 4 and 5 years. Children with NH significantly outperformed children with CIs for triphthong identification across all three age groups. For children with NH, a rapid development of perception of all three types of vowels occurred between age 4 and 5 years with a rapid development only for monophthong perception between age 5 and 6 years. For children with CIs, a rapid development of both diphthong and triphthong perception occurred between 4 and 5 years old, but not monophthong, with no significant development between 5 and 6 years old for all three types of vowels. Overall, Mandarin-speaking children with NH achieved their ceiling performance in vowel perception before or at the age of 6 years, whereas children with CIs may need more time to reach the typical level of their peers with NH. Conclusions The development of Mandarin vowel perception for Mandarin-native children differed between preschool-age children with NH and CIs, likely due to the deficits of spectral processing for children with CIs. The results would be a supplement to the development of speech recognition in Mandarin-native children with NH and CIs.

Effect of Increased Daily Cochlear Implant Use on Auditory Perception in Adults

Purpose Despite the recommendation for cochlear implant (CI) processor use during all waking hours, variability in average daily wear time remains high. Previous work has shown that objective wear time is significantly correlated with speech recognition outcomes. We aimed to investigate the causal link between daily wear time and speech recognition outcomes and assess one potential underlying mechanism, spectral processing, driving the causal link. We hypothesized that increased CI use would result in improved speech recognition via improved spectral processing. Method Twenty adult CI recipients completed two study visits. The baseline visit included auditory perception testing (speech recognition and spectral processing measures), questionnaire administration, and documentation of data logging from the CI software. Participants watched an educational video, and they were informed of the compensation schedule. Participants were then asked to increase their daily CI use over a 4-week period during everyday life. Baseline measures were reassessed following the 4-week period. Results Seventeen out of 20 participants increased their daily CI use. On average, participants’ speech recognition improved by 3.0, 2.4, and 7.0 percentage points per hour of increased average daily CI use for consonant–nucleus–consonant words, AzBio sentences, and AzBio sentences in noise, respectively. Questionnaire scores were similar between visits. Spectral processing showed significant improvement and accounted for a small amount of variance in the change in speech recognition values. Conclusions Improved consistency of processor use over a 4-week period yielded significant improvements in speech recognition scores. Though a significant factor, spectral processing is likely not the only mechanism driving improvement in speech recognition; further research is warranted.