FINAL YEAR PROJECT ON

IMPROVED SIGNAL-TO-NOISE RATIO

ESTIMATION FOR SPEECH ENHANCEMENT

SPEECH ENHANCEMENT- Improvement in the quality or intelligibility of a speech signal

SPEECH CLEANING- the reversal of degradations that have corrupted it

- 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

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

The removal of background noise Echo suppression The process of artificially bringing certain

frequencies into the speech signal

CENTRAL METHODS FOR ENHANCING SPEECH

power spectral subtraction Wiener filtering soft-decision estimation Minimum Mean Square Error (or MMSE)

estimation

noise reduction techniques

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

PRIORI SNR-defined before wiener filter

POSTERIORI SNR-defined after wiener filter

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

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)

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

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

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

REFERENCES:-