Audio user interaction recognition and context refinement

• US 9,736,604 B2
• Filed: 11/12/2012
• Issued: 08/15/2017
• Est. Priority Date: 05/11/2012
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Shin, Jongwon, Kim, Lae-Hoon, Visser, Erik
• Primary Examiner(s): Jerez Lora, William A
• Application Number:
  US13/674,690
• Publication Number:
  US 20130301837A1
• Time in Patent Office: 1,737 Days
• Field of Search: 381 17, 381 56, 381 58, 381 91, 381 92, 381 26, 381 74, 381122, 381313, 348 1408, 348 1409, 367124, 367125
• CPC Class Codes: G01S 3/80 using ultrasonic, sonic or infrasonic waves
• CPC Class Codes: G10L 17/00 Speaker identification or verification techniques
• CPC Class Codes: G10L 2021/02166 Microphone arrays; Beamforming
• CPC Class Codes: G10L 21/00 Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility G10L19/00 takes precedence
• CPC Class Codes: G10L 25/06 the extracted parameters being correlation coefficients
• CPC Class Codes: G10L 25/48 specially adapted for particular use
• CPC Class Codes: H04L 65/403 Arrangements for multi-party communication, e.g. for conferences data switching systems for conference H04L12/18; arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities H04M3/56; television conferencing systems H04N7/15
• CPC Class Codes: H04N 7/15 Conference systems
• CPC Class Codes: H04R 1/406 microphones
• CPC Class Codes: H04R 2430/20 Processing of the output signals of the acoustic transducers of an array for obtaining a desired directivity characteristic H04R2203/12 takes precedence
• CPC Class Codes: H04R 2460/01 Hearing devices using active noise cancellation
• CPC Class Codes: H04R 2499/11 Transducers incorporated or for use in hand-held devices, e.g. mobile phones, PDA's, camera's
• CPC Class Codes: H04R 29/005 Microphone arrays
• CPC Class Codes: H04R 29/008 Visual indication of individual signal levels visual indication of stereophonic sound image H04S7/40
• CPC Class Codes: H04R 3/005 for combining the signals of two or more microphones specially adapted for hearing aids H04R25/407

Abstract

A system which tracks a social interaction between a plurality of participants, includes a fixed beamformer that is adapted to output a first spatially filtered output and configured to receive a plurality of second spatially filtered outputs from a plurality of steerable beamformers. Each steerable beamformer outputs a respective one of the second spatially filtered outputs associated with a different one of the participants. The system also includes a processor capable of determining a similarity between the first spatially filtered output and each of the second spatially filtered outputs. The processor determines the social interaction between the participants based on the similarity between the first spatially filtered output and each of the second spatially filtered outputs.

Claims

1. A system to track social interactions between a plurality of participants, comprising:

a fixed beamformer configured to;

receive a plurality of second spatially filtered beam outputs from a plurality of steerable beamformers, each steerable beamformer configured to output a respective one of the second spatially filtered beam outputs and associated with a different participant of the plurality of participants; and

generate a plurality of first spatially filtered beam outputs corresponding to a plurality of active speakers of the plurality of participants, the plurality of first spatially filtered beam outputs indicating a number of active speakers of the plurality of active speakers; and

a processor configured to;

determine similarities between the plurality of first spatially filtered beam outputs and the plurality of second spatially filtered beam outputs;

based on the similarities, output a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers;

based on the similarities, determine the social interactions between the plurality of participants; and

identify a participation status associated with each steerable beamformer based on the social interactions.

2. The system of claim 1, wherein the fixed beamformer comprises a fixed microphone array, and wherein each of the steerable beamformers comprises a steerable microphone array.

3. The system of claim 1, wherein the fixed beamformer and the processor are included within a mobile device configured to track the social interactions and display a representation of the social interactions between the plurality of participants.

4. The system of claim 1, wherein the fixed beamformer and the processor are included in at least one of a handset, a laptop, a tablet, a computer, or a netbook.

5. The system of claim 1, wherein each of the plurality of steerable beamformers is included within a respective device, and wherein each respective device is configured to be associated with a different one of the participants.

6. The system of claim 5, wherein each respective device comprises a headset worn by an associated participant.

7. The system of claim 1, further comprising a user interface configured to display information representative of the social interactions between the participants, the information including the participation status.

8. The system of claim 7, wherein a user interface display of the user interface is configured to graphically display representative indicators for all of the plurality of participants at once.

