'Spectral subtraction' diaporamas de présentation

Robustness Techniques for Speech Recognition

Robustness Techniques for Speech Recognition. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References: 1. X. Huang et al. Spoken Language Processing (2001). Chapter 10

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Digital Audio Signal Processing Lecture-4: Noise Reduction

Digital Audio Signal Processing Lecture-4: Noise Reduction. Marc Moonen/Alexander Bertrand Dept. E.E./ESAT-STADIUS, KU Leuven marc.moonen@esat.kuleuven.be homes.esat.kuleuven.be /~ moonen /. Overview. Spectral subtraction for single -micr. noise reduction

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M. Tech. project, EE Dept., IIT Bombay, Jun. 2013. Real-time Enhancement of Noisy Speech

M. Tech. project, EE Dept., IIT Bombay, Jun. 2013. Real-time Enhancement of Noisy Speech Using Spectral Subtraction Santosh K. Waddi (10307932) Supervisor: Prof. P. C. Pandey. IIT Bombay June 2013. Overview Introduction Speech Enhancement Using Spectral Subtraction

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466 views • 36 slides

SRI 2001 SPINE Evaluation System

SRI 2001 SPINE Evaluation System. Venkata Ramana Rao Gadde Andreas Stolcke Dimitra Vergyri Jing Zheng Kemal Sonmez Anand Venkataraman. Results Dryrun, Evaluation What worked What didn't Fourier cepstrum revisited Evaluation Issues Future Work Conclusions. Talk Overview.

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Enhancement of Electrolaryngeal Speech by Reducing Leakage Noise Using Spectral Subtraction by

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Meeting 6

Meeting 6. Esfandiar Zavarehei Department of Electronic and Computer Engineering Brunel University 6 July, 2005. Review of Noise Reduction Methods (more Results) Review of the methods DFT-Kalman, a new method for parameter estimation Evaluation results and sample speech signals

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Comparison of Wavelet and FFT Based Single Channel Speech Signal Noise Reduction Techniques

Comparison of Wavelet and FFT Based Single Channel Speech Signal Noise Reduction Techniques. Ningping Fan, Radu Balan, Justinian Rosca Siemens Corporate Research Inc. SPIE Optics East 2004. Shot Time Discrete Fourier Transform in Frequency Presentation. k = 0. k = 1. k = 2. k = 3. k = 4.

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Frequency Kalman Noise Reduction

Frequency Kalman Noise Reduction. Esfandiar Zavarehei Department of Electronic and Computer Engineering Brunel University 28 July, 2004. Y ( e jw ) = X ( e jw ) + N ( e jw ) | Y ( e jw )| 2 = | X ( e jw )| 2 + | N ( e jw )| 2 + +2 | X ( e jw )| | N ( e jw )| cos ( ? ? )

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Enhancement of Speech in Noisy Conditions Project Presentation Paul Coffey

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Advances in WP1

Trento Meeting 11-12 January 2007. Advances in WP1. www.loquendo.com. WP1: Environment & Sensor Robustness T1.2 Noise Independence. Noise Reduction: Spectral Subtraction (YEAR 1) and Spectral Attenuation (YEAR2) Evaluation of feature normalization techniques in Loquendo ASR

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The YL2000 FTIR

The YL2000 FTIR. YL2000 FTIR Features. General - A low cost and compact design of the YL2000 ensures High performance. - The YL2000 measures just 59 X 39 X 19 cm. - Optical design, software and firmware designed specifically reduce overall analytical times significantly.

548 views • 8 slides

Enhanced energy redistribution speech intelligibility algorithm with real-time implementation

TWO APPROACHES TO SPEECH INTELLIGIBILITY RESEARCH 1) Target cues in highly intelligible speech -- Clear speech -- Lombard effect -- Hyper-articulated speech 2) Target cues lost due to hearing impairment -- Loudness recruitment, elevated threshold

137 views • 1 slides