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

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.

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.

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.

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.

Fast Fourier Analysis (FFTransform) The power spectrum gives information about the frequency content, but doesn’t localize these components in time domain. Thus, while the time domain function indicates how a signal’s 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).

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.

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.

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.

WEB-BASED APPLICATIONS Multy layer architecture: First layer: web-client (web-browser). Second layer: web-server, CGI scripts and API’s for database connection. Third layer: database server.

DYNAMIC WEB TECHNOLOGIES  Client-side dynamic web technologies: ActiveX controls Java applets DHTML (Dynamic HTML)  Server-side dynamic web technologies : Common Gateway Interface (CGI) Server-Side JavaScript (SSJS) Active Server Pages (ASP) Java Servlets и JSP PHP

Digital watermark l Special mark, imperceptibly embedded in an image, text or other signal in order to control its use. l Embedding and retrieving one information from another is of basic importance in steganography and is done by the stegosystem’s principles 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 stegomessage’s presence determination. decoder – structure for secret message’ s restoring. INFORMATION PROTECTION WITH A DIGITAL WATERMARK

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.

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.

AUDIO WATERMARKING Echo data hiding The delay (δ b ) 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 (δ 1 ) seconds while the “zero” kernel has a (δ 0 ) second delay.

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.

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.

AUDIO WATERMARKING Phase coding

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.

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.

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 CASCON’98: Meeting of Minds, 1998