Linear Predictive Coding
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What are Linear Predictive coding, its methods, and analysis? LPC using Autocorrelation, LPC-Vocoder, etc
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Linear Predictive Coding
Linear Predictive Coding (LPC) is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate. It provides extremely accurate estimates of speech parameters, and is relatively efficient for computation.
SPEECH CODING
The document discusses speech processing and vocoding. It begins by defining speech and how it is produced, including voiced and unvoiced sounds. It then describes the human speech production system and various speech coding techniques like waveform coding, vocoding, and analysis-by-synthesis coding. Finally, it provides details on the G.729 speech codec, including its operations, process flow, specifications, and how it achieves speech compression to 8 kbps from the original 128 kbps.
Final ppt
This document summarizes research on applying speech enhancement techniques including spectral subtraction and Wiener filtering. The goals were to examine and simulate these techniques in Matlab. The techniques were tested on speech degraded by additive noise at different signal-to-noise ratios. Spectral subtraction removes noise by subtracting noise spectrum estimates from the degraded speech spectrum. Wiener filtering suppresses noise by multiplying the speech spectrum by a frequency response. Both techniques performed similarly at low noise, but Wiener filtering performed better at higher noise levels. Future work could include automatic noise detection and adaptation to changing noise.
Homomorphic speech processing
This document discusses homomorphic speech processing and techniques for speech enhancement. It provides an overview of modeling speech production as the excitation of a linear time-invariant system. Homomorphic filtering is introduced as a way to deconvolve speech into excitation and system response using logarithmic transformations. The complex cepstrum is discussed as a representation of speech that can be used to estimate pitch, voicing and formant frequencies. Homomorphic vocoding is described as a speech coding technique that quantizes the low-time part of the cepstrum at regular intervals to encode speech. Common techniques for speech enhancement like spectral subtraction and adaptive noise cancellation are also mentioned.
Speech Signal Processing
This document discusses speech signal processing and speech recognition. It begins by defining speech processing and its relationship to digital signal processing. It then outlines several disciplines related to speech processing including signal processing, physics, pattern recognition, and computer science. The document discusses aspects of speech signals including phonemes, the speech waveform, and spectral envelope. It covers various aspects of speech processing including pre-processing, feature extraction, and recognition. It provides details on techniques for pre-processing, feature extraction including filtering, linear predictive coding, and cepstrum. Finally, it summarizes the main steps in a speech recognition procedure including endpoint detection, framing and windowing, feature extraction, and distortion measure calculations for recognition.
Adaptive filter
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
Speech processing
The document discusses research issues in speech processing. It covers topics like speech production, speech processing tasks, speech measurements, speech signal components, automatic speech recognition, speaker recognition, text-to-speech systems, speech coding, and a proposed speech-assisted translation corrector system. The key challenges in speech processing research are modeling the human auditory system, developing large multilingual speech databases, and generating natural sounding synthetic speech.
Speech coding techniques
This document discusses various techniques for speech coding used in digital communication systems. It covers fundamental concepts like sampling theory, quantization, predictive coding, and linear predictive coding (LPC). It then describes specific speech codecs including PCM, ADPCM, CELP, LD-CELP, ACELP, and LPC vocoders. It discusses characteristics of speech coding like being lossy and metrics like SNR and MOS. Finally, it provides details on widely used standards like G.711, G.729, G.723.1, and GSM.
Recommended
Linear Predictive Coding
Linear Predictive Coding (LPC) is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate. It provides extremely accurate estimates of speech parameters, and is relatively efficient for computation.
SPEECH CODING
The document discusses speech processing and vocoding. It begins by defining speech and how it is produced, including voiced and unvoiced sounds. It then describes the human speech production system and various speech coding techniques like waveform coding, vocoding, and analysis-by-synthesis coding. Finally, it provides details on the G.729 speech codec, including its operations, process flow, specifications, and how it achieves speech compression to 8 kbps from the original 128 kbps.
Final ppt
This document summarizes research on applying speech enhancement techniques including spectral subtraction and Wiener filtering. The goals were to examine and simulate these techniques in Matlab. The techniques were tested on speech degraded by additive noise at different signal-to-noise ratios. Spectral subtraction removes noise by subtracting noise spectrum estimates from the degraded speech spectrum. Wiener filtering suppresses noise by multiplying the speech spectrum by a frequency response. Both techniques performed similarly at low noise, but Wiener filtering performed better at higher noise levels. Future work could include automatic noise detection and adaptation to changing noise.