9. The system of claim 8, wherein the user interface display is configured to zoom in on one of the participants via the user interface.

10. The system of claim 1, further comprising a mobile device configured to track the social interactions and display a representation of the social interactions between the plurality of participants.

11. The system of claim 1, wherein the processor is configured to calculate a correlation between the plurality of first spatially filtered beam outputs of the fixed beamformer and a selected one of the second spatially filtered beam outputs of the steerable beamformers.

12. The system of claim 11, wherein the fixed beamformer is included within a first mobile device, and a selected steerable beamformer is included within a second mobile device that is different from the first mobile device.

13. The system of claim 1, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on at least one of linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

14. The system of claim 1, wherein the processor is further configured to determine a location of at least one of the participants.

15. A system to determine a similarity between an output of a fixed microphone array and outputs of a plurality of steerable microphone arrays, comprising:

a processor configured to;

receive first spatially filtered beam outputs from the fixed microphone array and second spatially filtered beam outputs from the steerable microphone arrays, wherein the first spatially filtered beam outputs are associated with a plurality of active speakers of a plurality of participants and the second spatially filtered beam outputs are associated with the plurality of participants, and wherein the first spatially filtered beam outputs indicate a number of active speakers of the plurality of participants; and

determine similarities between the first spatially filtered beam outputs and the second spatially filtered beam outputs; and

an output device that is configured to output, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers, wherein the output device is further configured to output, based on the similarities, social interactions between the plurality of participants.

16. The system of claim 15, wherein each spatially filtered beam output comprises an audio beam output.

17. The system of claim 15, wherein the processor is further configured to determine the similarities between the first spatially filtered beam outputs and the second spatially filtered beam outputs a plurality of times, once for each of the steerable microphone arrays.

18. The system of claim 15, wherein the processor and the output device are included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

19. The system of claim 15, wherein the processor is further configured to:

determine a first active speaker of the plurality of participants based on an estimated direction of signal arrival;

separate a spatially filtered beam output corresponding to the first active speaker from the output of the fixed microphone array using the estimated direction of signal arrival; and

determine second similarities between the outputs of the steerable microphone arrays and the output of the fixed microphone array based on the first spatially filtered beam outputs, the second spatially filtered beam outputs, and the separated spatially filtered beam output corresponding to the first active speaker.

20. The system of claim 19, wherein the estimated direction of signal arrival is estimated in three dimensions (3D).

21. The system of claim 15, wherein the second spatially filtered beam outputs correspond to look directions of the plurality of participants.

22. The system of claim 19, wherein the second spatially filtered beam outputs are generated by fixed broadside beamforming from active noise control (ANC) headsets.

23. The system of claim 15, wherein the second spatially filtered beam outputs of the steerable microphone arrays indicate at least one look direction of at least one active speaker, and wherein to determine the similarities, the processor is configured to:

for each active speaker of the at least one active speaker;

find a maximum peak of a cross-correlation equation based on a separated output of the fixed microphone array and a look direction of the active speaker; and

determine an angle of strong correlation associated with the maximum peak, wherein the angle of strong correlation corresponds to an angle between the fixed microphone array and the steerable microphone array of the active speaker.

24. The system of claim 15, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

25. A method for tracking social interactions between a plurality of participants, comprising:

receiving, from a fixed beamformer, a plurality of first spatially filtered beam outputs corresponding to a plurality of active speakers of a plurality of participants, the plurality of first spatially filtered beam outputs indicating a number of active speakers;

receiving, from a plurality of steerable beamformers, a plurality of second spatially filtered beam outputs, each steerable beamformer outputting a respective one of the second spatially filtered beam outputs and associated with a different one of the participants;

determining similarities between the plurality of first spatially filtered beam outputs and each of the plurality of second spatially filtered beam outputs;

determining, utilizing a processor, the social interactions between the participants based on the similarities;

identifying a participation status associated with each steerable beamformer based on the social interactions; and

outputting, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers.

26. The method of claim 25, wherein the fixed beamformer comprises a fixed microphone array, and wherein each of the steerable beamformers comprises a steerable microphone array.

27. The method of claim 25, wherein the fixed beamformer and the processor are included within a mobile device.

28. The method of claim 25, wherein the fixed beamformer and the processor are included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

29. The method of claim 25, wherein each of the plurality of steerable beamformers is included within a respective device, and wherein each respective device is associated with a different one of the participants.

