RMS based video song sequence extraction using continuity rule from bollywood movies
Authors Name/s per 1st Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2-name of organization, acronyms acceptable line 3-City, Country line 4-e-mail address if desired
Authors Name/s per 2nd Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2-name of organization, acronyms acceptable line 3-City, Country line 4-e-mail address if desired
Abstract—A movie contains a portion like action, song, drama and conversation. There are so many applications based on video such as viewers want to see only video songs from movie, video on demand services, video segmentation, user entrainment, video song removal …show more content…
Once threshold is computed, we have chosen most continuous segment whose value higher than RMS value from the histogram of figure 2 and then define the thick portion of the signal and last it is extracted.
1.1 Segmentation of the movie
Suppose, n is total no. of segments, L is length of movie (in seconds) and T is time period of each segment (in seconds) than segmentation is defined as, (1) Movie is segmented into different segments. Each segment contains 10 seconds of that movie. Whole movie is divided in 10 seconds segments. Suppose movie is 2 Hours long, then movie is divided in 720 segments. For 3 Hours movie, it is divided in 1080 segments. And then store summation of audio channel data for each segment in new array.
1.2 RMS value based thresholding
Compute RMS value of whole movie and then this RMS value based thresholding is performed and its store in new array.
RMS (root mean square): The RMS value of a set of values is the square root of the arithmetic mean (average) of the squares of the original …show more content…
Other high picks represent background music with comedy scenes and actions. So, to extract video song sequences, we need to extract all those thick portions with their respective index values with the help of continuity rule.
1.3 Continuity rule based extraction
Continuity Rule: Count number of segments whether it is continuous from previous segment or not and greater than RMS value. Using this rule we can find continuity of segment from each other. It gives count number of continuous segments. As shown in figure 2, small portion is drawn is from movie DDLJ. On the X-axis number of segments is plotted .On the Y-axis audio channel data value is plotted. Threshold value is given in figure 3. Those segments whose values is greater than RMS it has to identified.
Take one example of movie 2: Suppose in movie DDLJ, after 49th segment, continuous segment found which is greater than RMS value. If count is 10 then starting point and ending point is defined. In movie DDLJ, the starting point and ending point is given in the table 1 below: Start point = T, End point = T + 10, Where, T=Segment, In Movie DDLJ example, we got 12
Authors Name/s per 1st Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2-name of organization, acronyms acceptable line 3-City, Country line 4-e-mail address if desired
Authors Name/s per 2nd Affiliation (Author) line 1 (of Affiliation): dept. name of organization line 2-name of organization, acronyms acceptable line 3-City, Country line 4-e-mail address if desired
Abstract—A movie contains a portion like action, song, drama and conversation. There are so many applications based on video such as viewers want to see only video songs from movie, video on demand services, video segmentation, user entrainment, video song removal …show more content…
Once threshold is computed, we have chosen most continuous segment whose value higher than RMS value from the histogram of figure 2 and then define the thick portion of the signal and last it is extracted.
1.1 Segmentation of the movie
Suppose, n is total no. of segments, L is length of movie (in seconds) and T is time period of each segment (in seconds) than segmentation is defined as, (1) Movie is segmented into different segments. Each segment contains 10 seconds of that movie. Whole movie is divided in 10 seconds segments. Suppose movie is 2 Hours long, then movie is divided in 720 segments. For 3 Hours movie, it is divided in 1080 segments. And then store summation of audio channel data for each segment in new array.
1.2 RMS value based thresholding
Compute RMS value of whole movie and then this RMS value based thresholding is performed and its store in new array.
RMS (root mean square): The RMS value of a set of values is the square root of the arithmetic mean (average) of the squares of the original …show more content…
Other high picks represent background music with comedy scenes and actions. So, to extract video song sequences, we need to extract all those thick portions with their respective index values with the help of continuity rule.
1.3 Continuity rule based extraction
Continuity Rule: Count number of segments whether it is continuous from previous segment or not and greater than RMS value. Using this rule we can find continuity of segment from each other. It gives count number of continuous segments. As shown in figure 2, small portion is drawn is from movie DDLJ. On the X-axis number of segments is plotted .On the Y-axis audio channel data value is plotted. Threshold value is given in figure 3. Those segments whose values is greater than RMS it has to identified.
Take one example of movie 2: Suppose in movie DDLJ, after 49th segment, continuous segment found which is greater than RMS value. If count is 10 then starting point and ending point is defined. In movie DDLJ, the starting point and ending point is given in the table 1 below: Start point = T, End point = T + 10, Where, T=Segment, In Movie DDLJ example, we got 12