Discovering Auditory Objects Through Non-Negativity Constraints

ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, SAPA 2004

Volumn , Issue , 2004, Pages

  Smaragdis, Paris ^a  

^a MITSUBISHI ELECTRIC RESEARCH LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUDITORY OBJECTS; BUILDING ELEMENT; ICA ALGORITHMS; MAGNITUDE ANALYSIS; NONNEGATIVITY CONSTRAINTS; NOVEL METHODS; ROBUST METHODS; SELF-ORGANISED; SPECTRAL MAGNITUDES; STATISTICAL MEASURES;

EID: 78049383291     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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4. Lee, D.D. and H.S. Seung.”Learning the parts of objects with nonnegative matrix factorization”. In Nature, 401:788 791, 1999.
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6. Plumbley, M. D.”Algorithms for non-negative independent component analysis”. In IEEE Transactions on Neural Networks, 14(3), pp534- 543, May 2003.
7. Plumbley, M. D.”Conditions for non-negative independent component analysis”. In IEEE Signal Processing Letters, 9(6), pp177-180, June 2002.
8. Smaragdis, P.”Redundancy Reduction for Computational Audition, a Unifying Approach”, Doctoral Dissertation, MAS Dept. Massachusetts Institute of Technology, Cambridge MA, USA, 2001.
9. Smaragdis, P. and J.C. Brown.”Non-Negative Matrix Factorization for Polyphonic Music Transcription”, in IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY, October 2003.
10. Virtanen, T.”Separation of Sound Sources by Convolutive Sparse Coding” In ISCA Tutorial and Research Workshop on Statistical and Perceptual Audio Processing, October 2004.