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Audio fingerprint retrieval algorithm using anti-fingerprint and frequency domain segmentation

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Chinese Journal of Acoustics 2023年第1期42卷 82-97页

作者： CHEN Shuli ZHANG Xueshuai ZHANG Pengyuan LIU Jian Key Laboratory of Speech Acoustics and Content Understanding Institute of AcousticsChinese Academy of SciencesBeijing100190 University of Chinese Academy of Sciences Beijing 100049

In order to solve the problem of low audio fingerprint retrieval recognition rate under background sound and noise conditions,a novel algorithm based on mute masking and frequency segmentation is proposed.In the fingerprint extraction stage,voice activity detection technology is used to remove the non-valid speech frames,and then the valid speech frames are recombined and features are extracted according to the difference of the adjacent sub-band energy,which can effectively solve the problem that silence frame fingerprint characteristics are not robust.In the matching stage,according to the distribution characteristics of different audio signals in the frequency domain,the audio fingerprints are segmented and weighted in different frequencies to calculate the similarity between the template and the test audio more accurately.Experiments show that the proposed algorithm doubles the retrieval speed compared with the classic Philips algorithm.In the meantime,it yields a large definite improvement over Philips by 17.94%on mean average precision and 4.66%on recall respectively for the data set disturbed by background sounds.Compared with the latest Philips algorithm,the mean average precision and recall have been increased by 13.68%and 2.45%respectively.

关键词： algorithm frames template

An Efficient algorithm for Self-consistent Field Theory Calculations of Complex Self-assembled Structures of Block Copolymer Melts

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Chinese Journal of Polymer Science 2018年第4期36卷 488-496页

作者： Jun-Qing Song Yi-Xin Liu Hong-Dong Zhang State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science Fudan University

Self-consistent field theory（SCFT）, as a state-of-the-art technique for studying the self-assembly of block copolymers, is attracting continuous efforts to improve its accuracy and efficiency. Here we present a fourth-order exponential time differencing Runge-Kutta algorithm（ETDRK4） to solve the modified diffusion equation（MDE） which is the most time-consuming part of a SCFT calculation. By making a careful comparison with currently most efficient and popular algorithms, we demonstrate that the ETDRK4 algorithm significantly reduces the number of chain contour steps in solving the MDE, resulting in a boost of the overall computation efficiency, while it shares the same spatial accuracy with other algorithms. In addition, to demonstrate the power of our ETDRK4 algorithm, we apply it to compute the phase boundaries of the bicontinuous gyroid phase in the strong segregation regime and to verify the existence of the triple point of the O70 phase, the lamellar phase and the cylindrical phase.

关键词： Block copolymer Self-consistent field theory algorithm Pseudo-spectral Phase structure

Effect of a region-wide incorporation of an algorithm based on the 2012 international consensus guideline on the practice pattern for the management of pancreatic cystic neoplasms in an integrated health system

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World Journal of Clinical Cases 2018年第13期6卷 624-631页

作者： Andrew Khoi Nguyen Agathon Girgis Timnit Tekeste Karen Chang Mopelola Adeyemo Armen Eskandari Emilio Alonso Priyanka Yaramada Charles Chaya Albert Ko Edmund Burke Isaiah Roggow Rebecca Butler Aniket Kawatkar Brian S Lim School of Medicine University of California Department of Gastroenterology Kaiser Permanente Riverside Medical Center Department of Surgery Kaiser Permanente Riverside Medical Center Department of Research and Evaluation Southern California Permanente Medical Group

AIM To examine the practice pattern in Kaiser Permanente Southern California(KPSC), i.e., gastroenterology(GI)/surgery referrals and endoscopic ultrasound(EUS), for pancreatic cystic neoplasms(PCNs) after the regionwide dissemination of the PCN management algorithm.METHODS Retrospective review was performed; patients with PCN diagnosis given between April 2012 and April 2015(18 mo before and after the publication of the algorithm) in KPSC(integrated health system with 15 hospitals and 202 medical offices in Southern California) were identified.RESULTS2558(1157 pre-and 1401 post-algorithm) received a new diagnosis of PCN in the study period. There was no difference in the mean cyst size(pre-19.1 mm vs post-18.5 mm, P = 0.119). A smaller percentage of PCNs resulted in EUS after the implementation of the algorithm(pre-45.5% vs post-34.8%, P algorithm for PCN management in an integrated health system resulted in fewer EUS and GI/surgery referrals, likely by aiding the physicians ordering imaging studies in the decision making for the management of PCNs. This translated to cost saving of 24%, 18%, and 36% for EUS, GI, and surgical consultations, respectively, with total diagnostic cost saving of 24%.

