Digital modeling of speech signal
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This document summarizes digital modeling techniques for speech signals. It describes the vocal source and vocal tract that produce speech. It then discusses using sampling and techniques like PCM to digitally represent speech signals. Linear predictive coding is presented as a simple method to analyze speech that approximates samples as combinations of past signals. The summary concludes that linear prediction can be used for spectrum estimation by representing the vocal tract transfer function, pitch detection, and speech synthesis.
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PCM consists of 3 main steps: sampling, quantization, and binary encoding. Sampling converts an analog signal to digital pulses by taking amplitude measurements at regular time intervals. Quantization maps the infinite amplitude values between limits to a finite set of values. Binary encoding assigns binary code values to each quantization level. The sampling rate must be at least twice the highest frequency in the signal according to Nyquist's theorem. Quantization introduces error and noise that can be reduced by using more quantization levels, though this increases the bit rate. PCM is widely used in digital communications and its variants like ADPCM and DPCM aim to further compress signals.
Pulse Code Modulation
The document discusses various topics related to digital communication systems including:
- Advantages of digital over analog communication systems such as noise immunity and easier implementation of error control coding.
- The process of analog to digital conversion including sampling, quantization, encoding, and pulse code modulation (PCM).
- Digital modulation techniques like differential PCM (DPCM) and delta modulation (DM) that reduce redundancy before encoding.
- Considerations for line coding binary data onto an analog channel such as bandwidth, noise immunity, power efficiency and self-clocking capability.
Pulse code modulation (PCM)
Pulse code modulation (PCM) is a method to convert analog signals to digital signals for transmission. It involves sampling the analog signal, quantizing the samples to discrete levels, and encoding the quantized samples as binary code. At the receiver, PCM demodulation recovers the original analog signal. PCM allows analog signals to be transmitted over digital networks and is used in applications like digital telephony, audio recording, and radio control. Key steps in PCM include sampling the analog signal, quantizing the samples, encoding the quantized samples into binary code using methods like uniform quantization, and decoding and reconstructing the analog signal at the receiver.
Source coding systems
The document discusses digital communication systems and their advantages over analog communication. It describes how analog signals are digitized using sampling and quantization. Sampling must occur at least twice the maximum frequency of the signal to avoid aliasing, as stated by the Nyquist sampling theorem. Quantization converts continuous amplitudes to discrete levels, causing quantization error and noise. Digital communication provides benefits like lower distortion and easier signal processing. Companding is discussed as a technique used in pulse code modulation to improve signal-to-noise ratio by compressing higher amplitudes before transmission.
Pulse modulation
Pulse modulation techniques can encode an analog signal for transmission. This document discusses several techniques including:
- Pulse-amplitude modulation (PAM) which varies pulse amplitudes based on sample values of the message signal.
- Pulse code modulation (PCM) which assigns a binary code to each analog sample. PCM is commonly used in digital communications systems.
- Delta modulation which transmits one bit per sample indicating if the current sample is more positive or negative than the previous. It requires higher sampling rates than PCM for equal quality.
analog communication Pulse analog Modulation_.ppt
The document discusses various types of pulse modulation techniques used to convert analog signals to digital signals. It describes pulse amplitude modulation (PAM), pulse width modulation (PWM), and pulse position modulation (PPM). PAM encodes information by varying the amplitude of pulses, PWM varies the width, and PPM varies the position. The key requirements for pulse modulation are that the sampling frequency must be at least twice the maximum input frequency to avoid aliasing, and techniques like flat top sampling and PWM help reduce the aperture effect by limiting high frequency roll-off. Pulse modulation allows analog signals to be transmitted and processed digitally.
DDSP_2018_FOEHU - Lec 10 - Digital Signal Processing Applications
The document provides an overview of digital signal processing applications including digital spectrum analysis, speech processing, and radar. It discusses topics like digital spectrum analyzers, speech production and perception, audio compression techniques including channel vocoding, ADPCM, LPC, and CELP coding. The key concepts covered are time-frequency analysis, the anatomy and acoustics of speech, speech and audio compression standards, and speech modeling and coding.
Voice morphing document
Voice morphing is a technique that modifies a source speaker's speech to sound like it was spoken by a target speaker. It works by analyzing the source speech into an excitation signal and filter components, then resynthesizing it with the pitch and vocal characteristics of the target speaker. The key steps are detecting the pitches of the source and target speakers, scaling the source pitch to match the target, then resynthesizing the source speech using the target's vocal filter characteristics and the pitch-scaled excitation signal. Voice morphing was developed in 1999 and has applications in text-to-speech, dubbing, voice disguising, and public announcement systems.
