Yi Yu
Lei Chen
ABSTRACT
Multi-variant music tracks are those audio tracks of a particular song which are sung and recorded by different people (i.e., cover songs). As music social clubs grow on the Internet, more and more people like to upload music recordings onto such music social sites to share their own home-produced albums and participate in Internet singing contests. Therefore it is very important to explore a computer-assisted evaluation tool to detect these audio-based multi-variant tracks. In this paper we investigate such a task: the original track of a song is embedded in datasets, with a batch of multi-variant audio tracks of this song as input, our retrieval system returns an ordered list by similarity and indicates the position of relevant audio track. To help process multi-variant audio tracks, we suggest a semantic indexing framework and propose the Federated Features (FF) scheme to generate the semantic summarization of audio feature sequences. The conjunction of federated features with three typical similarity searching schemes, K-Nearest Neighbor (KNN), Locality Sensitive Hashing (LSH), and Exact Euclidian LSH (E2LSH), is evaluated. From these findings, a computer-assisted evaluation tool for searching multi-variant audio tracks was developed to search over large musical audio datasets.
- J. S. Downie. The Music Information Retrieval Evaluation eXchange (MIREX). In D-Lib Magazine 12, 2006. http://dlib.org/dlib/december06/downie/12downie.html.Google Scholar
- J. P. Bello. Audio-based Cover Song Retrieval Using Approximate Chord Sequences: Testing Shifts, Gaps, Swaps and Beats. ISMIR'07, pp.239--244, 2007.Google Scholar
- D. Ellis and G. Poliner. Identifying cover songs with chroma features and dynamic programming beat tracking. ICASSP'07, 2007.Google ScholarCross Ref
- Y. Yu, K. Joe, and J. S. Downie. Efficient Query-by- Content Audio Retrieval by Locality Sensitive Hashing and Partial Sequence Comparison. IEICE Transaction on Information and System, Vol.E91-D, No.6, pp. 1730--1739, 2008. Google ScholarDigital Library
- Y. Yu, J. S. Downie, and K. Joe. An Evaluation of Feature Extraction for Query-by-Content Audio Information Retrieval. Ninth IEEE International Symposium on Multimedia Workshops (ISMW), pp. 297--302, 2007. Google ScholarDigital Library
- Y. Yu, M. Takata, and K. Joe. Index-Based Similarity Searching with Partial Sequence Comparison for Query-by-Content Audio Retrieval. Workshop on Learning Semantics of Audio Signals (LSAS'06), pp.76--86, 2006.Google Scholar
- F. Moerchen, I. Mierswa, and A. Ultsch. Understandable Models of Music Collection based on Exhaustive Feature Generation with Temporal Statistics. KDD'06, pp.882--891, 2006. Google ScholarDigital Library
- C. Yang. Efficient Acoustic Index for Music Retrieval with Various Degrees of Similarity. ACM Multimedia, pp. 584--591, 2002. Google ScholarDigital Library
- B. Cui, J. L. Shen, G. Cong, H. T. Shen, and C. Yu. Exploring Composite Acoustic Features for Efficient Music Similarity Query. ACM MM'06, pp.634--642, 2006. Google ScholarDigital Library
- T. Pohle, M. Schedl, P. Knees, and G. Widmer. Automatically Adapting the Structure of Audio Similarity Spaces. Workshop on Learning Semantics of Audio Signals (LSAS'06), pp. 66--75, 2006.Google Scholar
- LSH Algorithm and Implementation (E2LSH) http://web.mit.edu/andoni/www/LSH/index.html.Google Scholar
- P. Indyk and N. Thaper. Fast color image retrieval via embeddings. Workshop on Statistical and Computational Theories of Vision (ICCV), 2003.Google Scholar
- S. Y. Hu. Efficient Video Retrieval by Locality Sensitive Hashing. ICASSP'05, pp.449--452, 2005.Google Scholar
- J. Reiss, J. J. Aucouturier, and M. Sandler. Efficient multi dimensional searching routines for music information retrieval. ISMIR'01, 2001.Google Scholar
- I. Karydis, A. Nanopoulos, A. N. Papadopoulos and Y. Manolopoulos. Audio Indexing for Efficient Music Information Retrieval. MMM'05, pp. 22--29, 2005. Google ScholarDigital Library
- M. Casey and M. Slaney. Song Intersection by Approximate Nearest Neighbor Search. ISMIR'06, pp. 144--149, 2006.Google Scholar
- M. Lesaffre and M. Leman. Using Fuzzy to Handle Semantic Descriptions of Music in a Content-based Retrieval System. Workshop on Learning Semantics of Audio Signals (LSAS'06), pp.43--5, 2006.Google Scholar
- G. Tzanetakis and P. Cook. Musical Genre Classification of Audio Signals. IEEE Transactions on Speech and Audio Processing, Vol.10, No.5, pp. 293--302, 2002.Google ScholarCross Ref
- R. Miotto and N. Orio. A Methodology for the Segmentation and Identification of Music Works. ISMIR'07, pp.239--244, 2007.Google Scholar
- L. Rabiner and B.-H. Juang. Fundamentals of Speech Recognition. Prentice-Hall, 1993. Google ScholarDigital Library
Index Terms
- Searching musical audio datasets by a batch of multi-variant tracks
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