Homomorphic speech processing
This document discusses homomorphic speech processing and techniques for speech enhancement. It provides an overview of modeling speech production as the excitation of a linear time-invariant system. Homomorphic filtering is introduced as a way to deconvolve speech into excitation and system response using logarithmic transformations. The complex cepstrum is discussed as a representation of speech that can be used to estimate pitch, voicing and formant frequencies. Homomorphic vocoding is described as a speech coding technique that quantizes the low-time part of the cepstrum at regular intervals to encode speech. Common techniques for speech enhancement like spectral subtraction and adaptive noise cancellation are also mentioned.
Speech Signal Processing
This document discusses speech signal processing and speech recognition. It begins by defining speech processing and its relationship to digital signal processing. It then outlines several disciplines related to speech processing including signal processing, physics, pattern recognition, and computer science. The document discusses aspects of speech signals including phonemes, the speech waveform, and spectral envelope. It covers various aspects of speech processing including pre-processing, feature extraction, and recognition. It provides details on techniques for pre-processing, feature extraction including filtering, linear predictive coding, and cepstrum. Finally, it summarizes the main steps in a speech recognition procedure including endpoint detection, framing and windowing, feature extraction, and distortion measure calculations for recognition.
Adaptive filter
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
Speech processing
The document discusses research issues in speech processing. It covers topics like speech production, speech processing tasks, speech measurements, speech signal components, automatic speech recognition, speaker recognition, text-to-speech systems, speech coding, and a proposed speech-assisted translation corrector system. The key challenges in speech processing research are modeling the human auditory system, developing large multilingual speech databases, and generating natural sounding synthetic speech.
Speech coding techniques
This document discusses various techniques for speech coding used in digital communication systems. It covers fundamental concepts like sampling theory, quantization, predictive coding, and linear predictive coding (LPC). It then describes specific speech codecs including PCM, ADPCM, CELP, LD-CELP, ACELP, and LPC vocoders. It discusses characteristics of speech coding like being lossy and metrics like SNR and MOS. Finally, it provides details on widely used standards like G.711, G.729, G.723.1, and GSM.
Sampling Theorem
The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
Digital speech processing lecture1
This document provides an overview of a course on digital speech processing. The course will cover fundamentals of speech production and perception, as well as techniques for digital speech processing including short-time Fourier analysis and linear predictive coding methods. Applications that will be discussed include speech coding, synthesis, recognition, and other speech applications involving pattern matching problems. Students will learn about representations and algorithms for processing speech signals.
Adaptive filter
An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. It has two processes: a filtering process that produces an output in response to input, and an adaptation process that adjusts the filter parameters to changing environments based on the error signal. Adaptive filters are commonly implemented as digital FIR filters and are used for applications like system identification, acoustic echo cancellation, channel equalization, and noise cancellation.
4.5 equalizers and its types
1. Equalizers are used to reduce inter-symbol interference in wireless communication and help reduce bit errors at the receiver.
2. There are two main types of equalizers - linear equalizers and non-linear equalizers. Linear equalizers include zero forcing and MMSE equalizers, while non-linear equalizers include decision feedback equalizers.
3. Adaptive equalizers automatically adapt to changing channel properties over time using algorithms like LMS and RLS to update equalizer coefficients.
speech processing basics
The document discusses digital speech processing. It covers the fundamentals of speech processing including the anatomy and physiology of speech production, acoustic theory of speech, and digital models of speech signals. It then discusses applications of speech processing such as speech recognition, speech understanding, speech synthesis, word processing, text prediction, and automatic summarization. Finally, it provides more details on speech production, recognition, classification of sounds, and an overview of signal processing aspects involved in digital speech processing.
Speech signal processing lizy
The document discusses speech signal processing. It begins by describing speech production, including the vocal tract model and acoustic theory of speech. It then covers various methods for speech analysis in both the time and frequency domains, including short-time analysis, short-time energy, zero crossing rate, and autocorrelation function. Parametric representations of speech such as linear predictive coding are also introduced. The document concludes by mentioning several applications of speech processing such as speech coding, recognition, and transmission over the internet.
