Results 161 to 170 of about 550,629 (233)

"RaagaDhvani: A novel augmented multi-feature dataset: Advancing emotion recognition in Carnatic music with multimodal features and hybrid deep learning". [PDF]

Data Brief
Priyadarshini A, Divakarla U.
europepmc   +1 more source

DESIGNING STRATEGIC AND INTERACTIVE SIGNING INSTRUCTION TO SUPPORT SIGNED LITERACY FOR DEAF LEARNERS. [PDF]

Int J Des Learn
Holcomb L.
europepmc   +1 more source

Categorizing music by genres. [PDF]

Ann N Y Acad Sci
Lange EB, Gernandt E, Merrill J.
europepmc   +1 more source

Color as physical/conceptual attribute: Color word translation by Chinese postgraduate students. [PDF]

PLoS One
Wang H, Lv G, Tian X.
europepmc   +1 more source

Dialogism in publicity discourses of Anglo-American and Chinese universities: A comparative analysis based on Engagement System. [PDF]

PLoS One
Zheng L.
europepmc   +1 more source

The Bayes factor to discriminate Molière and Corneille as authors of classical French plays. [PDF]

Sci Rep
Bozza S, Cammarota V, Roten CA, Roten V, Jover A, Taroni F.   +5 more
europepmc   +1 more source

Deep learning model using squeezenet and promoted ideal gas molecular motion for music genre classification from audio spectrograms. [PDF]

Sci Rep
Xue M.
europepmc   +1 more source

An interpretable multi-transformer ensemble for text-based movie genre classification. [PDF]

PeerJ Comput Sci
Shaukat F, Ejaz N, Ashraf Z, Alnfiai MM, Alotaibi NN, Alnefaie SMM.   +5 more
europepmc   +1 more source

Music preferences, listening purposes, perfectionism and substance use in Egyptian college students: a cross-sectional study. [PDF]

BMC Psychol
Ahmed N, Ahmed M, Gillipsie B, Sharaf F, Salah K, Eissa L, Abdrabou R, Essayed R, Yasser R, Zaky Y, Sayed M.   +10 more
europepmc   +1 more source

Taylor Swift versus Mozart: music preferences of C57BL/6J mice. [PDF]

Front Behav Neurosci
Kamionek D, Maass JG, Pitzer C, Schaaf CP.   +3 more
europepmc   +1 more source