30. The method of claim 29, wherein each respective device comprises a headset worn by an associated participant.

31. The method of claim 25, further comprising displaying an indication of the social interactions between the participants.

32. The method of claim 31, wherein the displaying further comprises graphically displaying information associated with the plurality of participants.

33. The method of claim 32, wherein the displaying further comprises zooming in on one of the participants via a user interface.

34. The method of claim 25, further comprising calculating a correlation between the plurality of first spatially filtered beam outputs of the fixed beamformer and a selected one of the second spatially filtered beam outputs of the steerable beamformers.

35. The method of claim 34, wherein the fixed beamformer is included within a first mobile device, and wherein a selected steerable beamformer is included within a second mobile device that is different from the first mobile device.

36. The method of claim 25, wherein the similarities are determined based on at least one of:

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Italcura-Saito distance.

37. The method of claim 25, further comprising determining a location of at least one of the participants.

38. A method for determining a similarity between an output of a fixed microphone array and outputs of a plurality of steerable microphone arrays, comprising:

receiving first spatially filtered beam outputs from the fixed microphone array and receiving second spatially filtered beam outputs from the steerable microphone arrays, wherein the first spatially filtered beam outputs are associated with a plurality of active speakers of a plurality of participants, and the second spatially filtered beam outputs are associated with the plurality of participants, and wherein the first spatially filtered beam outputs indicate a number of active speakers of the plurality of participants;

determining similarities between the first spatially filtered beam outputs of the fixed microphone array and the second spatially filtered beam outputs of the steerable microphone arrays;

determining, based on the similarities social interactions between the plurality of participants; and

outputting, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers.

39. The method of claim 38, wherein each spatially filtered beam output of the first spatially filtered beam outputs comprises a corresponding audio beam.

40. The method of claim 38, further comprising repeating receiving and determining a plurality of times, once for each of the steerable microphone arrays.

41. The method of claim 38, wherein the fixed microphone array is included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

42. The method of claim 38, further comprising:

estimating a direction of signal arrival at the fixed microphone array;

determining a first active speaker using the direction of signal arrival;

separating an output corresponding to the first active speaker from the output of the fixed microphone array using the direction of signal arrival; and

determining the similarity between the output of the steerable microphone array and the output of the fixed microphone array using the first spatially filtered beam outputs, the second spatially filtered beam outputs, and the output corresponding to the first active speaker.

43. The method of claim 42, wherein estimating the direction of signal arrival is estimated in three dimensions (3D).

44. The method of claim 42, wherein the second spatially filtered beam outputs correspond to look directions of the plurality of participants.

45. The method of claim 42, wherein the second spatially filtered beam outputs are generated by fixed broadside beamforming from an active noise control (ANC) headsets.

46. The method of claim 38, further comprising:

generating a separated speech signal for each active speaker of plurality of active speakers; and

generating a look direction of each active speaker,

wherein determining the similarities comprises, for each active speaker;

finding a maximum peak of a cross-correlation equation using the separated speech signal for the active speaker and the look direction of the active speaker; and

determining an angle of strong correlation for using the maximum peak, wherein the angle of strong correlation corresponds to an angle between the fixed microphone array and the steerable microphone array of the active speaker.

47. The method of claim 38, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

48. An apparatus to track social interactions between a plurality of participants, comprising:

means for generating a plurality of first spatially filtered beam outputs corresponding to a plurality of active speakers of a plurality of participants, the plurality of first spatially filtered beam outputs indicating a number of active speakers of the plurality of active speakers;

means for receiving a plurality of second spatially filtered beam outputs, each of the second spatially filtered beam outputs associated with a different participant of the plurality of participants;

means for determining similarities between the plurality of first spatially filtered beam outputs and each of the plurality of second spatially filtered beam outputs;

means for outputting, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers;

means for determining the social interactions between the plurality of participants based on the similarities; and

means for identifying a participation status associated with each steerable beamformer based on the social interactions.

49. The apparatus of claim 48, wherein each of the means are included within a mobile device.

50. The apparatus of claim 48, wherein each of the means are included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

51. The apparatus of claim 48, further comprising means for displaying an indication of the social interactions between the plurality of participants.

52. The apparatus of claim 51, further comprising means for graphically displaying the indication including information regarding all of the plurality of participants.

53. The apparatus of claim 52, further comprising means for zooming in on one of the participants.

54. The apparatus of claim 48, further comprising means for tracking the social interactions between the plurality of participants.

