EVENT RECOGNITION

• US 20080215318A1
• Filed: 03/01/2007
• Published: 09/04/2008
• Est. Priority Date: 03/01/2007
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Kong, Yuan, Zhang, Zhengyou, Soong, Frank Kao-Ping K., Huang, Chao
• Application Number:
  US11/680,827
• Publication Number:
  US 20080215318A1
• Time in Patent Office: Days
• US Class Current: 704/231
• CPC Class Codes: G10L 17/26 Recognition of special voice characteristics, e.g. for use in lie detectors; Recognition of animal voices
• CPC Class Codes: G10L 25/48 specially adapted for particular use

Abstract

Recognition of events can be performed by accessing an audio signal having static and dynamic features. A value for the audio signal can be calculated by utilizing different weights for the static and dynamic features such that a frame of the audio signal can be associated with a particular event. A filter can also be used to aid in determining the event for the frame.

Claims

1. A method for detecting an event from an audio signal that includes static and dynamic features for a plurality of frames, comprising:

calculating at least one statistical value for each frame based on the static features and dynamic features, wherein a dynamic feature weight for the dynamic features is greater than a static feature weight for the static features;

associating an event identifier to each frame based on the at least one statistical value for each frame, the event identifier representing one event from a plurality of events;

applying a filter to each frame, the filter including a window of frames surrounding each frame to determine if the event identifier for each frame should be modified; and

providing an output of each event identifier for the plurality of frames.

2. The method of claim 1 and further comprising:

providing boundaries corresponding to a beginning and an end for identified events based on the event identifiers.

3. The method of claim 1 and further comprising:

applying the filter to each frame during a second pass to determine if the event identifier for each frame should be modified.

4. The method of claim 1 and further comprising:

combining the output of each frame with an event determination output from another input signal.

5. The method of claim 1 and further comprising:

forming a decision based on the event identification for a plurality of frames.

6. The method of claim 5 and further comprising:

providing the decision to an application and performing an action with the application based on the decision.

7. The method of claim 6 wherein the action includes updating a status identifier for the application.

8. A system for detecting an event from an audio signal that includes static and dynamic features for a plurality of frames, comprising:

an input layer for collecting the audio signal;

an event layer coupled to the input layer and adapted to;

receive the audio signal to calculate at least one statistical value for each frame based on the static features and dynamic features, wherein a dynamic feature weight for the dynamic features is greater than a static feature weight for the static features;

associate an event identifier to each frame based on the at least one statistical value for each frame, the event identifier representing one event from a plurality of events;

apply a filter to each frame, the filter including a window of frames surrounding each frame to determine if the event identifier for each frame should be modified; and

provide an output of each event identifier for the plurality of frames; and

a decision layer coupled to the event layer and adapted to perform a decision based on the output from the event layer.

9. The system of claim 8 wherein the event layer is further adapted to provide boundaries corresponding to a beginning and an end for identified events based on the event identifiers.

10. The system of claim 8 wherein the event layer is further adapted to apply the filter to each frame during a second pass to determine if the event identifier for each frame should be modified.

11. The system of claim 8 wherein the decision layer is further adapted to combine the output of each frame with an event determination output from another input signal.

12. The system of claim 8 wherein the decision layer is further adapted to provide the decision to an application and wherein the application is adapted to perform an action based on the decision.

13. The system of claim 12 wherein the action includes updating a status identifier for the application.

14. The system of claim 12 wherein the decision layer is further adapted to delay providing the decision to the application.

15. A method adjusting an event model used for detecting an event from an audio signal that includes static and dynamic features for a plurality of frames, comprising:

accessing the event model;

adjusting weights for the static and dynamic features such that a dynamic feature weight for the dynamic features is greater than a static feature weight for the static features using a plurality of training instances having audio signals representing events from a plurality of events;

adjusting a window size for a filter, the window size being a number of frames surrounding a frame to determine if the event identifier for each frame should be modified; and

providing an output of an adjusted event model for recognizing an event from an audio signal based on the dynamic feature weight, the static feature weight and the window size.

16. The method of claim 15 wherein the event model is further adapted to provide boundaries corresponding to a beginning and an end for identified events.

17. The method of claim 15 and further comprising:

determining a number of times to apply the filter to each frame to determine if the event identifier for each frame should be modified.

18. The method of claim 15 wherein the static features and dynamic features represent Mel-frequency cepstrum coefficients.

19. The method of claim 15 wherein the events include at least two of speech, phone ring, music and silence.

20. The method of claim 15 wherein the window size is adjusted based on the plurality of training instances.