Spectrogram Dimensionality Reduction with Independence Constraints

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We propose an algorithm to find a low-dimensional decomposition of a spectrogram by formulating this as a regularized non-negative matrix factorization (NMF) problem with a regularization term chosen to encourage independence. This algorithm provides a better decomposition than standard NMF when the underlying sources are independent. It makes better use of additional observation streams than previous nonnegative ICA algorithms.


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    •  NEWS    ICASSP 2010: 9 publications by Anthony Vetro, Shantanu D. Rane and Petros T. Boufounos
      Date: March 14, 2010
      Where: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
      MERL Contacts: Anthony Vetro; Petros T. Boufounos
      • The papers "Privacy and Security of Features Extracted from Minutiae Aggregates" by Nagar, A., Rane, S.D. and Vetro, A., "Hiding Information Inside Structured Shapes" by Das, S., Rane, S.D. and Vetro, A., "Ultrasonic Sensing for Robust Speech Recognition" by Srinivasan, S., Raj, B. and Ezzat, T., "Reconstruction of Sparse Signals from Distorted Randomized Measurements" by Boufounos, P.T., "Disparity Search Range Estimation: Enforcing Temporal Consistency" by Min, D., Yea, S., Arican, Z. and Vetro, A., "Synthesizing Speech from Doppler Signals" by Toth, A.R., Raj, B., Kalgaonkar, K. and Ezzat, T., "Spectrogram Dimensionality Reduction with Independence Constraints" by Wilson, K.W. and Raj, B., "Robust Regression using Sparse Learning for High Dimensional Parameter Estimation Problems" by Mitra, K., Veeraraghavan, A.N. and Chellappa, R. and "Subword Unit Approaches for Retrieval by Voice" by Gouvea, E., Ezzat, T. and Raj, B. were presented at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP).