Gang Ren – Page 2 – Frost Institute for Data Science & Computing

Modeling Between-Partial Components for Musical Timbre Imitation and Migration

Most musical sounds have strong and regularly distributed spectral components such as harmonic partials. However, the energy distribution patterns between any two such sonic partials, the in-between low-energy signal patterns such as performance articulation or instrument signatures, are also important for characterizing musical sounds. Read more “Modeling Between-Partial Components for Musical Timbre Imitation and Migration” →

Publications

17th IEEE International Conference on Machine Learning and Applications

IEEE’s ICMLA 2018 (International Conference on Machine Learning and Applications) held December 17-20, in Orlando FL, aimed to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). The Conference provided an international forum for the dissemination of original research in ML, Read more “17th IEEE International Conference on Machine Learning and Applications” →

Publications

Student Retention Pattern Prediction Employing Linguistic Features Extracted from…

This paper investigates the use of linguistic features extracted from the application essay so students enrolled in a university academic program for their retention pattern prediction. Three sets of linguistic features are generated from text analysis: Read more “Student Retention Pattern Prediction Employing Linguistic Features Extracted from Admission Application Essays” →

News

Multimodal Content Analysis for Effective Advertisements on YouTube

The recent advancement of web-scale digital advertising saw a paradigm shift from the conventional focus of digital advertisement distribution towards integrating digital processes and methodologies and forming a seamless workflow of advertisement design, production, distribution, and effectiveness monitoring. Read more “Multimodal Content Analysis for Effective Advertisements on YouTube” →

Publications

A Matlab-Based Signal Processing Toolbox for Musical Vibrato

To assess and manipulate the vibrato in musical sounds, audio engineers either informally listen to the audio or visually inspect waveform envelopes or spectrographic representations. Unfortunately, detailed descriptions of the amplitude and frequency trajectories of harmonic partials are difficult to infer from audio spectrograms, which means quantitative information is limited. Read more “A Matlab-Based Signal Processing Toolbox for Musical Vibrato” →

n>Gang Ren

Modeling Between-Partial Components for Musical Timbre Imitation and Migration

17th IEEE International Conference on Machine Learning and Applications

Student Retention Pattern Prediction Employing Linguistic Features Extracted from…

Multimodal Content Analysis for Effective Advertisements on YouTube

A Matlab-Based Signal Processing Toolbox for Musical Vibrato