Media

EFFICIENT SUPER-WIDE BANDWIDTH EXTENSION USING LINEAR PREDICTION BASED ANALYSIS-SYNTHESIS

P. Bachhav, M. Todisco, C. Beaugeant and N. Evans

(Submitted to WASPAA 2017)

 

Databases

AMR-WB

(12.65kbps)

EVS

(13.2kbps)

Proposed

EHBE
CMU Arctic A1 A1 A1 A1
  A2 A2 A2 A2
  A3 A3 A3 A3
  A4 A4 A4 A4
  A5 A5 A5 A5
3GPP A6 A6 A6 A6
  A7 A7 A7 A7
  A8 A8 A8 A8
  A9 A9 A9 A9
  A10 A10 A10 A10
TSP speech A11 A11 A11 A11
  A12 A12 A12 A12
  A13 A13 A13 A13
  A14 A14 A14 A14
  A15 A15 A15 A15

 


Artificial Bandwidth Extention using the Constant Q Transform

P. Bachhav, M. Todisco, M. Mossi, C. Beaugeant and N. Evans

(to appear in Proc. of  ICASSP 2017)

 

NB

Original WB

OG-OP

EG-EP (Proposed)

M1 M1 M1 M1
M2 M2 M2 M2
M3 M3 M3 M3
M4 M4 M4 M4
M5 M5 M5 M5
F1 F1 F1 F1
F2 F2 F2 F2
F3 F3 F3 F3
F4 F4 F4 F4
F5 F5 F5 F5

 


 

An experimental framework for the derivation of

perceptually-optimal noise suppression functions

A. Daniel, L. Lepauloux, C. Yemdji, N. Evans and C. Beaugeant

(Submitted to ICASSP 2013)

 

Demonstration video:

 

Sound samples:

These samples allow for a direct comparison between a conventional Wiener filter and a "perceptually-optimal" filter applying the mean experimental noise suppression curve obtained in this paper. It is recommended to listen to these samples through headphones at a comfortable level to be in the same conditions as the participants.

Clean speech, additive white noise, noisy speech (0 dB SNR).

Utterance #1: Wiener filter, perceptual filter

Utterance #2: Wiener filter, perceptual filter

Utterance #3: Wiener filter, perceptual filter