Automatic Musical Video Creation with Media Analysis

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Automatic Musical Video Creation with Media
Analysis

• 2004/02/16
• Student Chen-Hsiu Huang
• Advisor Prof. Ja-Ling Wu

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Outline

• Problem Formulation
• Current Solutions
• Our Goal
• Gory Details
• Performance Evaluation
• Whats Next?
• Questions and Discussion

3
Problem Formulation

• The digital video capture devices such as DVs are
  made more affordable for end users.
• Its interesting to shoot videos but frustrating
  for editing them.
• Theres still a tremendous barrier between
  amateurs (home users) and the powerful video
  editing software.
• Finally people leave their precious shots in
  piles of DV tapes without editing and management.

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• According to a survey on DVworld, the relations
  between the video length and how many times will
  user review them after days
• Video clips with no more then 5 minutes are best
  for humans concentration.

http//www.DVworld.com.tw/
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Facts about Musical Video

• People are inpatient for videos without scenario
  or voice-over, especially for those with no
  music.
• The improved soundtrack quality improved
  perceived video image quality.
• Synchronizing video and audio segments enhance
  the perception of both.
• One study at MIT showed that listener judge the
  identical video image to be higher quality when
  accompanied by higher-fidelity audio.

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• Home videos can be roughly classified by its
  nature property.

• Four profiles are proposed to deal with videos of
  different nature.

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Current Solutions

• A consumer product called muvee autoProducer
  has been announced to ease the burden of
  professional video editing.
• Its application scenario is quite simple

Pick-up your video
Select profiles to apply
Produce a quality musical video
Choose your favorite music
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Our Goal

• Although there are commercial products in the
  market, only few academic publications related.
• Jonathan Foote, Matthew D. Cooper, Andreas
  Girgensohn, "Creating music videos using
  automatic media analysis," ACM Multimedia 2002
  553-560
• The content-analysis technologies are developed
  for years can we adopt those technologies to
  help auto-creation of musical videos?
• Goal To achieve the near or beyond quality in
  the similar application scenario with the
  content-analysis technologies developed in
  multimedia domain.

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Volume ZCR Brightness Bandwidth
Proposed Framework
Audio segment cutting
Shot change Scene change
Human face Flash light Motion strength Color
variance Camera Operation ...
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Audio Analysis

• We should cut the input audio into several clips
  according to its audio features.
• Frame-level features
• Volume defined as the MSR of audio samples
• ZCR the number of times that the audio waveform
  crosses the zero axis in each frame.
• Spectral features
• Brightness the centroid of frequency spectrum
• Bandwidth the standard deviation of frequency
  spectrum

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• Generally the brightness distribution curve is
  almost the same as ZCR curve, so here we use ZCR
  feature only.
• Bandwidth is an important audio feature but we
  can not easily tell whats the real physical
  meaning in music when the bandwidth reaches its
  high/low value.
• Furthermore, the relations between musical
  perceptual and bandwidth values are not clear and
  not regular.

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Audio Segmentation

• First we cut the input audio into clips when the
  volume changes dramatically.
• For each clip, we define the burst of ZCR as an
  attack, which may be a beat of base drum or the
  singers voice.

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• The dramatic volume change defines the audio clip
  boundary, while the burst of ZCR (attack) in each
  clip defines the granular sub-segment within it.

?????

• Here we define the dynamic of each clip as

? The dynamic feature can be used as a good
reference later for video/audio synchronization
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Video Analysis

• First we need to apply shot change detection to
  segment video into scenes.
• Here we use the combination of pixel MAD and
  pixel histogram method to perform the shot change
  detection.

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• Flashlight detection
• The flashlight event will be detected as shot
  change.
• When the shot change is founded, check if
• If so, then its a flashlight event, should not
  be treated as shot change.
• Sub-Shot segmentation
• Here we use MPEG-7 ColorLayout descriptor to
  measure each frames similarity.
• The first frame in each shot is selected as the
  basis, each consecutive frames are compared with
  the basis. If
• Then we say that in frame i, a sub-shot is
  occurred.

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Camera Operation

• Camera operations such as pan or zoom are widely
  used in amateur home videos. By detection those
  camera operations can help catch the video
  takers intention.
• Our camera operation detection is performed base
  on the MPEG videos motion vectors in P-frames.

Pan
Zoom

• This method is simple and efficient. However, it
  does well when detecting camera operations.

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Video Features

• Frame-level features
• The presence of human faces.
• Use OpenCV library as face detection module.
• Motion intensity
• Flashlight detection
• Mean and standard deviation of luminance plane
• (Dcolor(i) Thcolor Dhist(i) defines the unsuitable frames
• Shot-level features
• Numbers and types of camera operation in each
  shot.
• Numbers of faces and flashlight event in each
  shot.
• The accumulation of distance between each frame
  and first frame can be used to describe the
  shots homogeneity.

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Importance Measure
A scaling coefficient according to synchronized
audio clips feature

• Frame-level score function

• The face and flashlight event have the highest
  weighting.
• Camera operation and higher motion intensity
  represent the video takers intension, so its
  more important.
• Frames with higher luminance and larger standard
  derivation are more suitable.
• The penalty of unsuitable frames will be
  discussed later.