Gaussian noise
The document discusses Gaussian noise, which refers to statistical noise that follows a normal distribution. It is commonly found in digital images, telecommunications systems, and other contexts. Some key points made in the document include:
- Gaussian noise arises from natural sources like thermal vibrations and has a probability density function given by the normal distribution.
- In digital images, it provides a good model for sensor and transmission noise and can be reduced using spatial filters, though this may also blur details.
- It is commonly used to model thermal noise in communication channels, where it is assumed to be additive, white, and have a Gaussian distribution.
- Bit error rate and packet error ratio are measures of noise that indicate the need
Speech technology basics
Digital signal processing (DSP) involves converting analog signals to digital signals and manipulating the digital signals using software algorithms. DSP systems use analog-to-digital conversion to convert analog signals to digital signals represented as sequences of numbers. They then process the digital signals using a digital signal processor and convert them back to analog signals using digital-to-analog conversion. Key techniques in DSP include decomposing signals into simple components, processing the components individually, and then combining the results.
Source coding
This document provides an overview of digital communications and source encoding. It discusses why digital communication is preferable to analog, describes the basic block diagram of a digital communication system, and defines key terms like the information source and channel encoder. The document then covers some of the foundational work in digital communications, including Nyquist's sampling theorem and Shannon's channel capacity theorem. It discusses ideal sampling and the Nyquist rate, as well as practical sampling techniques like sample-and-hold. The document also covers quantization, quantization noise, and how the step size and number of quantization levels affect the signal-to-quantization noise ratio. In closing, it briefly mentions pulse code modulation and nonuniform quantization.
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Windowing techniques of fir filter design
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
Sampling
This document discusses the process of sampling in signal processing. It defines key terms like analog and digital signals, sampling frequency, and samples. It explains how sampling works by taking regular measurements of a continuous signal's amplitude over time. This converts it into a discrete-time signal. It discusses applications of sampling like audio sampling, where signals are typically sampled above 20 kHz. It also discusses video sampling rates and speech sampling rates. The document contains examples and diagrams to illustrate these concepts.
Generation of SSB and DSB_SC Modulation
The document discusses two methods of single sideband (SSB) modulation and balanced modulator modulation. It explains that SSB modulation eliminates one sideband from an amplitude modulated wave. It then describes the balanced modulator method, which uses two balanced modulators and a 90 degree phase shift to cancel out one sideband. The document also provides a brief overview of double sideband suppressed carrier (DSB-SC) modulation and notes that it uses two methods: multiplier modulation and balanced modulator.
Equalization
The document discusses equalization techniques used to mitigate inter-symbol interference (ISI) in digital communication systems. Equalization aims to remove ISI and noise effects from the channel. It is located at the receiver and uses techniques like linear equalizers, decision feedback equalization, and maximum likelihood sequence estimation to estimate the channel response and minimize the error between transmitted and received symbols while balancing noise. As the wireless channel changes over time, adaptive equalization is used where the equalizer periodically trains and tracks the changing channel response.
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Eye diagram in Communication
In telecommunication, an eye pattern, also known as an eye diagram, is an oscilloscope display in which a digital signal from a receiver is repetitively sampled and applied to the vertical input, while the data rate is used to trigger the horizontal sweep. It is so called because, for several types of coding, the pattern looks like a series of eyes between a pair of rails. It is a tool for the evaluation of the combined effects of channel noise and intersymbol interference on the performance of a baseband pulse-transmission system. It is the synchronised superposition of all possible realisations of the signal of interest viewed within a particular signaling interval.
Speech Compression using LPC
This document discusses speech compression using linear predictive coding (LPC). It begins with the objectives of developing low bit-rate speech coders for cellular networks. It then introduces LPC and how it models the human vocal tract. The key aspects of LPC encoding and decoding are described, including analysis, synthesis, and the Levinson-Durbin algorithm. Simulation results on compressing male and female speech are presented, showing compression ratios and signal-to-noise ratios. The document concludes that LPC is well-suited for secure telephone systems by preserving the meaning of speech at low bit rates.
Speech Compression using LPC
The document summarizes linear predictive coding (LPC), a speech compression technique. LPC works by modeling the human vocal tract and representing each speech segment as a linear combination of past speech samples. It analyzes speech signals by determining if segments are voiced or unvoiced, estimating the pitch period, and computing filter coefficients. The coefficients and other parameters are transmitted to allow reconstruction of the speech. LPC can achieve a bit rate of 2400 bps, making it suitable for secure communications. Simulation results show LPC can compress male and female speech but introduces noise, performing better on male voices which have less high frequencies.
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Digital speech processing lecture1
This document provides an overview of a course on digital speech processing. The course will cover fundamentals of speech production and perception, as well as techniques for digital speech processing including short-time Fourier analysis and linear predictive coding methods. Applications that will be discussed include speech coding, synthesis, recognition, and other speech applications involving pattern matching problems. Students will learn about representations and algorithms for processing speech signals.
Adaptive filter
An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. It has two processes: a filtering process that produces an output in response to input, and an adaptation process that adjusts the filter parameters to changing environments based on the error signal. Adaptive filters are commonly implemented as digital FIR filters and are used for applications like system identification, acoustic echo cancellation, channel equalization, and noise cancellation.
4.5 equalizers and its types
1. Equalizers are used to reduce inter-symbol interference in wireless communication and help reduce bit errors at the receiver.
2. There are two main types of equalizers - linear equalizers and non-linear equalizers. Linear equalizers include zero forcing and MMSE equalizers, while non-linear equalizers include decision feedback equalizers.
3. Adaptive equalizers automatically adapt to changing channel properties over time using algorithms like LMS and RLS to update equalizer coefficients.
speech processing basics
The document discusses digital speech processing. It covers the fundamentals of speech processing including the anatomy and physiology of speech production, acoustic theory of speech, and digital models of speech signals. It then discusses applications of speech processing such as speech recognition, speech understanding, speech synthesis, word processing, text prediction, and automatic summarization. Finally, it provides more details on speech production, recognition, classification of sounds, and an overview of signal processing aspects involved in digital speech processing.
Speech signal processing lizy
The document discusses speech signal processing. It begins by describing speech production, including the vocal tract model and acoustic theory of speech. It then covers various methods for speech analysis in both the time and frequency domains, including short-time analysis, short-time energy, zero crossing rate, and autocorrelation function. Parametric representations of speech such as linear predictive coding are also introduced. The document concludes by mentioning several applications of speech processing such as speech coding, recognition, and transmission over the internet.
Gaussian noise
The document discusses Gaussian noise, which refers to statistical noise that follows a normal distribution. It is commonly found in digital images, telecommunications systems, and other contexts. Some key points made in the document include:
- Gaussian noise arises from natural sources like thermal vibrations and has a probability density function given by the normal distribution.
- In digital images, it provides a good model for sensor and transmission noise and can be reduced using spatial filters, though this may also blur details.
- It is commonly used to model thermal noise in communication channels, where it is assumed to be additive, white, and have a Gaussian distribution.
- Bit error rate and packet error ratio are measures of noise that indicate the need
Speech technology basics
Digital signal processing (DSP) involves converting analog signals to digital signals and manipulating the digital signals using software algorithms. DSP systems use analog-to-digital conversion to convert analog signals to digital signals represented as sequences of numbers. They then process the digital signals using a digital signal processor and convert them back to analog signals using digital-to-analog conversion. Key techniques in DSP include decomposing signals into simple components, processing the components individually, and then combining the results.
Source coding
This document provides an overview of digital communications and source encoding. It discusses why digital communication is preferable to analog, describes the basic block diagram of a digital communication system, and defines key terms like the information source and channel encoder. The document then covers some of the foundational work in digital communications, including Nyquist's sampling theorem and Shannon's channel capacity theorem. It discusses ideal sampling and the Nyquist rate, as well as practical sampling techniques like sample-and-hold. The document also covers quantization, quantization noise, and how the step size and number of quantization levels affect the signal-to-quantization noise ratio. In closing, it briefly mentions pulse code modulation and nonuniform quantization.
APPLICATION OF DSP IN BIOMEDICAL ENGINEERING
This document discusses the application of digital signal processing (DSP) in biomedical engineering. It describes how DSP is used in applications like electrocardiography (ECG), hearing aids, magnetic resonance imaging (MRI), and measuring blood pressure. DSP enables the analysis and visualization of biomedical data and improves the efficiency of medical devices. Key advantages of DSP include its ability to precisely diagnose conditions, reduce background noise, and provide highly customizable solutions for individual patient needs.