55. The apparatus of claim 48, further comprising means for calculating a correlation between the plurality of first spatially filtered beam outputs and a selected one of the second spatially filtered beam outputs.

56. The apparatus of claim 48, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

57. The apparatus of claim 48, further comprising means for determining a location of at least one of the participants.

58. An apparatus to determine a similarity between an output of a fixed microphone array and outputs of a plurality of steerable microphone arrays, comprising:

means for receiving first spatially filtered beam outputs from the fixed microphone array and second spatially filtered beam outputs from the steerable microphone arrays, wherein the first spatially filtered beam outputs are associated with a plurality of active speakers of a plurality of participants and the second spatially filtered beam outputs are associated with the plurality of participants, and wherein the first spatially filtered beam outputs indicate a number of active speakers of the plurality of participants;

means for performing a comparison between the first spatially filtered beam outputs and the second spatially filtered beam outputs at least one for each of the steerable microphone arrays;

means for determining, based on the comparison, similarities between the first spatially filtered beam outputs of the fixed microphone array and the second spatially filtered beam outputs of the steerable microphone array;

means for determining, based on the similarities, social interactions between the plurality of participants; and

means for outputting, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers.

59. The apparatus of claim 58, wherein each spatially filtered beam output of the second spatially filtered beam outputs comprises a corresponding audio beam.

60. The apparatus of claim 58, wherein the means for receiving is configured to receive the first spatially filtered beam outputs and the second spatially filtered beam outputs at least once for each of the steerable microphone arrays.

61. The apparatus of claim 58, wherein the means for receiving is included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

62. The apparatus of claim 58, further comprising:

means for recognizing a first active speaker using an estimated direction of signal arrival; and

means for identifying the first active speaker from the plurality of active speakers using the estimated direction of signal arrival.

63. The apparatus of claim 58, further comprising means for estimating a direction of signal arrival in three dimensions (3D).

64. The apparatus of claim 62, wherein the second spatially filtered beam outputs correspond to look directions.

65. The apparatus of claim 62, wherein the second spatially filtered beam outputs are generated by fixed broadside beamforming from active noise control (ANC) headsets.

66. The apparatus of claim 58, further comprising:

means for generating separated speech signals corresponding to the plurality of active speakers;

means for determining second similarities between the outputs of the steerable microphone arrays and the separated speech signals; and

means for providing a look direction of each of the plurality of active speakers, wherein the means for determining the second similarities comprises;

means for finding a maximum peak of a cross-correlation equation using a separated speech signal of a particular active speaker and the look direction of the particular active speaker; and

means for determining an angle of strong correlation based on the maximum peak, wherein the angle of strong correlation corresponds to an angle between the fixed microphone array and the steerable microphone array of the particular active speaker.

67. The apparatus of claim 58, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC)mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

68. A non-transitory computer-readable medium comprising computer-readable instructions for causing a processor to:

receive, from a plurality of steerable beamformers, a plurality of second spatially filtered beam outputs, each steerable beamformer outputting a respective one of the second spatially filtered beam outputs and each of the plurality of steerable beamformers associated with a different participant of a plurality of participants;

generate and output a plurality of first spatially filtered beam outputs corresponding to a plurality of active speakers of the plurality of participants, the plurality of first spatially filtered beam outputs indicating a number of active speakers of the plurality of active speakers;

determine similarities between the plurality of first spatially filtered beam outputs and each of the plurality of second spatially filtered beam outputs;

based on the similarities, output a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers;

determine, utilizing the processor, social interactions between the participants based on the similarities; and

identify a participation status associated with each steerable beamformer based on the social interactions.

69. The non-transitory computer-readable medium of claim 68, wherein a fixed beamformer comprises a fixed microphone array, and wherein each of the steerable beamformers comprises a steerable microphone array.

70. The non-transitory computer-readable medium of claim 68, wherein a fixed beamformer and the processor are included within a mobile device that is configured to track the social interactions and display a representation of the social interactions between the plurality of participants.

71. The non-transitory computer-readable medium of claim 68, wherein a fixed beamformer and the processor are included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

72. The non-transitory computer-readable medium of claim 68, wherein each of the plurality of steerable beamformers is included within a respective device, and wherein each respective device is capable of being associated with a different one of the participants.

73. The non-transitory computer-readable medium of claim 72, wherein each respective device comprises a headset wearable by an associated participant.

