RA recruitment goes through the whole year. Both XJTLU students and non-XJTLU students could apply. Please contact Dr. Shengchen Li for further information.
Usually students applying for a RA position should propose a project. From time to time, there will be some mini projects calling for RAs.
RA may be invited to continue their research as a PhD student at XJTLU. Usually grant will be provided according to performance of RA.
This year there are three SURF projects offered this year. For each SURF proejct, students are suppose to spend 10 weeks onsite to perform basic research. Usually SURF projects only accpet XJTLU students. The abstract of three projects are as follows.
All projects are subject to approval of XJTLU. If you are interested in applying one of the SURF projects listed here, please fill in this survey to register your interests.
Expressive Timing Modelling in Performed Classical Piano Music: This project aims to develop an algorithm that can analyse and model the unique expressive timing of a particular pianist, enabling the generation of performances of any piece of music in the style of the candidate pianist. By capturing the subtle nuances and variations in timing and dynamics that define a style of performance, this research will contribute to the field of expressive musical performance modelling. The obtained model would support a better understanding of newly composed music for composers, providing insights into how their compositions might be interpreted and performed by the modelled pianist. The anticipated outcomes include a working algorithm capable of generating stylistically accurate performances and valuable insights into the modelling of expressive timing. This research has the potential to advance our understanding of musical expression and open up new possibilities for computer-generated music that more closely resembles human performances, as well as assisting composers in visualising the potential interpretations of their work.
Detection and Identification of Synthesised Music: In the modern music landscape, the line between synthesised and real music has become increasingly blurred. This project aims to develop an algorithm capable of accurately identifying synthesised music among real musical compositions. By leveraging advanced signal processing techniques and machine learning algorithms, the proposed system will analyse musical samples and determine whether they are synthesised or performed by human musicians. The development of such an algorithm has significant implications for various fields, including music copyright protection, plagiarism detection, and musicological research. The anticipated outcomes include a robust algorithm for synthesised music detection and a deeper understanding of the characteristics that distinguish synthesised music from real musical performances. This research has the potential to contribute to the advancement of music technology and its applications in multiple domains.
Detection and Identification of Cover Song: In the vast landscape of music, cover songs – different renditions of the same musical composition – are a common occurrence. This project aims to develop an algorithm capable of accurately identifying cover songs among a collection of musical recordings. By leveraging advanced audio signal processing techniques and machine learning algorithms, the proposed system will analyze musical features and determine whether two given recordings are renditions of the same piece. The development of such an algorithm has significant implications for various fields, including music copyright protection, music recommendation systems, and musicological research. The anticipated outcomes include a robust algorithm for cover song identification and a deeper understanding of the musical characteristics that define a song's identity across different renditions. This research has the potential to contribute to the advancement of music information retrieval and its applications in multiple domains.
Throughout the year, PhD application is welcome. Three full scholarship PhD positions are expected to be released by the end of 2024.
The candidate student will be awarded a PhD degree from the University of Liverpool (UK) upon successful completion of a PhD program.
The candidate should have a first class or upper second class honours degree, or a master’s degree (or equivalent qualification), in related fields such as Electronic Engineering and Computer Science.
Evidence of good spoken and written English is essential. The candidate should have an IELTS score of 6.5 or above, if the first language is not English. This position is open to all qualified candidates irrespective of nationality.
Students with Chinese nationality currently do not have an accurate enroll date at this stage (due to quota restrictions). Students with other nationalities will follow the normal enroll date as regulated by University of Liverpool.
Further information could be found at XJTLU official pages of entry requirements and fees information
Materials for Application: