Methods for Investigation and Security of the Audio and Video Archive for Unique Bulgarian Bells

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Methods for Investigation and Security of the
Audio and Video Archive for Unique Bulgarian Bells

• Galina Bogdanova, Institute of Mathematics and
  Informatics
• Tihomir Trifonov, University of Veliko Tarnovo
• Todor Todorov, Institute of Mathematics and
  Informatics
• Tsvetanka Georgieva, University of Veliko Tarnovo

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Introduction

• The aim of our project is to study and identify
  several dozens of the most valuable bells in our
  churches, monasteries and museums as well as to
  develop an audio archive and video (with the help
  of advanced technologies) for analysis,
  preservation and protection of the data.

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Introduction

• Tasks
• Spectrum and wavelet analysis, mathematical
  modeling of acoustics processes and sound wave
  spreading, localization and measuring of faults
  if such are found.
• Development of audio and video archive with
  information collected by items, analysis,
  optimization and archiving, compression and
  protection of the database. Design and
  maintenance of the database and a web site for
  it.
• Description and documentation of the cultural and
  historical value of the bells (historical data,
  information about their present state with
  digital photos, video clips, etc.). Advertising,
  Public Relations, translation, etc.

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Spectrum and wavelet analysis

• Analyzing and recording the frequency spectrum
  of the bells during a stroke and in main tone
  will provide possibilities to obtain the fine
  features of every bell, its present-day condition
  and reveal the future perspectives.

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Fast Fourier Analysis (FFTransform)

• Breaks down a signal into constituent sinusoids
  of different frequencies. Another way to think of
  Fourier analysis is as a mathematical technique
  for transforming our view of the signal from
  time-based into frequency-based one. As it is
  known, Fourier analysis has a serious drawback.
  During the transform to the frequency domain, the
  time information is lost. When looking at a
  Fourier transform of a signal, it is impossible
  to tell when a particular event took place.

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Fast Fourier Analysis (FFTransform)

• The power spectrum gives information about the
  frequency content, but doesnt localize these
  components in time domain. Thus, while the time
  domain function indicates how a signals
  amplitude change over time, the frequency domain
  function tells how often such changes take place.
  The bridge between time and frequency is the
  Fourier transform (FT).

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Short-Time Fourier Transform (STFT )

• Maps a signal into a two-dimensional function of
  time and frequency. The STFT represents a sort of
  compromise between the time- and frequency-based
  views of a signal. It provides some information
  about both when and at what frequencies a signal
  event occurs. However, we can only obtain this
  information with limited precision, and that
  precision is determined by the size of the window.

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Short-Time Fourier Transform (STFT )

• The STFT is a modification of the Fourier
  transform. Instead of processing the entire
  signal at once, the STFT takes the FT on a block
  by block basis. Therefore, the resulting FT
  presents a signal frequency behavior during the
  time period covered by the data block.

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Wavelet analysis

• Represents the next logical step a windowing
  technique with variable-sized regions. Wavelet
  analysis allows the use of long time intervals
  where we want more precise low-frequency
  information, and shorter regions where we want
  high-frequency information.

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WEB-BASED APPLICATIONS
Multy layer architecture
First layer web-client (web-browser).
Second layer web-server, CGI scripts and APIs
for database connection .
Third layer database server.
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DYNAMIC WEB TECHNOLOGIES

• Client-side dynamic web technologies

ActiveX controls
Java applets
DHTML (Dynamic HTML)

• Server-side dynamic web technologies

Common Gateway Interface (CGI)
PHP
Server-Side JavaScript (SSJS)
Active Server Pages (ASP)
Java Servlets ? JSP
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• Digital watermark
• Special mark, imperceptibly embedded in an
  image, text or other signal in
• order to control its use .
• Embedding and retrieving one information from
  another is of basic
• importance in steganography and is done by
  the stegosystems principles

INFORMATION PROTECTION WITH A DIGITAL WATERMARK

• precursory coder structure for proper
  transforming of the secret
• message in order to embed it in the signal
  container.
• stegocoder structure for embedding the secret
  message in other data and
• reading its specialities.
• structure for watermark retrieving.
• stegodetector structure for stegomessages
  presence determination.
• decoder structure for secret message s
  restoring.

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AUDIO WATERMARKING

• Low-bit coding
• Simplest way to embed data into other data
  structures. By replacing the
• least significant bit of each sampling point
  by a coded binary string, we
• can encode a large amount of data in an audio
  signal.
• The major disadvantage of this method is its
  poor immunity to
• manipulation. Encoded information can be
  destroyed by channel noise,
• resampling etc.
• Could be improved by error-correcting codes.

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AUDIO WATERMARKING

• Echo data hiding
• Echo data hiding embeds data into a host audio
  signal by introducing an
• echo. The data are hidden by varying three
  parameters of the echo initial
• amplitude, decay rate, and offset.
• The coder uses two delay times, one to
  represent a binary one (offset)
• and another to represent a binary zero (offset
  delta). Both delay times
• are below the threshold at which the human ear
  can resolve the echo.

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AUDIO WATERMARKING
Echo data hiding
The delay (db) between the original signal and
the echo is dependent on which kernel or system
function we use. The one kernel is created with
a delay of (d1) seconds while the zero kernel
has a (d0) second delay.
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AUDIO WATERMARKING
Echo data hiding - decoding

• Perform calculation autocorrelation of the
  cepstrum.
• This transformatoin produces two defined spikes.
  Time delay between
• the spike and the original signal determines
  the decoding decision.

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AUDIO WATERMARKING
Phase coding

• Substituting the phase of an initial audio
  segment with a reference
• phase that represents the data.
• The phase of subsequent segments is adjusted in
  order to preserve the
• relative phase between segments.

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AUDIO WATERMARKING
Phase coding
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AUDIO WATERMARKING

• Phase coding decoding
• Initial synchronization needed.
• The value of the underlying phase of the first
  segment is detected as a 0
• or 1,which represents the coded binary string.

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Methods for data analyzing

• The data warehouse has enormous value by
  arranging operational data into meaningful
  information. They are designed for online
  analytical processing (OLAP).
• The data liable to OLAP is organized in
  multidimensional cubes.
• The data cubes store preprocessed summaries of
  the data.
• The data cubes creation and usage eliminates the
  need of joining the tables and preprocessing the
  values returned from the most frequently
  performed queries.
• One of the basic advantages of the OLAP mining is
  the usage of data extracted from data warehouses.
  The data is loaded into data warehouse after it
  is previously integrated, consolidated, cleaned,
  and transformed.

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Methods for data analyzing

• Multimedia data mining
• deals with the extraction of implicit knowledge,
  multimedia data relationships, or other patterns
  not explicitly stored in multimedia databases
• includes mining of data containing text, images,
  audio and video information
• aims multidimensional analyzing the multimedia
  data to find patterns characteristic of them,
  rules, hidden relationships and dependencies
  between the different attributes in database from
  multimedia objects.
• The construction of multimedia data cubes
  facilitates multiple dimensional analysis of
  multimedia data, and the mining of multiple kinds
  of knowledge, including summarization,
  classification, and association, in image and
  video databases. 1

1 O. Zaïane, J. Han, Z. Li J. Hou. Mining
Multimedia Data, In Proceedings of the CASCON98
Meeting of Minds, 1998