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The Efficacy of Deep Learning-Based Mixed Model for Speech Emotion Recognition

Computers, Materials & Continua 2023年第1期74卷 1709-1722页

作者： mohammad amaz uddin mohammad Salah uddin Chowdury Mayeen uddin Khandaker Nissren Tamam Abdelmoneim Sulieman Department of Computer Science and Engineering BGC Trust University BangladeshChittagong4381Bangladesh Centre for Applied Physics and Radiation Technologies School of Engineering and TechnologySunway UniversityBandar SunwaySelangor47500Malaysia Department of Physics College of SciencesPrincess Nourah bint Abdulrahman UniversityP.O Box 84428Riyadh11671Saudi Arabia Department of Radiology and Medical Imaging Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Human speech indirectly represents the mental state or emotion of *** use of Artificial Intelligence(AI)-based techniques may bring revolution in this modern era by recognizing emotion from *** this study,we introduced a robust method for emotion recognition from human speech using a well-performed preprocessing technique together with the deep learning-based mixed model consisting of Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN).About 2800 audio files were extracted from the Toronto emotional speech set(TESS)database for this study.A high pass and Savitzky Golay Filter have been used to obtain noise-free as well as smooth audio data.A total of seven types of emotions;Angry,Disgust,Fear,Happy,Neutral,Pleasant-surprise,and Sad were used in this ***,Fundamental frequency,and Mel Frequency Cepstral Coefficient(MFCC)have been used to extract the emotion features,and these features resulted in 97.5%accuracy in the mixed LSTM+CNN *** mixed model is found to be performed better than the usual state-of-the-art models in emotion recognition from *** also indicates that this mixed model could be effectively utilized in advanced research dealing with sound processing.

关键词： Emotion recognition Savitzky Golay fundamental frequency MFCC neural networks

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FungalTraits:a user-friendly traits database of fungi and fungus-like stramenopiles

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Fungal Diversity 2020年第2期105卷 I0001-I0016页

作者： Sergei Põlme Kessy Abarenkov RHenrik Nilsson Björn D.Lindahl Karina Engelbrecht Clemmensen Havard Kauserud Nhu Nguyen Rasmus Kjøller Scott T.Bates Petr Baldrian Tobias Guldberg Frøslev Kristjan Adojaan Alfredo Vizzini Ave Suija Donald Pfister Hans-Otto Baral Helle Järv Hugo Madrid Jenni Nordén Jian-Kui Liu Julia Pawlowska Kadri Põldmaa Kadri Pärtel Kadri Runnel Karen Hansen Karl-Henrik Larsson Kevin David Hyde Marcelo Sandoval-Denis Matthew E.Smith Merje Toome-Heller Nalin N.Wijayawardene Nelson Menolli Jr Nicole K.Reynolds Rein Drenkhan Sajeewa S.N.Maharachchikumbura Tatiana B.Gibertoni Thomas Læssøe William Davis Yuri Tokarev Adriana Corrales Adriene Mayra Soares Ahto Agan Alexandre Reis Machado Andrés Argüelles-Moyao Andrew Detheridge Angelina de Meiras-Ottoni Annemieke Verbeken Arun Kumar Dutta Bao-Kai Cui C.K.Pradeep César Marín Daniel Stanton Daniyal Gohar Dhanushka N.Wanasinghe Eveli Otsing Farzad Aslani Gareth W.Griffith Thorsten H.Lumbsch Hans-Peter Grossart Hossein Masigol Ina Timling Inga Hiiesalu Jane Oja John Y.Kupagme József Geml Julieta Alvarez-Manjarrez Kai Ilves Kaire Loit Kalev Adamson Kazuhide Nara Kati Küngas Keilor Rojas-Jimenez Krišs Bitenieks Laszlo Irinyi LászlóGNagy Liina Soonvald Li-Wei Zhou Lysett Wagner M.Catherine Aime MaarjaÖpik María Isabel Mujica Martin Metsoja Martin Ryberg Martti Vasar Masao Murata Matthew PNelsen Michelle Cleary Milan C.Samarakoon Mingkwan Doilom mohammad Bahram Niloufar Hagh-Doust Olesya Dulya Peter Johnston Petr Kohout Qian Chen Qing Tian Rajasree Nandi Rasekh Amiri Rekhani Hansika Perera Renata dos Santos Chikowski Renato L.Mendes-Alvarenga Roberto Garibay-Orijel Robin Gielen Rungtiwa Phookamsak Ruvishika S.Jayawardena Saleh Rahimlou Samantha C.Karunarathna Saowaluck Tibpromma Shawn P.Brown Siim-Kaarel Sepp Sunil Mundra Zhu-Hua Luo Tanay Bose Tanel Vahter Tarquin Netherway Teng Yang Tom May Torda Varga Wei Li Victor Rafael Matos Coimbra Virton Rodrigo Targino de Oliveira Vitor Xavier de Lima Vladimir S.Mikryukov Yongzhong Lu Yosuke Matsuda Yumiko Miyamoto Urm Institute of Ecology and Earth Sciences University of Tartu14A Ravila50411 TartuEstonia Natural History Museum University of Tartu46 Vanemuise Str51003 TartuEstonia Gothenburg Global Biodiversity Centre Box 46140530 GothenburgSweden Department of Biological and Environmental Sciences University of GothenburgGothenburgSweden Department of Soil and Environment Swedish University of Agricultural SciencesBox 701475007 UppsalaSweden Department of Forest Mycology and Plant Pathology Swedish University of Agricultural SciencesBox 702675007 UppsalaSweden Section for Genetics and Evolutionary Biology(EvoGene) Department of BiosciencesUniversity of OsloP.O.Box 1066BlindernNO-0316 OsloNorway University of Hawaiʻi at Mānoa 3190 Maile WaySt.John 102HonoluluHIUSA Department of Biology University of CopenhagenUniversitetsparken 152100 CopenhagenØDenmark Purdue University Northwest WestvilleIN 46391USA Institute of Microbiology Czech Academy of SciencesVidenska 108314220 Praha 4Czech Republic GLOBE Institute University of CopenhagenØster Farimagsgade 5KH7.2.321353 CopenhagenDenmark Dipartimento di Scienze della Vita e Biologia dei Sistemi Universitàdi TorinoViale Mattioli 2510125 TorinoItaly Department of Organismic and Evolutionary Biology and Farlow Library and Herbarium Harvard University22 Divinity AveCambridgeMA 02138USA Blaihofstr.42 72074 TübingenGermany SYNLAB Estonia Veerenni 53aTallinn 11313Estonia Escuela de Tecnología Médica Universidad Santo TomásLos Carreras 753OsornoChile Centro de Genómica y Bioinformática Universidad MayorCamino La Pirámide 5750HuechurabaSantiagoChile Norwegian Institute for Nature Research(NINA) Sognsveien 680855 OsloNorway School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731People’s Republic of China Evolutionary Biology Faculty of BiologyBiological and Chemical Research CentreUniversity of Warsawul.Zwirki i Wigury 10102-089 WarsawPoland Department of Botany Swedish Museum of

The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental *** the past decades,rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and ***,in spite of the progress of molecular methods,knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains *** order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis *** the information from previous efforts such as FUNGuild and FunFun together with involvement of expert knowledge,we reannotated 10,210 and 151 fungal and Stramenopila genera,*** resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera,designed for rapid functional assignments of environmental *** order to assign the trait states to fungal species hypotheses,the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS *** the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1%dissimilarity threshold.

关键词： Fungal traits Trophic modes Function Guild Bioinformatics High-throughput sequencing Community ecology

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