2021 – 2022 Research Highlights
Deep learning applications for audio and music
My research interests are in deep learning applications for audio and music. Research projects in the APTLY lab that I direct include detection of generated speech, spoken language identification, and deep learning-based sound design. I am also interested in music classification, music transcription, as well as music generation. I have also recently started a research program on open education resources and the automatic derivation of learning paths.
Research Highlights
- “Synthetic Speech Detection Using Deep Neural Networks”,
Ricardo Reimao and Vassilios Tzerpos,
to be submitted to the 2021 IEEE International Conference on Speech Technology and
Human-Computer Dialogue (SpeD). - “Evaluating The Effects of Timbre Transfer on Multiple Instruments”, Pedro Casas and Vassilios Tzerpos, in preparation.
- “Spoken Language Identification Using Acoustic Features and Deep Neural Networks”, by Rajshree Daulatabad and Vassilios Tzerpos, in preparation.
- “Music-STAR: A Style Transforming system for Audio-based Rearrangement”, by Mahshid Alinoori and Vassilios Tzerpos, in preparation.
- MITACS Accelerate: “Ultrasound image analysis for identifying blood in an existing effusion in knee joint”, $15,000.