IP Library Granted Patent US 8,612,222
Granted Patent B2
US 8,612,222 · App. 13/601,314 · Granted Dec 17, 2013

Signature noise removal

Inventors: Phillip A. Hetherington (Vancouver, CA); Shreyas A. Paranjpe (Vancouver, CA)
Assignee: QNX Software Systems Limited
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Quick Facts
Patent No.
US 8,612,222
App. No.
13/601,314
Filed
Aug 31, 2012
Granted
Dec 17, 2013
Kind
B2
Examiner
HE, JIALONG
Art Unit
2659
USPC
704/233
Abstract

A speech enhancement system improves the perceptual quality of a processed voice signal. The system improves the perceptual quality of a voice signal by removing unwanted noise components from a voice signal. The system removes undesirable signals that may result in the loss of information. The system receives and analyzes signals to determine whether an undesired random or persistent signal corresponds to one or more modeled noises. When one or more noise components are detected, the noise components are substantially removed or dampened from the signal to provide a less noisy voice signal.

Claims (35)

1. A noise detection system, comprising:

a computer memory that stores a noise model that includes spectral and temporal shape characteristics of a noise; and

a processor coupled with the computer memory;

where the processor is configured to access the noise model from the computer memory and analyze a signal to determine whether characteristics of the signal correspond to characteristics of the noise model;

where the processor is configured to fit the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal;

where the processor is configured to identify the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; and

where the processor is configured to model individual sound events that make up the noise of the noise model, and model a temporal space between the individual sound events.

2. The noise detection system of claim 1 , where the processor is configured to attenuate the sound event of the signal in response to the processor identifying the sound event as the noise event.

3. The noise detection system of claim 2 , where the processor is configured to add the noise model to a recorded or modeled continuous noise for use to attenuate the sound event.

4. The noise detection system of claim 1 , where the processor is configured to model temporal and spectral noise characteristics in response to detecting noise.

5. The noise detection system of claim 1 , where the noise model comprises a dynamic model, and where the processor is configured to change the dynamic model in response to detection of changing conditions in the signal.

6. The noise detection system of claim 1 , where the computer memory stores a plurality of noise models, and where the processor is configured to combine the plurality of noise models to detect or attenuate a noise in the signal.

7. A noise detection method, comprising:

accessing, by a processor, a computer memory that stores a noise model that includes spectral and temporal shape characteristics of a noise;

analyzing a signal to determine whether characteristics of the signal correspond to characteristics of the noise model;

fitting, by the processor, the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal;

identifying, by the processor, the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event; and

modeling individual sound events that make up the noise of the noise model; and

modeling a temporal space between the individual sound events.

8. The noise detection method of claim 7 , further comprising attenuating the sound event of the signal in response to the processor identifying the sound event as the noise event.

9. The noise detection method of claim 7 , further comprising adding the noise model to a recorded or modeled continuous noise for use to attenuate the sound event.

10. The noise detection method of claim 7 , further comprising modeling temporal and spectral noise characteristics in response to detecting noise.

11. The noise detection method of claim 7 , where the noise model comprises a dynamic model, the method further comprising changing the dynamic model in response to detection of changing conditions in the signal.

12. The noise detection method of claim 7 , where the computer memory stores a plurality of noise models, the method further comprising combining the plurality of noise models to detect or attenuate a noise in the signal.

13. A non-transitory computer-readable medium with instructions stored thereon, where the instructions are executable by a processor to cause the processor to perform the steps of:

accessing a noise model that includes spectral and temporal shape characteristics of a noise;

analyzing a signal to determine whether characteristics of the signal correspond to characteristics of the noise model;

fitting the noise model to the signal in a time-frequency domain to evaluate spectral and temporal shape characteristics of a sound event in the signal;

identifying the sound event as a noise event based on a correlation between the noise model and a signal envelope of the sound event;

modeling individual sound events that make up the noise of the noise model; and

modeling a temporal space between the individual sound events.

14. The non-transitory computer-readable medium of claim 13 , where the instructions are executable by the processor to cause the processor to perform the step of attenuating the sound event of the signal in response to the processor identifying the sound event as the noise event.

