Method of DJ commentary analysis for indexing and search

• US 20100146009A1
• Filed: 12/05/2008
• Published: 06/10/2010
• Est. Priority Date: 12/05/2008
• Status: Abandoned Application

• Current Assignee: Lemi Technology, LLC (Concert Technology Corporation)
• Original Assignee: Concert Technology Corporation
• Inventors: Issa, Alfredo, Kandekar, Kunal, Evans, Greg
• Application Number:
  US12/314,193
• Publication Number:
  US 20100146009A1
• Time in Patent Office: Days
• US Class Current: 707/803
• CPC Class Codes: G06F 16/68 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/685 using automatically derived transcript of audio data, e.g. lyrics speech recognition G10L15/00

Abstract

A method of conducting a disc jockey (DJ) commentary analysis for indexing and search is provided. More specifically, a method is provided for automatically generating metadata related to commentary of media segments to enable tagging, storing and context relevant searching. Speech-to-text conversion technology and audio/video analysis are used to generate content and metadata. Subject matter is then identified and filtered to a predetermined set of subjects. Metadata tags and context profiles for the media segments are generated to index the media segments. Moreover, context information of the user is used to generate a context profile of the user in a format similar to that of the media segment. Indexed media segments are searched to match with the user context profile and a relevant media segment is presented to the user.

Claims

1. A method of generating metadata for disc jockey (DJ) commentary media segments to enable contextually relevant searches, the method comprising:

generating data including using at least one of speech-to-text conversion or audio/video analysis;

analyzing the generated data to extract subject matters;

filtering the extracted subject matters such that they only refer to a pre-determined set of subjects;

accepting any other contextual information;

generating metadata tags for each of the media segments using the predetermined set of subjects referenced during the filtering step;

generating a context profile for each of the media segments using the metadata tags and the other contextual information; and

indexing the media segments using at least one of the metadata tags or the context profile.

2. The method of claim 1, wherein the predetermined set of subjects of the filtering step includes at least one of:

media content, artist or category, events and conditions, time, location, or opinions.

3. The method of claim 1, further comprising:

receiving user context information, including time, location and interests;

building a context profile from the received user context information in the same format as the metadata tag generating step;

finding one or more commentary media segments by searching the index constructed by the metadata tag generating step using a profile of the extracted subject matters of the analyzing step; and

identifying a most relevant commentary media segment by determining that the most relevant commentary media segment'"'"'s profile most matches the profile of the analyzing step.

4. The method of claim 1, wherein prior to the analyzing step, further comprising assigning a tone to the media segment based on at least one of voice-recognition or laughter detection.

5. The method of claim 1, wherein prior to the analyzing step, further comprising categorizing a voice of the media segment.

6. The method of claim 1, wherein the extracted subject matters of the analyzing step are selected from at least one of semantic analysis, keyword analysis, or natural language processing.

7. The method of claim 1, wherein the context profile is in an extensible markup language (XML) format.

8. The method of claim 1, further comprising discarding media segments that are unsuitable for re-use by checking if their context is too narrow.

9. The method of claim 8, wherein in the step of discarding media segments, the context checking is carried out using at least one of heuristics, keyword filtering using pre-configured keywords, pre-configured rules that operate on the metadata tags.

10. The method of claim 3, wherein after identifying the most relevant commentary media segment, presenting the most relevant commentary media segment to a user'"'"'s mobile device.

11. The method of claim 1, wherein the step of generating data includes using both textual data and speech data.

12. The method of claim 1, further comprising:

receiving user context information, including time, location and interests;

finding one or more commentary media segments by searching the index constructed by the indexing step;

identifying a most relevant commentary media segment by determining that the most relevant commentary media segment'"'"'s profile matches most closely to the context profile of the indexing step; and

presenting the most relevant commentary media segment to a user'"'"'s mobile device.

13. A system for generating metadata for disc jockey (DJ) commentary media segments to enable contextually relevant searches, comprising:

means for generating data including using at least one of speech-to-text conversion or audio/video analysis;

means for analyzing the generated data to extract subject matters;

means for filtering the extracted subject matters such that they only refer to a pre-determined set of subjects;

means for accepting any other contextual information;

means for generating metadata tags for each of the media segments using the predetermined set of subjects referenced by the filtering means;

means for generating a context profile for each of the media segments using the metadata tags and the other contextual information; and

means for indexing the media segments using at least one of the metadata tags or the context profile.