74. The non-transitory computer-readable medium of claim 68, further comprising instructions for causing the processor to display an indication of the social interactions between the participants.

75. The non-transitory computer-readable medium of claim 74, further comprising instructions for causing the processor to graphically display the indication including information regarding all of the plurality of participants.

76. The non-transitory computer-readable medium of claim 75, further comprising instructions for causing the processor to zoom in on one of the participants via a user interface.

77. The non-transitory computer-readable medium of claim 68, further comprising instructions for causing the processor to track the social interactions between the plurality of participants.

78. The non-transitory computer-readable medium of claim 68, further comprising instructions for causing the processor to calculate a correlation between the first spatially filtered beam outputs and a selected one of the second spatially filtered beam outputs of the steerable beamformers.

79. The non-transitory computer-readable medium of claim 78, wherein the first spatially filtered beam outputs are generated by a fixed beamformer that is included within a first mobile device, and wherein a selected steerable beamformer is included within a second mobile device that is different from the first mobile device.

80. The non-transitory computer-readable medium of claim 68, wherein the similarities are determined based on at least one of:

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.

81. The non-transitory computer-readable medium of claim 68, further comprising instructions for causing the processor to determine a location of at least one of the participants.

82. A non-transitory computer-readable medium comprising computer-readable instructions for causing a processor to:

receive first spatially filtered beam outputs from a fixed microphone array and second spatially filtered beam outputs from a plurality of steerable microphone arrays, wherein the first spatially filtered beam outputs are associated with a plurality of active speakers of a plurality of participants, and the second spatially filtered beam outputs are associated with the plurality of participants, and wherein the first spatially filtered beam outputs indicate a number of active speakers of the plurality of participants;

perform a comparison between the first spatially filtered beam outputs and the second spatially filtered beam outputs;

determine, based on the comparison, similarities between the first spatially filtered beam outputs of the fixed microphone array and the second spatially filtered beam outputs of the steerable microphone arrays;

determine, based on the similarities, social interactions between the plurality of participants; and

output, based on the similarities, a plurality of speaker identifiers (IDs), each speaker ID of the plurality of speaker IDs corresponding to a different active speaker of the plurality of active speakers.

83. The non-transitory computer-readable medium of claim 82, wherein each spatially filtered beam output comprises an audio beam.

84. The non-transitory computer-readable medium of claim 82, further comprising instructions for causing the processor to determine similarities between the first spatially filtered beam outputs of the fixed microphone array and the second spatially beam filtered outputs of the steerable microphone arrays a plurality of times, once for each of the steerable microphone arrays.

85. The non-transitory computer-readable medium of claim 82, wherein the processor is included within a device comprising at least one of a handset, a laptop, a tablet, a computer, or a netbook.

86. The non-transitory computer-readable medium of claim 82, further comprising instructions for causing the processor to:

determine a first active speaker using an estimated direction of signal arrival;

separate an output corresponding to the first active speaker from the output of the fixed microphone array using the estimated direction of signal arrival; and

determine second similarities between the first spatially beam filtered outputs, the second spatially filtered beam outputs, and the separated output of the first active speaker.

87. The non-transitory computer-readable medium of claim 82, further comprising instructions for causing the processor to estimate a direction of signal arrival in three dimensions (3D).

88. The non-transitory computer-readable medium of claim 86, wherein the second spatially filtered beam outputs correspond to look directions.

89. The non-transitory computer-readable medium of claim 86, wherein the second spatially filtered beam outputs are generated by fixed broadside beamforming from an active noise control (ANC) headsets.

90. The non-transitory computer-readable medium of claim 82, further comprising instructions for causing the processor to generate a look direction of each of the plurality of active speakers, wherein determining the similarities comprises, for each of the plurality of active speakers:

finding a maximum peak of a cross-correlation equation using a separated output for a particular active speaker of the plurality of active speakers and a look direction of the particular active speaker; and

determining an angle of strong correlation for using the maximum peak, wherein the angle of strong correlation corresponds to an angle between the fixed microphone array and a steerable microphone array of the particular active speaker.

91. The non-transitory computer-readable medium of claim 82, wherein the similarities are determined based on at least one of:

a correlation,

a least square fit with allowable time adjustment in a time domain or a frequency domain,

a feature based approach based on linear prediction coding (LPC), mel-frequency cepstral coefficients (MFCC), or cross-cumulant,

an empirical Kullback-Leibler divergence, or

an Itakura-Saito distance.