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Chapter: Graphics and Multimedia - Multimedia File Handling

Digital Voice and Audio

Sound is made up of continuous analog sine waves that tend to repeat depending on the music or voice. The analog waveforms are converted into digital fornlat by analog-to-digital converter (ADC) using sampling process.

DIGITAL VOICE AND AUDIO

Digital Audio

 

Sound is made up of continuous analog sine waves that tend to repeat depending on the music or voice. The analog waveforms are converted into digital fornlat by analog-to-digital converter (ADC) using sampling process.

Sampling process

Sampling is a process where the analog signal is sampled over time at regular intervals to obtain the amplitude of the analog signal at the sampling time.

 

Sampling rate

The regular interval at which the sampling occurs is called the sampling rate.


Digital Voice

Speech is analog in nature and is cOl1veli to digital form by an analog-to-digital converter (ADC). An ADC takes an input signal from a microphone and converts the amplitude of the sampled analog signal to an 8, 16 or 32 bit digital value.

The four important factors governing the

ADC process are sampling rate resolution linearity and conversion speed.

Sampling Rate: The rate at which the ADC takes a sample of an analog signal. Resolution: The number of bits utilized for conversion determines the resolution of ADC.

 

Linearity: Linearity implies that the sampling is linear at all frequencies and that the amplitude tmly represents the signal.

 

Conversion Speed: It is a speed of ADC to convert the analog signal into Digital signals. It must be fast enough.

 

VOICE Recognition System

 

Voice Recognition Systems can be classified into three types. 1.Isolated-word Speech Recognition.

 

2.Connected-word Speech Recognition.

3.Continuous Speech Recognition.

 

1.  Isolated-word Speech Recognition.

 

It provides recognition of a single word at a time. The user must separate every word by a pause. The pause marks the end of one word and the beginning of the next word.

 

Stage 1: Normalization

 

The recognizer's first task is to carry out amplitude and noise normalization to minimize the variation in speech due to ambient noise, the speaker's voice, the speaker's distance from and position relative to the microphone, and the speaker's breath noise.

 

Stage2: Parametric Analysis

 

It is a preprocessing stage that extracts relevent time-varying sequences of speech parameters. This stage serves two purposes: (i) It extracts time-varying speech parameters. (ii) It reduces the amount of data of extracting the relevant speech parameters.

 

Training modeIn training mode of the recognizer, the new frames are added to the reference list. Recognizer modeIf the recognizer is in Recognizer mode, then dynamic time warping is applied to the unknown patterns to average out the phoneme (smallest distinguishable sound, and spoken words are constructed by concatenatic basic phonemes) time duration. The unknown pattern is then compared with the reference patterns.

 

A speaker independent isolated word recognizer can be achieved by groupi.ng a large number of samples corresponding to a word into a single cluster.

 

2Connected-Word Speech RecognitionConnected-word speech consists of spoken phrase consisting of a sequence of words. It may not contain long pauses between words.

The method using Word Spotting technique

It Recognizes words in a connected-word phrase. In this technique, Recognition is carried out by compensating for rate of speech variations by the process called dynamic time warping (this process is used to expand or compress the time duration of the word), and sliding the adjusted connected-word phrase representation in time past a stored word template for a likely match.

 

Continuous Speech Recognition

This sytem can be divided into three sections:

 

(i)               A section consisting of digitization, amplitude normalization, time nonnalization and parametric representation.

 

(ii)             Second section consisting of segmentation and labeling of the speech segment into a symbolic string based on a knowledgebased or rule-based systems.

 

(iii)          The final section is to match speech segments to recognize word sequences.

 

Voice Recognition performance

 

It is categorized into two measures: Voice recognition performance and system performance. The following four measures are used to determine voice recognition performance.

 


Voice Recognition Applications

Voice mail integration: The voice-mail message can be integrated with e-mail messages to create an integrated message.

 

DataBase Input and Query Applications

 

A number of applications are developed around the voice recognition and voice synthesis function. The following lists a few applications which use Voice recognition.

 

         Application such as order entry and tracking

 

It is a server function; It is centralized; Remote users can dial into the system to enter an order or to track the order by making a Voice query.

 

         Voice-activated rolodex or address book

 

When a user speaks the name of the person, the rolodex application searches the name and address and voice-synthesizes the name, address, telephone numbers and fax numbers of a selected person. In medical emergency, ambulance technicians can dial in and register patients by speaking into the hospital's centralized system.

 

Police can make a voice query through central data base to take follow-up action ifhe catch any suspect.

 

Language-teaching systems are an obvious use for this technology. The system can ask the student to spell or speak a word. When the student speaks or spells the word, the systems performs voice recognition and measures the student's ability to spell. Based on the student's ability, the system can adjust the level of the course. This creates a self-adjustable learning system to follow the individual's pace.

 

Foreign language learning is another good application where"' an individual student can input words and sentences in the system. The system can then correct for pronunciation or grammar.

 

Musical Instrument Digital Interface (MIDI)

MIDI interface is developed by Daver Smith of sequential circuits, inc in 1982. It is an universal synthesizer interface

 

MIDI Specification 1.0

MIDI is a system specification consisting of both hardware and software ~omponents which define inter-coimectivity and a communication protocol for electronic sysnthesizers, sequences, rythm machines, personal computers, and other electronic musical instruments. The inter-connectivity defines the standard cabling scheme, connector type and input/output circuitry which enable these different MIDI instruments to be interconnected. The communication protocol defines standard multibyte messages that allow controlling the instrument"s voice and messages including to send response, to send status and to send exclusive.

 

MIDI Hardware Specification

 

The MIDI. hardware specification require five pin panel mount requires five pin panel mount receptacle DIN connectors for MIDI IN, MIDI OUT and MIDI THRU signals. The MIDI IN connector is for input signals The MIDI OUT is for output signals MIDI THRU connector is for daisy-chaining multiple MIDI instruments.

 

MIDI Interconnections

 

The MIDI IN port of an instrument receives MIDI ncssages to play the instrument's internal synthesizer. The MIDI OUT port sends MIDI messages to play these messages to an external synthesizer. The MIDI THRU port outputs MIDI messages received by the MIDI IN port for daisy-chaining external synthesizers.

 

MIDI Input and output circuitry:


Communication Protocol

The MIDI communication protocol uses multibyte messages; There are two types of messages:

 

(i)  Channel messages

(ii) System messages.

 

The channel message have three bytes. The first byte is called a status byte, and the other two bytes are called data bytes.

 

The two types of channel messages: (i) Voice messages

 

(ii) Mode messages.

System messages: The three types of system messages.

 

Common message: These messages are common to the complete system. These messages provide for functions.

 

System real.time messages: These messages are used for setting the system's real-time parameters. These parameters include the timing clock, starting and stopping the sequencer, resuming the sequencer from a stopped position and restarting the system.

 

System exclusive message: These messages contain manufacturer specific data such as identification, serial number, model number and other information.

 

SOUND BOARD ARCHITECTURE

A sound card consist of the following components:

 

MIDI Input/Output Circuitry, MIDI Synthesizer Chip, input mixture circuitry to mix CD audio input with LINE IN input and microphone input, analog-to-digital converter with a pulse code modulation circuit to convert analog signals to digital to create WAVfiles, a decompression and compression chip to compress and decompress audio files, a speech synthesizer to synthesize speech output, a speech recognition circuitry to recognize speech input and output circuitry to output stereo audio OUT or LINEOUT.

AUDIO MIXER

 

The audio mixer c:omponent of the sound card typically has external inputs for stereo CD audio, stereo LINE IN, and stereo microphone MICIN.

 

These are analog inputs, and they go through analog-to-digitaf conversion in conjunction with PCM or ADPCM to generate digitized samples.

 

SOUND BOARD ARCHITECTURE:


Analog-to-Digital Converters: The ADC gets its input from the audio mixer and converts the amplitude of a sampled analog signal to either an 8-bit or 16-bit digital value.

 

Digital-to-Analog Converter (DAC): A DAC converts digital input in the 'foml of W AVE files, MIDI output and CD audio to analog output signals.

 

Sound Compression and Decompression: Most sound boards include a codec for sound compression and decompression.

 

ADPCM for windows provides algorithms for sound compression.

CD-ROM Interface: The CD-ROM interface allows connecting u CD ROM drive.to the sound board.

 

VIDEO IMAGES AND ANIMATION

VIDEO FRAME GRABBER ARCHITECTURE

A video frame grabber is used to capture, manipulate and enhance video images.

 

A video frame grabber card consists of video channel multiplexer, Video ADC, Input look-up table with arithmetic logic unit, image frame buffer, compression-decompression circuitry, output color look-up table, video DAC and synchronizing circuitry.

 

Video Channel Multiplexer:

 

A video channel multiplexer has multiple inputs for different video inputs. The video channel multiplexer allows the video channel to be selected under program control and switches to the control circuitry appropriate for the selected channel in aTV with multi – system inputs.

 

Analog to Digital Converter: The ADC takes inputs from video multiplexer and converts the amplitude of a sampled analog signal to either an 8-bit digital value for monochrome or a 24 bit digital value for colour.

 

Input lookup table: The input lookup table along with the arithmetic logic unit (ALU) allows performing image processing functions on a pixel basis and an image frame basis. The pixel image-processing functions ate histogram stretching or histogram shrinking for image brightness and contrast, and histogram sliding to brighten or darken the image. The frame-basis image-processing functions perform logical and arithmetic operations.

 

Image Frame Buffer Memory: The image frame buffer is organized as a l024 x 1024 x 24 storage buffer to store image for image processing and display.

 

Video Compression-Decompression: The video compressiondecompression processor is used to compress and decompress still image data and video data.

Frame Buffer Output Lookup Table: The frame buffer data represents the pixel data and is used to index into the output look uptable. The output lookup table generates either an 8 bit pixel value for monochrome or a 24 bit pixel value for color.

 

SVGA Interface: This is an optional interface for the frame grabber. The frame grabber can be designed to include an SVGA frame buffer with its own output lookup table and digital-to-analog converter.

 

Analog Output Mixer: The output from the SVGA DAC and the output from image frame buffer DAC is mixed to generate overlay output signals. The primary components involved include the display image frame buffer and the display SVGA buffer. The display SVGA frame buffer is overlaid on the image frame buffer or live video, This allows SVGA to display live video.

 

Video and Still Image Processing

 

Video image processing is defined as the process of manipulating a bit map image so that the image can be enhanced, restored, distorted, or analyzed.

 

Let us discuss about some of the terms using in video and still image processing.

 

Pixel point to point processing: In pixel point-to-point processing, operations are carried out on individual pixels one at a time.

 

Histogram Sliding: It is used to change the overall visible effect of brightening or darkening of the image. Histogram sliding is implemented by modifying the input look-up table values and using the input lookup table in conjunction with arithmetic logic unit.

 

Histogram Stretching and Shrinking: It is to increase or decrease the contrast.

 

In histogram shrinking, the brighter pixels are made less bright and the darker pixels are made less dark. Pixel Threshold: Setting pixel threshold levels set a limit on the bright or dark areas of a picture. Pixel threshold setting is also achieved through the input lookup table.

 

Inter- frame image processing

 

Inter- frame image processing is the same as point-to-point image processing, except that the image processor operates on two images at the same time. The equation of the image operations is as follows: Pixel output (x, y) = (Image l(x, y)

 

Operator (Image 2(x, y)

Image Averaging: Image averaging minimizes or cancels the effects of random noise.

 

Image Subtraction: Image subtraction is used to determine the change from one frame to the next .for image comparisons for key frame detection or motion detection.

 

Logical Image Operation: Logical image processing operations are useful for comparing image frames and masking a block in an image frame.

 

Spatial Filter Processing The rate of change of shades of gray or colors is called spatial frequency. The process of generating images with either low-spatial frequency-components or high frequency components is called spatial filter processing.

 

Low Pass Filter: A low pass filter causes blurring of the image and appears to cause a reduction in noise.

 

High Pass Filter: The high-pass filter causes edges to be emphasized. The high-pass filter attenuates low-spatial frequency components, thereby enhancing edges and sharpening the image.

 

Laplacian Filter: This filter sharply attenuates low-spatial-frequency components without affecting and high-spatial frequency components, thereby enhancing edges sharply.

 

Frame Processing Frame processing operations are most commonly for geometric operations, image transformation, and image data compression and decompression Frame processing operations are very compute intensive many multiply and add operations, similar to spatial filter convolution operations.

 

Image scaling: Image scaling allows enlarging or shrinking the whole or part of an image.

 

Image rotation: Image rotation allows the image to be rotated about a center point. The operation can be used to rotate the image orthogonally to reorient the image if it was scanned incorrectly. The operation can also be used for animation. The rotation formula is:

 

pixel output-(x, y) = pixel input (x, cos Q + y sin Q, - x sin Q + Y cos Q) where, Q is the orientation angle

 

x, yare the spatial co-ordinates of the original pixel.

 

Image translation: Image translation allows the image to be moved up and down or side to side. Again, this function can be used for animation.

 

The translation formula is:

Pixel output (x, y) =Pixel Input (x + Tx, y + Ty) where

Tx and Ty are the horizontal and vertical coordinates. x, yare the spatial coordinates of the original pixel.

Image transformation: An image contains varying degrees of brightness or colors defined by the spatial frequency. The image can be transformed from spatial domain to the frequency domain by using frequency transform.

 

Image Animation Techniques

 

Animation: Animation is an illusion of movement created by sequentially playing still image frames at the rate of 15-20 frames per second.

 

Toggling between image frames: We can create simple animation by changing images at display time. The simplest way is to toggle between two different images. This approach is good to indicate a "Yes" or "No" type situation.

 

Rotating through several image frames: The animation contains several frames displayed in a loop. Since the animation consists of individual frames, the playback can be paused and resumed at any time.


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