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FINAL YEAR PROJECT ON
IMPROVED SIGNAL-TO-NOISE RATIO
ESTIMATION FOR SPEECH ENHANCEMENT
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SPEECH ENHANCEMENT- Improvement in the quality or intelligibility of a speech signal
SPEECH CLEANING- the reversal of degradations that have corrupted it
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- Improving quality and inteligibility (hearing aid, cockpit comm., video conferencing ...)
- Source coding (mobile phone, video conferencing, IP phone ...)
- Pre-processor for other speech processing applications (speech recognition, speaker varification ...)
APPLICATIONS
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The aims of speech cleaning vary according to the application and may include:
Improvements in the intelligibility of speech Improvement in the quality of speech Modifications to the speech that lead to
improved performance Modifications to the speech so that it may
be encoded more effectively
AIMS OF SPEECH ENHANCEMENT
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The removal of background noise Echo suppression The process of artificially bringing certain
frequencies into the speech signal
CENTRAL METHODS FOR ENHANCING SPEECH
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power spectral subtraction Wiener filtering soft-decision estimation Minimum Mean Square Error (or MMSE)
estimation
noise reduction techniques
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Emphasises portions of noisy signal spectrum where snr is high
Attenuates portions of spectrum where snr is low
the amount of noise reduction is in general proportional to the amount of speech degradation.
Wiener filter
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PRIORI SNR-defined before wiener filter
POSTERIORI SNR-defined after wiener filter
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Decision-directed (DD) Approach
a priori SNR follows the a posteriori SNR with a delay of one frame in speech frames.
estimated noise suppression gain matches the previous frame rather than the current frame and thus it degrades the quality of
ESTIMATION OF A PRIORI SNR
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refine the estimation of the a priori SNR removes the drawbacks of the DD approach
while maintaining its advantage, i.e., highly reduced musical noise level.
major advantage – suppression of the frame delay bias
TWO-STEP NOISE REDUCTION (TSNR)
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Some harmonics are considered as noise only components and consequently are suppressed by the noise reduction process.
ANOTHER TECHNIQUE USED-harmonic regeneration noise reduction (HRNR).
Limitation of TSNR
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Takes into account the harmonic characteristic of speech.
output signal of any classic noise reduction technique (with missing or degraded harmonics) is further processed to create an artificial signal where the missing harmonics have been automatically regenerated.
HRNR
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Claude Marro and Pascal Scalart “Improved Signal-to-Noise Ratio Estimation for Speech Enhancement”,IEEE transactions on audio, speech, and language processing, vol. 14, no. 6, november 2006
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