My research interests include multimedia information retrieval,
machine learning, data mining and databases. My current research (as
of 2003) and thesis topic focus on music database search, indexing
and retrieval based on perceived similarity, that is, given a piece of
musical recording (in raw audio format), how can we find similar (but
not necessarily identical) pieces from the database? A more detailed
description and some sound samples can be found here. Prior to this, I did research on data
mining algorithms, computer vision and machine learning.
Here is a list of my publications:
- Cheng Yang: Peer-to-Peer Architecture for Content-Based Music Retrieval on Acoustic Data.
In International World Wide Web Conference, 2003.
- Cheng Yang: Efficient Acoustic Index for Music Retrieval with Various Degrees of Similarity.
In Proc. ACM Multimedia, 2002.
PS file, PDF file.
- Cheng Yang: The MACSIS Acoustic Indexing Framework for Music Retrieval: An Experimental Study.
In International Conference on Music Information Retrieval, 2002.
- Cheng Yang: MACS: Music Audio Characteristic Sequence Indexing for Similarity Retrieval.
PDF file.
In
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
, 2001.
- Cheng Yang: Music Database Retrieval Based on Spectral Similarity.
In International Symposium on Music Information Retrieval, 2001.
A longer version can be found here: PDF file.
(Stanford University Database Group technical report 2001-14.)
- Cheng Yang, Usama Fayyad and Paul S. Bradley: Efficient Discovery of Error-Tolerant Frequent Itemsets in High Dimensions.
In ACM International Conference on Knowledge Discovery and Data Mining, 2001.
PS file, PDF file.
- Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D. Ullman, Cheng Yang: Finding Interesting Associations without Support Pruning.
In IEEE Transactions on Knowledge and Data Engineering, Vol. 13, No. 1, Jan./Feb. 2001.
- Edith Cohen, Mayur Datar, Shinji Fujiwara, Aristides Gionis, Piotr Indyk, Rajeev Motwani, Jeffrey D. Ullman, Cheng Yang: Finding Interesting Associations without Support Pruning.
In IEEE International Conference on Data Engineering, 2000.
- Cheng Yang and Tomás Lozano-Pérez: Image Database Retrieval with Multiple-Instance Learning Techniques.
In IEEE International Conference on Data Engineering, 2000.
PS file,
PDF file.
Database Group
Stanford University