关键词： Pancreatic cyst algorithm incorporation Pancreas Optimization of resource utilization Fukuoka criteria Sendai criteria Pancreatic cancer Intraductal papillary mucinous neoplasm Practice management algorithm

A non-intrusive speech quality evaluation algorithm combining auxiliary target learning and convolutional recurrent network

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Chinese Journal of Acoustics 2023年第2期42卷 235-250页

作者： TANG Guichen LIANG Ruiyu KONG Fanliu XIE Yue JU Mengjie School of Information and Communication Engineering Nanjing Institute of TechnologyNanjing 211167 School of Information Science and Engineering Southeast UniversityNanjing 210096

The objective evaluation of speech quality can replace expensive manual scoring,but current objective indicators usually need pure reference speech,which is difficult to obtain in many practical acoustic systems.A non-intrusive speech quality evaluation algorithm combining auxiliary target learning and a convolutional recurrent network(CRN)is proposed.Bark frequency cepstral coefficients(BFCCs),which are based on human-like auditory filters,are used as the input of the CRN network to effectively reduce the network complexity.Firstly,frame-level features are extracted by a convolutional neural network(CNN)from BFCCs.Then,long-term time dependence and sequence features are modeled by the bidirectional long shortterm memory(BiLSTM)networks in frame-level features.Finally,a self-attention mechanism is introduced into the CRN,thereby adaptively extracting useful information from frame-level features,which is then integrated into the characteristics of the sentence level and mapped into the final objective score.In addition,a multi-task training strategy is adopted,and voice activity detection(VAD)is introduced as an auxiliary learning target to improve the performance of the algorithm.Experiments in public databases show that compared with other non-intrusive algorithms,the proposed algorithm has a better correlation with the mean opinion score(MOS).Moreover,it has a small parameter size and good generalization ability for the distorted speech database with MOS released by ITU-T P.808,which is close to the accuracy of the perceptual evaluation of speech quality(PESQ).

关键词： network speech algorithm

Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm

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Chinese Chemical Letters 2020年第3期31卷 890-896页

作者： Jiawei Zhang Jianfu Chen Peijun Hu Haifeng Wang Key Laboratory for Advanced Materials Research Institute of Industrial Catalysis and Centre for Computational ChemistrySchool of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China School of Chemistry and Chemical Engineering The Queen's University of BelfastBelfast BT95AGUnited Kingdom

Bimetallic nanoparticles(AmBn)usually exhibit rich catalytic chemistry and have drawn tremendous attention in heterogeneous catalysis.However,challenged by the huge configuration space,the understanding toward their composition and distribution of A/B element is known little at the atomic level,which hinders the rational synthesis.Herein,we develop an on-the-fly training strategy combing the machine learning model(SchNet)with the genetic algorithm(GA)search technique,which achieve the fast and accurate energy prediction of complex bimetallic clusters at the DFT level.Taking the 38-atom PtmAu38-mnanoparticle as example,the element distribution identification problem and the stability trend as a function of Pt/Au composition is quantitatively re solved.Specifically,results show that on the Pt-rich cluster Au atoms prefer to occupy the low-coordinated surface corner sites and form patch-like surface segregation patte rns,while for the Au-rich ones Pt atoms tend to site in the co re region and form the co re-shell(Pt@Au)configuration.The thermodynamically most stable PtmAu38-mcluster is Pt6 Au32,with all the core-region sites occupied by Pt,rationalized by the stronger Pt-Pt bond in comparison with Pt-Au and Au-Au bonds.This work exemplifies the potent application of rapid global sea rch enabled by machine learning in exploring the high-dimensional configuration space of bimetallic nanocatalysts.

关键词： Density functional theory calculation Machine learning Genetic algorithm Bimetallic nanoparticle PtAu cluster

Two-dimensional self-adapting fast Fourier transform algorithm for nanoparticle sizing by ultrafast image-based dynamic light scattering

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Particuology 2018年第6期16卷 74-84页

作者： Dechuan Zhang Xiaoshu Cai Wu Zhou Institute of Particle and Two-Phase Flow Measurement University of Shanghai for Sdence and TechnologyShanghai Key Laboratory of Multiphase Flow and Heat Transfer in Power Engineering (University of Shanghai for Sdence and Technology)Shanghai 200093China

In nanoparticle sizing using the ultrafast image-based dynamic light scattering (UIDLS)method,larger impurities and dark noise from the complementary metal-oxide-semiconductor (CMOS)detector affect measurement accuracy.To solve this problem,a two-dimensional self-adapting fast Fourier transform (2D-SAFFT)algorithm is proposed for UIDLS.Dynamic light scattering images of nanoparticles are processed using 2D fast Fourier transforms,and a high-frequency threshold and a low-frequency threshold are then set using the self-adapting algorithm to eliminate the effects of the dark noise of the CMOS detector and the impurities.The signals caused by the dark noise of the CMOS detector and the impurities are cut off using the high-frequency threshold and the low-frequency threshold.The signals without the high- and low-frequency components are then processed again using an inverse Fourier transform to obtain new images without the dark noise and impurities signals.The mean diameters of the measured nanoparticles can be obtained from images obtained using UIDLS.Five standard latex nanoparticles (46,100, 203,508,994nm)and commercial nanoparticles (antimony-doped tin oxide,indium tin oxide,TWEEN- 80,nano-Fe,and nano-Al2O3)were measured using this new method.Results show that 2D-SAFFT can effectively eliminate the effects of dark noise from the CMOS detector and the impurities.

关键词： Dynamic light scattering Nanoparticle size Fast Fourier transform algorithm Spectrum segmentation

Downhole Microseismic Source Location Based on a Multi-Dimensional DIRECT algorithm for Unconventional Oil and Gas Reservoir Exploration

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Acta Geologica Sinica(English Edition) 2019年第3期93卷 718-730页

作者： YIN Qifeng TAO Pengfei ZHENG Shuo HE Qing AN Yanfei GUO Quanshi School of Resources and Environmental Engineering Anhui UniversityHefei 230601China Sinopec Geophysical Research Institute Nanjing 211103China

Downhole microseismic data has the significant advantages of high signal-to-noise ratio and well-developed P and S waves and the core component of microseismic monitoring is microseismic event location associated with hydraulic fracturing in a relatively high confidence level and accuracy.In this study,we present a multidimensional DIRECT inversion method for microseismic locations and applicability tests over modeling data based on a downhole microseismic monitoring system.Synthetic tests inidcate that the objective function of locations can be defined as a multi-dimensional matrix space by employing the global optimization DIRECT algorithm,because it can be run without the initial value and objective function derivation,and the discretely scattered objective points lead to an expeditious contraction of objective functions in each dimension.This study shows that the DIRECT algorithm can be extensively applied in real downhole microseismic monitoring data from hydraulic fracturing completions.Therefore,the methodology,based on a multidimensional DIRECT algorithm,can provide significant high accuracy and convergent efficiency as well as robust computation for interpretable spatiotemporal microseismic evolution,which is more suitable for real-time processing of a large amount of downhole microseismic monitoring data.

关键词： unconventional oil and gas reservoir downhole microseismic monitoring source location DIRECT algorithm

A Fuzzy Decision Based WSN Localization algorithm for Wise Healthcare

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China Communications 2019年第4期16卷 208-218页

作者： Jiangyu Yan Ran Qiao Liangrui Tang Chenxi Zheng Bing Fan North China Electric Power University

Wise healthcare is a typical application of wireless sensor network(WSN), which uses sensors to monitor the physiological state of nursing targets and locate their position in case of an emergency situation. The location of targets need to be determined and reported to the control center,and this leads to the localization problem. While localization in healthcare field demands high accuracy and regional adaptability, the information processing mechanism of human thinking has been introduced,which includes knowledge accumulation, knowledge fusion and knowledge expansion. Furthermore, a fuzzy decision based localization approach is proposed. Received signal strength(RSS) at references points are obtained and processed as position relationship indicators, using fuzzy set theory in the knowledge accumulation stage; after that, optimize degree of membership corresponding to each anchor nodes in different environments during knowledge fusion; the matching degree of reference points is further calculated and sorted in decision-making, and the coordinates of several points with the highest matching degree are utilized to estimate the location of unknown nodes while knowledge expansion. Simulation results show that the proposed algorithm get better accuracy performance compared to several traditional algorithms under different typical occasions.

关键词： WSN localization wise healthcare fuzzy decision algorithm reference points matching degree

Genetic algorithm-based multiple moving target reaching using a fleet of sailboats

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IET Cyber-Systems and Robotics 2019年第3期1卷 93-100页

作者： Viel Christophe Vaultier Ulysse Wan Jian Jaulin Luc School of Engineering Computing and MathematicsUniversity of PlymouthPlymouthDevonUK Lab STICC ENSTA BretagneBrestFrance

This study addresses the problem of Dynamic Travelling Salesman Problem for a multi-agent system using a fleet of sailboats.A genetic algorithm(GA)is proposed,which attributes to each agent a varying number of targets to be collected.GA allows obtaining a suboptimal solution in the shortest time possible.Moreover,this study adapts it to the specific problem involving a fleet of sailboats,which is a challenging task with comparison to autonomous underwater vehicles or motorised vehicles in terms of the propulsion.Therein motors can be flexibly controlled while sailboat movements are constrained by available wind direction and speed.Thus the method takes into account wind conditions at various locations of the sailboat.Simulation results demonstrate the effectiveness of the proposed approach.

关键词： agent flex algorithm