15. The non-transitory computer-readable medium of claim 13 , where the instructions are executable by the processor to cause the processor to perform the step of modeling temporal and spectral noise characteristics in response to detecting noise.

16. The non-transitory computer-readable medium of claim 13 , where the noise model comprises a dynamic model, where the instructions are executable by the processor to cause the processor to perform the step of changing the dynamic model in response to detection of changing conditions in the signal.

17. The non-transitory computer-readable medium of claim 13 , where the instructions are executable by the processor to cause the processor to perform the step of combining a plurality of noise models to detect or attenuate a noise in the signal.

Assignments (12)
CORRECTIVE ASSIGNMENT TO CORRECT THE ADDED PATENT NUMBER TO REMOVE PATENT NO. 8,873,407 AT PREVIOUSLY RECORDED ON REEL 64066 FRAME 1. ASSIGNOR(S) HEREBY CONFIRMS THE NUNC PRO TUNC ASSIGNMENT EFFECTIVE DATE MARCH 20, 2023. Recorded Feb 2, 2026
From: BLACKBERRY LIMITED
To: MALIKIE INNOVATIONS LIMITED
Reel/Frame 074921/0001 →
CORRECTIVE ASSIGNMENT TO CORRECT 12817157 APPLICATION NUMBER PREVIOUSLY RECORDED AT REEL: 064015 FRAME: 0001. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Sep 5, 2023
From: OT PATENT ESCROW, LLC
To: MALIKIE INNOVATIONS LIMITED
Reel/Frame 064807/0001 →
CORRECTIVE ASSIGNMENT TO CORRECT THE COVER SHEET AT PAGE 50 TO REMOVE 12817157 PREVIOUSLY RECORDED ON REEL 063471 FRAME 0474. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Sep 5, 2023
From: BLACKBERRY LIMITED
To: OT PATENT ESCROW, LLC
Reel/Frame 064806/0669 →
NUNC PRO TUNC ASSIGNMENT Recorded Jun 19, 2023
From: BLACKBERRY LIMITED
To: MALIKIE INNOVATIONS LIMITED
Reel/Frame 064066/0001 →
NUNC PRO TUNC ASSIGNMENT Recorded Jun 16, 2023
From: OT PATENT ESCROW, LLC
To: MALIKIE INNOVATIONS LIMITED
Reel/Frame 064015/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 27, 2023
From: BLACKBERRY LIMITED
To: OT PATENT ESCROW, LLC
Reel/Frame 063471/0474 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2020
From: 2236008 ONTARIO INC.
To: BLACKBERRY LIMITED
Reel/Frame 053313/0315 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 4, 2014
From: 8758271 CANADA INC.
To: 2236008 ONTARIO INC.
Reel/Frame 032607/0674 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 4, 2014
From: QNX SOFTWARE SYSTEMS LIMITED
To: 8758271 CANADA INC.
Reel/Frame 032607/0943 →
CONFIRMATORY ASSIGNMENT Recorded Sep 7, 2012
From: QNX SOFTWARE SYSTEMS (WAVEMAKERS), INC.
To: QNX SOFTWARE SYSTEMS CO.
Reel/Frame 028921/0545 →
CHANGE OF NAME Recorded Sep 7, 2012
From: QNX SOFTWARE SYSTEMS CO.
To: QNX SOFTWARE SYSTEMS LIMITED
Reel/Frame 028918/0160 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 7, 2012
From: HETHERINGTON, PHILLIP A.; PARANJPE, SHREYAS A.
To: QNX SOFTWARE SYSTEMS (WAVEMAKERS), INC.
Reel/Frame 028917/0975 →
Continuity (8)
Continuation 11607340 · Nov 30, 2006
Continuation In Part 11331806 · Jan 13, 2006
Continuation In Part 11252160 · Oct 17, 2005
Continuation In Part 10688802 · Oct 16, 2003
Continuation In Part 10410736 · Apr 10, 2003
Continuation In Part 11006935 · Dec 8, 2004
Provisional Application 60449511 · Feb 21, 2003
Related Publication 20120321095A1 · Dec 20, 2012