1 / 16

Multiple Pitch Tracking for Blind Source Separation Using a Single Microphone

BGU. Multiple Pitch Tracking for Blind Source Separation Using a Single Microphone. Joseph Tabrikian Dept. of Electrical and Computer Engineering Ben-Gurion University of the Negev Workshop on: Speech Enhancement and Multichannel Audio Processing Technion 22.2.2007. BGU. Outline.

hans
Download Presentation

Multiple Pitch Tracking for Blind Source Separation Using a Single Microphone

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BGU Multiple Pitch Tracking for Blind Source Separation Using a Single Microphone Joseph Tabrikian Dept. of Electrical and Computer Engineering Ben-Gurion University of the Negev Workshop on: Speech Enhancement and Multichannel Audio Processing Technion 22.2.2007

  2. BGU Outline • Motivation • Single source pitch estimation and tracking • Multiple source pitch estimation and tracking • Experiments • Conclusion

  3. BGU Motivation • Speech enhancement • Sensitivity of many audio processing algorithms to interference. For example: • Automatic speech/speaker recognition • Speech/music compression • Single microphone blind source separation (BSS) • Karaoke

  4. BGU Single Source - Modeling • Voice frames - harmonic model: additive Gaussian noise • In matrix notation:

  5. BGU Single Source – Pitch Tracking • Maximum Likelihood (ML) estimator: • Pitch tracking: The data vector at the mth frame: - first-order Markov process: • Maximum A-posteriori Probability (MAP) pitch tracking via the Viterbi algorithm. (Tabrikian-Dubnov-Dickalov 2004)

  6. BGU Single Source - Voicing Decision • Unvoiced model • Colored Gaussian noise model: • Voiced/unvoiced decision by the Generalized Likelihood Ratio Test (GLRT): (Fisher-Tabrikian-Dubnov 2006)

  7. BGU Multiple Sources • ML estimator of from under the model: with unknown signal and unknown (Gaussian) noise covariance: (Harmanci-Tabrikian-Krolik 2000)

  8. BGU Multiple Sources • Voiced model: v includes other interferences. is unknown. • Using J overlapping subframes of size Ls (2K+1<J< Ls): jth column of :

  9. BGU Multiple Sources • Pitch tracking: The data vector at the mth frame: - first-order Markov process  Maximum A-posteriori Probability (MAP) pitch tracking via the Viterbi algorithm

  10. BGU Multiple Sources - Voicing Decision • Unvoiced model Colored Gaussian noise model: • Voiced/unvoiced decision by the GLRT: (Fisher-Tabrikian-Dubnov 2007)

  11. BGU Multiple Source Models • Exact ML for the strongest voiced signal, and “locally ML” for other voiced signals Likelihood function

  12. BGU Experiments – Single Source

  13. BGU Experiments - Two Sources

  14. BGU Experiments – Voicing Decision

  15. BGU Experiments - – Voicing Decision

  16. BGU Conclusions • ML pitch estimation for single and multiple sources have been developed under the harmonic model for voiced frames. • The derived likelihood functions under the two models allow implementation of the Viterbi algorithm for MAP pitch tracking. • The GLRT for voicing decision is derived under the two models. • Future work: • development of multiple hypothesis tracking methods for single microphone BSS. • Adaptive estimation of the number of harmonics

More Related