14. The system of claim 13, wherein the predetermined set of subjects of the filtering means includes at least one of:

media content, artist or category, events and conditions, time, location, or opinions.

15. The system of claim 13, further comprising:

means for receiving user context information, including time, location and interests;

means for building a context profile from the received user context information in the same format as the metadata tag generation;

means for finding one or more commentary media segments by searching the index constructed by the metadata tag generation using a profile of the extracted subject matters of the analyzing means; and

means for identifying a most relevant commentary media segment by determining that the most relevant commentary media segment'"'"'s profile most matches the profile of the analyzing means.

16. The system of claim 13, wherein prior to the analysis of the analyzing means, further comprising means for assigning a tone to the media segment based on at least one of voice-recognition or laughter detection.

17. The system of claim 13, wherein prior to the analysis of the analyzing means, further comprising means for categorizing a voice of the media segment.

18. The system of claim 13, wherein the extracted subject matters of the analyzing means are selected from at least one of semantic analysis, keyword analysis, or natural language processing.

19. The system of claim 13, wherein the context profile is in an extensible markup language (XML) format.

20. The system of claim 13, further comprising means for discarding media segments that are unsuitable for re-use by checking if their context is too narrow.

21. The system of claim 20, wherein the discarding means carries out the context checking using at least one of heuristics, keyword filtering using pre-configured keywords, pre-configured rules that operate on the metadata tags.

22. The system of claim 15, wherein after the means for identifying has identified the most relevant media segment, the system further comprises means for presenting the most relevant commentary media segment to a user'"'"'s mobile device.

23. The system of claim 13, wherein the means for generating data includes using both textual data and speech data.

24. A computer readable medium comprising a program for instructing a system to:

generate data including using at least one of speech-to-text conversion or audio/video analysis;

analyze the generated data to extract subject matters;

filter the extracted subject matters such that they only refer to a pre-determined set of subjects;

accept any other contextual information;

generate metadata tags for each of the media segments using the predetermined set of subjects referenced during the filtering operation;

generate a context profile for each of the media segments using the metadata tags and the other contextual information; and

index the media segments using at least one of the metadata tags or the context profile.

25. The computer readable medium of claim 24, wherein the predetermined set of subjects of the filtering operation includes at least one of:

media content, artist or category, events and conditions, time, location, or opinions.

26. The computer readable medium of claim 24, wherein the program further instructs the system to:

receive user context information, including time, location and interests;

build a context profile from the received user context information in the same format as the metadata tag generation;

find one or more commentary media segments by searching the index constructed by the metadata tag generation using a profile of the extracted subject matters of the analysis operation; and

identify a most relevant commentary media segment by determining that the most relevant commentary media segment'"'"'s profile most matches the profile of the analysis operation.

27. The computer readable medium of claim 24, wherein prior to the analysis operation, the program is further operative to instruct the system to assign a tone to the media segment based on at least one of voice-recognition or laughter detection.

28. The computer readable medium of claim 24, wherein prior to the analysis operation, the program is further operative to instruct the system to categorize a voice of the media segment.

29. The computer readable medium of claim 24, wherein the extracted subject matters of the analysis operation are selected from at least one of semantic analysis, keyword analysis, or natural language processing.

30. The computer readable medium of claim 24, wherein the context profile is in an extensible markup language (XML) format.

31. The computer readable medium of claim 24, wherein the program is further operative to instruct the system to discard media segments that are unsuitable for re-use by checking if their context is too narrow.

32. The computer readable medium of claim 31, wherein in the operation of discarding media segments, the context checking is carried out using at least one of heuristics, keyword filtering using pre-configured keywords, pre-configured rules that operate on the metadata tags.

33. The computer readable medium of claim 26, wherein after the operation of identifying the most relevant media segment, the program instructs the system to present the most relevant commentary media segment to a user'"'"'s mobile device.

34. The computer readable medium of claim 24, wherein the operation of generating data includes using both textual data and speech data.

35. The computer readable medium of claim 24, wherein the program further instructs the system to:

receive user context information, including time, location and interests;

find one or more commentary media segments by searching the index constructed by the indexing operation;

identify a most relevant commentary media segment by determining that the most relevant commentary media segment'"'"'s profile matches most closely to the context profile of the indexing operation; and

present the most relevant commentary media segment to a user'"'"'s mobile device.