Measuring content coherence and measuring similarity

1. A method of measuring content coherence between a first audio section and a second audio section, comprising:

for each of audio segments in the first audio section,

determining a predetermined number of audio segments in the second audio section, wherein content similarity between the audio segment in the first audio section and the determined audio segments is higher than that between the audio segment in the first audio section and all the other audio segments in the second audio section; and

calculating an average of the content similarity between the audio segment in the first audio section and the determined audio segments; and

calculating first content coherence as an average, the minimum or the maximum of the averages calculated for the audio segments in the first audio section.

2. The method according to claim 1 , further comprising:

for each of the audio segments in the second audio section,

determining a predetermined number of audio segments in the first audio section, wherein content similarity between the audio segment in the second audio section and the determined audio segments is higher than that between the audio segment in the second audio section and all the other audio segments in the first audio section; and

calculating an average of the content similarity between the audio segment in the second audio section and the determined audio segments;

calculating second content coherence as an average, the minimum or the maximum of the averages calculated for the audio segments in the second audio section;

calculating symmetric content coherence based on the first content coherence and the second content coherence.

3. The method according to claim 1 , wherein each of the content similarity S(s i,l , s j,r ) between the audio segment s i,l in the first audio section and the determined audio segments s j,r is calculated as content similarity between sequence [s i,l , . . . , s i+L−1,l ] in the first audio section and sequence [s j,r , . . . , s j+L−1,r ] in the second audio section, L>1.

4. The method according to claim 3 , wherein the content similarity between the sequences is calculated by applying a dynamic time warping scheme or a dynamic programming scheme.

5. The method according to claim 1 , wherein the content similarity between two audio segments is calculated by

extracting first feature vectors from the audio segments;

generating statistical models for calculating the content similarity from the feature vectors; and

calculating the content similarity based on the generated statistical models,

wherein all the feature values in each of the first feature vectors are non-negative and the sum of the feature values is one, and the statistical models are based on Dirichlet distribution.

6. The method according to claim 5 , wherein the extracting comprises:

extracting second feature vectors from the audio segments; and

for each of the second feature vectors, calculating an amount for measuring a relation between the second feature vector and each of reference vectors, wherein all the amounts corresponding to the second feature vectors form one of the first feature vectors.

7. The method according to claim 6 , wherein the reference vectors are determined through one of the following methods:

random generating method where the reference vectors are randomly generated;

unsupervised clustering method where training vectors extracted from training samples are grouped into clusters and the reference vectors are calculated to represent the clusters respectively;

supervised modeling method where the reference vectors are manually defined and learned from the training vectors; and

eigen-decomposition method where the reference vectors are calculated as eigenvectors of a matrix with the training vectors as its rows.

8. An apparatus for measuring content coherence between a first audio section and a second audio section, comprising:

a similarity calculator which, for each of audio segments in the first audio section,

determines a predetermined number of audio segments in the second audio section, wherein content similarity between the audio segment in the first audio section and the determined audio segments is higher than that between the audio segment in the first audio section and all the other audio segments in the second audio section; and

calculates an average of the content similarity between the audio segment in the first audio section and the determined audio segments; and

a coherence calculator which calculates first content coherence as an average, the minimum or the maximum of the averages calculated for the audio segments in the first audio section.

9. The apparatus according to claim 8 , wherein the similarity calculator is further configured to, for each of the audio segments in the second audio section,

determine a predetermined number of audio segments in the first audio section, wherein content similarity between the audio segment in the second audio section and the determined audio segments is higher than that between the audio segment in the second audio section and all the other audio segments in the first audio section; and

calculate an average of the content similarity between the audio segment in the second audio section and the determined audio segments, and

wherein the coherence calculator is further configured to

calculate second content coherence as an average, the minimum or the maximum of the averages calculated for the audio segments in the second audio section, and

calculate symmetric content coherence based on the first content coherence and the second content coherence.

10. The apparatus according to claim 8 , wherein each of the content similarity S(s i,l , s j,r ) between the audio segment si,l in the first audio section and the determined audio segments s j,r is calculated as content similarity between sequence [s i,l , . . . , s i+L−1,l ] in the first audio section and sequence [s j,r , . . . , s j +L−1,r ] in the second audio section, L>1.

11. The apparatus according to claim 10 , wherein the content similarity between the sequences is calculated by applying a dynamic time warping scheme or a dynamic programming scheme.

12. The apparatus according to claim 8 , wherein the similarity calculator comprises:

a feature generator which, for each of the content similarity, extracts first feature vectors from the associated audio segments;

a model generator which generates statistical models for calculating each of the content similarity from the feature vectors; and

a similarity calculating unit which calculates the content similarity based on the generated statistical models,

wherein all the feature values in each of the first feature vectors are non-negative and the sum of the feature values is one, and the statistical models are based on Dirichlet distribution.

13. A method of measuring content similarity between two audio segments, comprising:

extracting first feature vectors from the audio segments, wherein all the feature values in each of the first feature vectors are non-negative and normalized so that the sum of the feature values is one;

generating statistical models for calculating the content similarity based on Dirichlet distribution from the feature vectors; and

calculating the content similarity based on the generated statistical models.

14. The method according to claim 13 , wherein the extracting comprises:

extracting second feature vectors from the audio segments; and

for each of the second feature vectors, calculating an amount for measuring a relation between the second feature vector and each of reference vectors, wherein all the amounts corresponding to the second feature vectors form one of the first feature vectors.

15. The method according to claim 14 , wherein the reference vectors are determined through one of the following methods:

random generating method where the reference vectors are randomly generated;

unsupervised clustering method where training vectors extracted from training samples are grouped into clusters and the reference vectors are calculated to represent the clusters respectively;

supervised modeling method where in the reference vectors are manually defined and learned from the training vectors; and

eigen-decomposition method where the reference vectors are calculated as eigenvectors of a matrix with the training vectors as its rows.

16. The method according to claim 14 , wherein the relation between the second feature vectors and each of the reference vectors is measured by one of the following amounts:

distance between the second feature vector and the reference vector;

correlation between the second feature vector and the reference vector;

inter product between the second feature vector and the reference vector; and

posterior probability of the reference vector with the second feature vector as the relevant evidence.

17. An apparatus for measuring content similarity between two audio segments, comprising:

a feature generator which extracts first feature vectors from the audio segments, wherein all the feature values in each of the first feature vectors are non-negative and normalized so that the sum of the feature values is one;

a model generator which generates statistical models for calculating the content similarity based on Dirichlet distribution from the feature vectors; and

a similarity calculator which calculates the content similarity based on the generated statistical models.

18. The apparatus according to claim 17 , wherein the feature generator is further configured to

extract second feature vectors from the audio segments; and

for each of the second feature vectors, calculate an amount for measuring a relation between the second feature vector and each of reference vectors, wherein all the amounts corresponding to the second feature vectors form one of the first feature vectors.

19. The apparatus according to claim 18 , wherein the reference vectors are determined through one of the following methods:

random generating method where the reference vectors are randomly generated;

unsupervised clustering method where training vectors extracted from training samples are grouped into clusters and the reference vectors are calculated to represent the clusters respectively;

supervised modeling method where in the reference vectors are manually defined and learned from the training vectors; and

eigen-decomposition method where the reference vectors are calculated as eigenvectors of a matrix with the training vectors as its rows.

20. The apparatus according to claim 18 , wherein the relation between the second feature vectors and each of the reference vectors is measured by one of the following amounts:

distance between the second feature vector and the reference vector;

correlation between the second feature vector and the reference vector;

inter product between the second feature vector and the reference vector; and

posterior probability of the reference vector with the second feature vector as the relevant evidence.