Method And Apparatus For Fast Audio Search

• US 20110184952A1
• Filed: 02/01/2011
• Published: 07/28/2011
• Est. Priority Date: 07/03/2006
• Status: Abandoned Application

• Current Assignee: Yurong Chen
• Original Assignee: Yurong Chen
• Inventors: Chen, Yurong
• Application Number:
  US13/018,635
• Publication Number:
  US 20110184952A1
• Time in Patent Office: Days
• US Class Current: 707/737
• CPC Class Codes: G06F 16/634 Query by example, e.g. query by humming
• CPC Class Codes: G06F 16/683 using metadata automatically derived from the content

Abstract

According to embodiments of the subject matter disclosed in this application, a large audio database in a multiprocessor system may be searched for a target audio clip using a robust and parallel search method. The large audio database may be partitioned into a number of smaller groups, which are dynamically scheduled to available processors in the system. Processors may process the scheduled groups in parallel by partitioning each group into smaller segments, extracting acoustic features from the segments; and modeling the segments using a common component Gaussian Mixture model (“CCGMM”). One processor may also extract acoustic features from the target audio clip and model it using the CCGMM. Kullback-Leibler (KL) distance may be further computed between the target audio clip and each segment. Based on the KL distance, a segment may be determined to match the target audio clip; and/or a number of following segments may be skipped.

Claims

1. A method comprising:

parallel processing audio segments, including first and second audio segments, to search for a target audio clip;

determining a target model for the target clip and first and second segment models respectively for the first and second segments;

determining first and second distances respectively between the target model and the first and second segment models; and

skipping searching a number of audio segments based on the first distance, and determining the second segment matches the target clip based on the second distance.

2. The method of claim 1, wherein the magnitude of the number of audio segments is based on the magnitude of the first distance.

3. The method of claim 1, wherein (a) determining the target model comprises extracting a target feature vector sequence (“

FVS”

) from the target clip and modeling the target FVS based on a Gaussian Mixture model (“

GMM”

), and (b) determining the first segment model comprises extracting a first segment FVS from the first segment and modeling the first segment FVS based on a GMM.

4. The method of claim 1, wherein the first segment partially overlaps a third segment.

5. The method of claim 1, wherein the first segment partially overlaps one of the number of audio segments.

6. The method of claim 1, including:

partitioning an audio database into the first and second segments; and

determining first and second sizes for the first and second segments, the first and second sizes being determined to reduce the amount of overlapped computation among the audio segments and load imbalance in parallel processing of the audio segments.

7. The method of claim 1, including determining the first segment does not match the target clip based on the first distance satisfying a first threshold;

wherein (a) the first and second audio segments are each partitioned from an audio database, and (b) the number of audio segments exceeds 1.

8. An article comprising a machine-readable medium that contains instructions, which when executed by a processing platform, cause the processing platform to perform operations comprising:

parallel processing audio segments, including first and second audio segments, to search for a target audio clip;

determining a target model for the target clip and first and second segment models respectively for the first and second segments;

determining first and second similarity measures respectively between the target model and the first and second segment models; and

skipping searching a number of audio segments based on the first similarity measure, and determining the second segment matches the target clip based on the second similarity measure.

9. The article of claim 8, wherein (a) the first similarity measure includes a first distance, and (b) the magnitude of the number of audio segments is based on the magnitude of the first distance.

10. The article of claim 8, wherein (a) determining the target model comprises extracting a target feature vector sequence (“

FVS”

) from the target clip and modeling the target FVS based on a Gaussian Mixture model (“

GMM”

), and (b) determining the first segment model comprises extracting a first segment FVS from the first segment and modeling the first segment FVS based on a GMM.

11. The article of claim 8, wherein the first segment partially overlaps a third segment.

12. The article of claim 8, wherein the first segment partially overlaps one of the number of audio segments.

13. The article of claim 8, including:

partitioning an audio database into the first and second segments; and

determining first and second sizes for the first and second segments, the first and second sizes being determined to reduce the amount of overlapped computation among the audio segments and load imbalance in parallel processing of the audio segments.

14. The article of claim 8, including determining the first segment does not match the target clip based on the first similarity measure satisfying a first threshold;

wherein (a) the first and second audio segments are each partitioned from an audio database, and (b) the number of audio segments exceeds 1.

15. An apparatus comprising:

a memory to receive audio segments; and

a plurality of processor cores, coupled to the memory, to;

(a) parallel process the audio segments, including first and second audio segments, to search for a target audio clip;

(b) determine a target model for the target clip and first and second segment models respectively for the first and second segments;

(c) determine first and second similarity measures respectively between the target model and the first and second segment models; and

(d) determine the second segment matches the target clip based on the second similarity measure;

wherein the second segment partially overlaps a third segment.

16. The apparatus of claim 15, wherein the plurality of processor cores are to skip searching a number of audio segments based on the first similarity measure.

17. The apparatus of claim 16, wherein (a) the first similarity measure includes a first distance, and (b) the magnitude of the number of audio segments is based on the magnitude of the first distance.

18. The apparatus of claim 16, wherein (a) determining the target model comprises extracting a target feature vector sequence (“

FVS”

) from the target clip and modeling the target FVS based on a Gaussian Mixture model (“

GMM”

), and (b) determining the first segment model comprises extracting a first segment FVS from the first segment and modeling the first segment FVS based on a GMM.

19. The apparatus of claim 16, wherein the plurality of processor cores are to:

partition an audio database into the first and second segments; and

determine first and second sizes for the first and second segments, the first and second sizes being determined to reduce the amount of overlapped computation among the audio segments and load imbalance in parallel processing of the audio segments.

20. The apparatus of claim 16, wherein the plurality of processor cores are to determine the first segment does not match the target clip based on the first similarity measure satisfying a first threshold;

wherein (a) the first and second audio segments are each partitioned from an audio database, (b) the number of audio segments exceeds 1, and (c) the plurality of processor cores are included in a plurality of processors.

21. The apparatus of claim 15, wherein the first segment partially overlaps one of the number of audio segments.