Process for searching for a noise model in noisy audio signals

• US 6,438,513 B1
• Filed: 12/30/1999
• Issued: 08/20/2002
• Est. Priority Date: 07/04/1997
• Status: Expired due to Term

• Current Assignee: Sextant Avionique
• Original Assignee: Sextant Avionique
• Inventors: Pastor, Dominique, Reynaud, Gérard
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): SUAREZ, FELIX E
• Application Number:
  US09/446,886
• Time in Patent Office: 964 Days
• Field of Search: 704/246, 704/233, 704/253, 702/66, 702/77, 702/191, 381/94.7, 364/574, 364/572, 395/2.35
• US Class Current: 702/191
• CPC Class Codes: G10L 2021/02168 the estimation exclusively taking place during speech pauses
• CPC Class Codes: G10L 21/0208 Noise filtering

Abstract

A process for the denoising of audio signals picked up in a noisy environment, for example in the cockpit of an aircraft or of another vehicle, and more precisely to the searching for a noise model in the audio signals. Input signals are digitized, and these signals are processed on the basis of a noise model, in principle with a view to eliminate as far as possible the noise corresponding to the model. The input signals are chopped into successive frames of P samples each, and a repetitive search for a noise model is performed continuously in the input signals themselves, by searching for N successive frames (N lying between a minimum N1 and a maximum N2) having the expected characteristics of a noise, by storing N×P corresponding samples so as to construct a noise model useful in the denoising processing of the input signals and by iteratively repeating the search so as to find a new noise model and to store the new noise model as a replacement for the previously stored noise mode or to retain the previously stored noise model according to the respective characteristics of the two models. The model is obtained by finding N frames whose energies are close to one another (ratio of energies lying between two values S and 1/S).

Claims

1. A process for automatically searching for noise models in noisy audio input signals, comprising:

digitizing the input signals;

processing the input signals based on an active noise model;

chopping the input signals into successive frames of P samples; and

searching for a new noise model in the input signals, by searching for N successive frames having expected characteristics of a noise, storing the N×

P corresponding samples so as to construct the new noise model useful in denoising the input signals, and iteratively repeating the search so as to find the new noise model and store the new noise model as a replacement for the active noise model or retain the active noise model according to characteristics of the active noise model and the new noise model,

wherein searching for the new noise model comprises,

searching for N successive frames whose energies are close to one another, N lying between a minimum value N1 and a maximum value N2,

calculating an average energy of the N successive frames, and

storing the N×

P samples in a guise of a new noise active model if a ratio between the average energy of the new noise model and the average energy of the frames of the active noise model previously stored is less than a determined replacement threshold.

2. The process according to claim 1, wherein searching for N successive frames comprises at least the following iterative steps:

calculating an energy of a current frame of rank n able to be appended to a model undergoing formulation already including n−

1 successive frames;

calculating a ratio between the energy of the current frame of rank n and an energy of a previous frame of rank n−

1;

comparing the calculated ratio with a low threshold less than 1 and a high threshold greater than 1; and

deciding whether to incorporate the frame of rank n into the model undergoing formulation based on a result of the compared calculated ratio.

3. The process according to claim 2, wherein searching for N successive frames further comprises:

calculating a ratio between an energy of a current frame and an energy of one or more other previous frames;

comparing the calculated ratio with the low and high thresholds; and

deciding whether to incorporate the current frame into the model undergoing formulation based on a result of the compared calculated ratio.

4. The process according to claim 2, wherein when the frame of rank n is incorporated into the model undergoing formulation:

n is incremented by one unit so as to continue the formulation of the model if n is less than N2, and

when n≧

N2, the formulation of the model undergoing formulation is halted, an average energy of the n frames is calculated, a ratio between the average energy of the n frames and an average energy of the frames of the actual stored noise model is calculated, the actual noise model is retained or is replaced by the model undergoing formulation according to a value of the ratio, and the iterative search for a new noise model is restarted.

5. The process according to claim 2, wherein when the current frame of rank n is not incorporated into the model undergoing formulation:

the formulation of the model of n−

1 frames is halted,

if n is greater than N1, a ratio between an average energy of the frames of the model undergoing formulation and the average energy of the frames of the actual stored noise model is calculated, and the actual stored noise model is retained or is replaced by the new noise model according to a value of the ratio, and

the iterative search for the new noise model is restarted.

6. The process according to claim 1, wherein

a search for a presence of speech is made in the input signals, and

searching for the new noise model is disabled if the presence of speech is detected.

7. The process according to claim 1, wherein

searching for the new noise model is periodically reinitialized by imposing the new noise model regardless of the respective characteristics of the new noise model and the active noise model.

8. The process according to claim 1, wherein

the noisy input signals are processed based on a found noise model, by spectral filtering, to eliminate as far as possible a noise corresponding to the found noise model.

9. The process according to claim 3, wherein when the frame of rank n is incorporated into the model undergoing formulation:

n is incremented by one unit so as to continue the formulation of the model if n is less than N2,

when n≧

N2, the formulation of the model is halted, an average energy of the n frames is calculated, the ratio between the energy of the n frames and the average energy of the frames of the actual stored noise model is retained or is replaced by the model undergoing formulation according to a value of the ratio, and the iterative search for the new noise model is restarted.

10. The process according to claim 3, wherein when the current frame of rank n is not incorporated into the model undergoing formulation:

the formulation of the model of n−

1 frames is halted,

if n is greater than N1, the ratio between the average energy of the frames of the model undergoing formulation and the average energy of the frames of the actual stored noise model is calculated, and the actual stored noise model is retained or is replaced by the new noise model according to the value of the ratio, and

the iterative search for the new model is restarted.

11. The process according to claim 2, wherein

a search for a presence of speech is made in the input signals, and

searching for the new noise model is disabled if the presence of speech is detected.

12. The process according to claim 2, wherein

searching for the new noise model is periodically reinitialized by imposing the new noise model regardless of the respective characteristics of the new noise model and of the active noise model.

13. The process according to claim 2, wherein

the noisy input signals are processed based on a found noise model, by spectral filtering, to eliminate as far as possible a noise corresponding to the found noise model.

14. The process according to claim 3, wherein

a search for a presence of speech is made in the input signals, and

searching for the new noise model is disabled if the presence of speech is detected.

15. The process according to claim 3, wherein

searching for the new noise model is periodically reinitialized by imposing the new noise model regardless of the respective characteristics of the new noise model and of the active noise model.

16. The process according to claim 3, wherein

the noisy input signals are processed based on a found noise model, by spectral filtering, to eliminate as far as possible a noise corresponding to the found noise model.