Missing data imputation for time-frequency representations of audio signals

Journal of Signal Processing Systems

Volumn 65, Issue 3, 2011, Pages 361-370

  Smaragdis, Paris ^a   Raj, Bhiksha ^b   Shashanka, Madhusudana ^c  

^a ADOBE RESEARCH   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c UNITED TECHNOLOGIES RESEARCH CENTER   (United States)

Author keywords

Audio; Imputation; Missing data; Time frequency

Indexed keywords

ACOUSTIC MIXTURES; AUDIO; AUDIO PROCESSING; AUDIO SIGNAL; IMPUTATION; MISSING DATA; MISSING VALUES; TIME FREQUENCY; TIME FREQUENCY DOMAIN; TIME-FREQUENCY REPRESENTATIONS;

FREQUENCY DOMAIN ANALYSIS;

AUDIO SIGNAL PROCESSING;

EID: 81855166765     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-010-0512-7     Document Type: Article

References (19)

1. Raj, B. (2000). Reconstruction of incomplete spectrograms for robust speech recognition. Ph.D. Dissertation, Carnegie Mellon University.
2. Roweis, S. T. (2000). One microphone source separation (pp. 793-799). NIPS.
3. Brand, M. E. (2002). Incremental singular value decomposition of uncertain data with missing values, European conference on computer vision (ECCV) (Vol. 2350, pp. 707-720).
4. Reyes-Gomez, M. J., Jojic, N., & Ellis, D. P. W. (2004). Detailed graphical models for source separation and missing data interpolation in audio. Utah: Snowbird Learning Workshop Snowbird.
5. Le Roux, J, Kameoka, H., Ono, N., de Cheveigné, A., & Sagayama, S. (2008). Computational auditory induction by missing-data non-negative matrix factorization. Brisbane, Australia: SAPA.
6. Shashanka, M., Raj, B., & Smaragdis, P. (2000). Sparse overcomplete latent variable decomposition of counts data. NIPS.
7. Smith, J. O. (2007). Spectral audio signal processing. March 2007 draft. http://ccrma.stanford.edu/~jos/sasp/. Accessed June 2008.
8. David, M. H., Little, R. J. A., Samuhel, M. E., & Triest, R. K. (1983). Imputation methods based on the propensity to respond. In Proceedings of the business and economics section, American statistical association.
9. Quinlan, J. R. (1989). Unknown attribute values in induction. In Proc. of the sixth international conference on machine learning.
10. Ghaharamani, Z., & Jordan, M. I. (1994). Learning from incomplete data. Technical report AI Memo 1509. Artificial Intelligence Laboratory, MIT.
11. B Raj ML Seltzer RM Stern 2004 Reconstruction of missing features for robust speech recognition Speech Communication Journal 43 4 275 296 10.1016/j.specom.2004.03.007
12. Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M., Brown, P., & Botstein, D. (1999). Imputing missing data for gene expression arrays. Technical report. Stanford Statistics Department.
13. Hofmann, T. (2000). Learning the similarity of documents: An information-geometric approach to document retrieval and categorization. In Advances in neural information processing systems (Vol. 12, pp. 914-920). Cambridge: MIT Press.
14. Hofmann, T., & Puzicha, J. (1998). Unsupervised learning from dyadic data. TR 98-042. Berkeley: ICSI.
15. Hazewinkel, M. Encyclopedia of mathematics. http://eom.springer.de/.
16. AP Dempster NM Laird DB Rubin 1977 Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society, B 39 1 38 501537 0364.62022
17. DW Griffin JS Lim 1984 Signal reconstruction from short-time Fourier transform magnitude IEEE Transactions of Acoustics, Speech, and Signal Processing 32 2 236 243 10.1109/TASSP.1984.1164317
18. Bouvrie, J., & Ezzat, T. (2006). An incremental algorithm for signal reconstruction from short-time Fourier transform magnitude, in interspeech. USA: Pittsburgh.
19. Gould, G. (1994). Bach: The two and three part inventions-the Glenn Gould edition, by SONY classics. ASIN B000GF2YZ8.