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• The shot-level importance is motivated by
  observing that
• Shots with larger motion intensity take longer
  duration.
• The presence of face attracts viewer.
• Shots of higher heterogeneity can taker longer
  playing time.
• Shots with more camera operations are more
  important.
• Of course, shots with longer length in origin are
  more important.
• Shot-level importance

• The shot-level importance function is used in the
  medium profile to reassign each shots length
  according to its importance.
• Static shots takes shorter, while dynamic shots
  can take longer.? Gets better results after
  editing
• muvee autoProducer does not reassign each
  shots length!

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Example 1

• ????? (3155)
• Music SHE / ?????
• Length 425
• Profile Sequential Medium

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Proposed Profiles

• The usage of profiles allows users to customize
  their videos according to its content property
  and users preference in a easy way.
• We said that home videos have four types
• Causal, Non-causal, Recreational, Memorial
• For causal or non-causal videos, we use the
  sequential or non-sequential parameter to deal
  with.
• For memorial or recreational videos, the rhythmic
  or medium parameter is developed to cope with.
• In rhythmic, the music tempo/rhythm is better
  preserved, while some shots of video will be
  neglected.
• In medium, the accompany of music tempo/rhythm is
  not so clear as rhythm, but most of the shots
  will be promised to shown. The medium parameter
  preserved the original video the most.

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• Thus we have four profiles
• Sequential Rhythmic, Sequential Medium
• Non-Sequential Rhythmic, Non-Sequential Medium

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Rhythmic vs. Medium

• The video is segmented according to the audio
  clips and sub-clips.
• After projecting to the video time-line,
  searching in the video range to find the video
  segments with the highest score as the same
  length as audio segment.
• Finally concatenate all the selected segments.

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• Each shot will be reassign to a new length
  according to its shot importance, shots may
  becomes longer or shorter in proportion to the
  total length.
• After projection to the video space, the length
  budget is calculated according to the reduction
  rate then allocate the budget to each inner
  shots according to its length.
• If the allocated shot length is to short (frames), then its budget will be transfer to near
  shots.

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• However, there are some issues
• The fast tempo/rhythm audio clip may be aligned
  to a static video shot, which will be annoying
  for viewer.
• The slow audio clip may be aligned to a dynamic
  video shot.
• ? We apply an audio scaling coefficient in
  synchronization stage. The motion intensity of
  video shots weight will be decreased when
  aligned with a slow audio clip nearly preserved
  when synchronized with fast audio clip.
• Another issue when the media length differ

• Its unavoidable when the sequential policy is
  enforced. ?

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• For some video sources, the order of shots is not
  so important, and re-order shots will not degrade
  the original.
• If we allow re-order the input video shots,
  things may be better

• It sounds simple and intuitive, but its not an
  easy problem if we want to develop an efficient
  algorithm to find such permutation.
• Furthermore, the best solution may not exist
  and the optimal solution may not be only one
  permutation.

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Non-Sequential Permutation

• So we developed a randomize algorithm to find a
  not-bad solution within predictable computation
  time.
• First randomly permute each video shot
• Then we compute the Ravc audio-to-video
  coverage in the corresponding time-line for each
  shot

• Then we calculate the average Ravc, each
  permutation will has its Ravc.
• After lots of iterations, find the minimal Ravc,
  theoretically we can approach to the optimal
  solution efficiently and predictable, only
  depends on how many iterations we perform.

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• For an example, 10000 iterations are performed

• We can get better solution with more iterations,
  but through experiments, 10000 iterations are
  quite enough and will not be a burden for our
  computation power (actually its really fast)
• Since its random property, each synchronization
  result will be different. But we have discussed
  before that its normal to have lots of solutions.

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Example 2
??? (1908) Music ???? Length 425 Profile
Sequential Medium and Non-Sequential Medium
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Performance Evaluation

• Development environment
• AMD Duron 1.2G Hz with 386 MB RAM
• Analysis complexity
• For videos, about 1.21.31 comparing to the
  original video time.
• For audios, about 2 minutes for a 5 minute audio
  if perform the spectral analysis, 4-5 minutes are
  needed.
• The audio/video analysis will be saved as
  description files, so the analysis is required
  only once.
• The synchronization can be regarded as O(n)
  complexity.
• When analyzing, usually less than 20 MB RAM is
  required (depends on how many shots in video)
• The synchronization result is saved as an
  AviSynth script. Then we use VirtualDub to encode
  the produced musical video.

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Sample Videos
????? (3155)
??????? (1759)
??? (1908)
???? (4342)
????? (6034)
littleco ??? (2022)
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Whats Next?

• How to design the experimental result?
• The subjective test should not over-burden the
  viewer.
• Adding the shot transition effects? Such as
  dissolve, fade in, fade out.
• Ive tried, but not so easy as I thought.
• The automatic approach may not always product a
  satisfaction result and the experience is highly
  subjective and differs from people to people.
• Semi-automatic is probably the best compromise.
  The automatic result is served only as a
  pre-process basis and a labor-saving tool.
• But the video editing tool is hard to develop,
  and I doubt if its necessary to develop one from
  startup on the purpose of thesis.

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Questions and Discussion

• Any comments are welcomed.
• Acknowledgment
• Special thanks for Mr. ???, for his videos and
  suggestions. ?
• Thanks friends in DVworld who provide lots of
  ideas and comments.
• Thanks Chih-Hao Shen for his dancing video.