Radar Systems -Unit- I : Radar Equation
This document provides an overview of the course content for Unit 1 of a radar systems course. The key topics covered include the modified radar range equation, signal-to-noise ratio, probability of detection and false alarms, integration of radar pulses, radar cross section of targets, creeping waves, transmitter power, pulse repetition frequency and range ambiguities, and system losses. The document also provides qualitative explanations and equations for several radar concepts.
Speaker recognition systems
This presentation consists of description about speaker verification system and speaker identification system
_Pulse-Modulation-Techniqnes.pdf
This document discusses various analog and digital pulse modulation techniques. It covers topics such as pulse amplitude modulation (PAM), pulse width modulation (PWM), pulse position modulation (PPM), pulse code modulation (PCM), and delta modulation (DM). It also discusses the key steps of PCM which are sampling, quantization, and binary encoding. Important concepts covered include Nyquist rate, quantization error, signal-to-noise ratio, bandwidth requirements, and line coding techniques.
311 pulse modulation
Pulse modulation techniques encode information by manipulating pulse characteristics such as amplitude, width, and position. Common pulse modulation types include pulse amplitude modulation (PAM), pulse width modulation (PWM), pulse position modulation (PPM), and pulse code modulation (PCM). PCM is the most widely used technique, where an analog signal is sampled, quantized into discrete levels, and encoded into a binary digital signal for transmission.
ADDITTIVE WHITE GAUSIAN NOIS ( AWGN)
1) The document discusses digital modulation techniques for transmitting digital information over an additive white Gaussian noise (AWGN) channel. It describes geometric representations of signal waveforms and orthogonalization procedures.
2) Binary and M-ary modulation schemes are covered, including binary antipodal signaling, orthogonal signaling, pulse position modulation, and frequency-shift keying. Optimal receivers for the AWGN channel using correlation and matched filtering are also described.
3) Probabilities of error are derived for various digital modulation techniques, including M-ary pulse amplitude modulation, phase-shift keying, and quadrature amplitude modulation. Differential phase-shift keying is also introduced.
Introduction to Modulation and Demodulation.pptx
1) The document discusses various modulation techniques used in communication systems including amplitude modulation (AM), frequency modulation (FM), phase modulation (PM), pulse amplitude modulation (PAM), frequency-shift keying (FSK), phase-shift keying (PSK), and their derivatives.
2) It explains the basic concepts of modulation such as using a message signal to control parameters of a carrier signal to transmit information.
3) Key modulation types covered are AM, which varies the amplitude of a carrier signal; FSK and PSK, which are used for digital modulation by shifting the frequency or phase of a carrier.
ANALYSIS OF SPEECH UNDER STRESS USING LINEAR TECHNIQUES AND NON-LINEAR TECHNI...
Analysis of speech for recognition of stress is important for identification of emotional state of
person. This can be done using ‘Linear Techniques’, which has different parameters like pitch,
vocal tract spectrum, formant frequencies, Duration, MFCC etc. which are used for extraction
of features from speech. TEO-CB-Auto-Env is the method which is non-linear method of
features extraction. Analysis is done using TU-Berlin (Technical University of Berlin) German
database. Here emotion recognition is done for different emotions like neutral, happy, disgust,
sad, boredom and anger. Emotion recognition is used in lie detector, database access systems, and in military for recognition of soldiers’ emotion identification during the war.
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Digital modeling of speech signal
- 1. Digital modeling of SPEECH SIGNAL
- 2. Speech Signal: Vocal source: Lung, vocal cord and vocal gate Vocal tract: start from voice gate till lip including throat and air tract. In addition to that mouth cavity, nose cavity, lip.
- 5. Simply we can represent speech signal using the schemes PCM,DPCM,DM etc.. For example PCM technique, Applying sampling theorem to the digital signal of speech.(if the signal band width is W hertz then the sampling period must be T<= 1/2W.) Measure of fidelity of PCM is SNR in this noise or error signal will be
- 7. Linear Predictive Method This is the simple method to analyze speech signal Basic idea sample of speech is approximated as linear combination of past speech signals.
- 8. Transfer function speech samples Let speech signal be x(n) With linear predictor Predictor error will be Predictor polynomial:
- 9. Use of Linear Prediction Spectrum estimation : If the predictor polynomial is assumed to represent the denominator of the vocal tract transfer function, we can obtain frequency response of vocal tract as Pitch detection Speech synthesis.