Windowing techniques of fir filter design
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
Sampling
This document discusses the process of sampling in signal processing. It defines key terms like analog and digital signals, sampling frequency, and samples. It explains how sampling works by taking regular measurements of a continuous signal's amplitude over time. This converts it into a discrete-time signal. It discusses applications of sampling like audio sampling, where signals are typically sampled above 20 kHz. It also discusses video sampling rates and speech sampling rates. The document contains examples and diagrams to illustrate these concepts.
Generation of SSB and DSB_SC Modulation
The document discusses two methods of single sideband (SSB) modulation and balanced modulator modulation. It explains that SSB modulation eliminates one sideband from an amplitude modulated wave. It then describes the balanced modulator method, which uses two balanced modulators and a 90 degree phase shift to cancel out one sideband. The document also provides a brief overview of double sideband suppressed carrier (DSB-SC) modulation and notes that it uses two methods: multiplier modulation and balanced modulator.
Equalization
The document discusses equalization techniques used to mitigate inter-symbol interference (ISI) in digital communication systems. Equalization aims to remove ISI and noise effects from the channel. It is located at the receiver and uses techniques like linear equalizers, decision feedback equalization, and maximum likelihood sequence estimation to estimate the channel response and minimize the error between transmitted and received symbols while balancing noise. As the wireless channel changes over time, adaptive equalization is used where the equalizer periodically trains and tracks the changing channel response.
Pulse Modulation ppt
This document discusses various types of pulse modulation techniques used in analog and digital communication systems. It begins by defining pulse amplitude modulation (PAM) and describing how the amplitude of pulses varies proportionally to the message signal. It then discusses different types of PAM based on the sampling technique used - ideal, natural, and flat-top sampling. Flat-top sampling uses sample-and-hold circuits and can introduce amplitude distortion known as the aperture effect. The document also covers pulse width modulation (PWM), pulse position modulation (PPM), pulse code modulation (PCM), delta modulation (DM), and their advantages. It explains the sampling theorem and proves it through Fourier analysis. Finally, it discusses bandwidth requirements, transmission, drawbacks
Composite video signal
This document discusses modulation techniques and the composite video signal. It describes positive modulation, which increases the carrier amplitude with the signal amplitude, and negative modulation, which decreases the carrier amplitude. Negative modulation is preferred for TV broadcasting as it minimizes the effect of noise. The composite video signal contains the camera signal, blanking pulses, and synchronizing pulses. It discusses the components, levels, and functions of the horizontal and vertical blanking pulses.
DPCM
A Brief Knowledge about Differential Pulse Code Modulation.
It contains the basics of Pulse Code modulation and why we all switching to Differential Pulse Code Modulation.
All the things about the Differential Pulse Code Modulation is given in a good understandable way
Chap04
This document discusses optimal receivers for additive white Gaussian noise (AWGN) channels. It begins by modeling the digital communication system and channel as a vector channel with additive noise. It defines optimal receivers as those that minimize the error probability. The document then derives the maximum likelihood (ML) and maximum a posteriori probability (MAP) decision rules, and shows that the ML rule is to choose the message with highest probability density given the received vector. It also discusses estimating bits individually and relates bit and symbol error probabilities. Preprocessing is discussed, showing it cannot reduce the error rate of an optimal receiver.
Introduction to dsp
This document provides an overview of digital signal processing (DSP). It begins by defining an analog signal and a digital signal. It then describes the basic components of a DSP system, which includes an analog-to-digital converter (ADC) to convert the analog input signal to digital, a digital signal processor to process the digital signal, and a digital-to-analog converter (DAC) to reconstruct the analog output signal. Finally, it discusses some advantages and limitations of DSP systems compared to analog systems and provides examples of DSP applications.
Eye diagram in Communication
In telecommunication, an eye pattern, also known as an eye diagram, is an oscilloscope display in which a digital signal from a receiver is repetitively sampled and applied to the vertical input, while the data rate is used to trigger the horizontal sweep. It is so called because, for several types of coding, the pattern looks like a series of eyes between a pair of rails. It is a tool for the evaluation of the combined effects of channel noise and intersymbol interference on the performance of a baseband pulse-transmission system. It is the synchronised superposition of all possible realisations of the signal of interest viewed within a particular signaling interval.
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This document discusses speech compression using linear predictive coding (LPC). It begins with the objectives of developing low bit-rate speech coders for cellular networks. It then introduces LPC and how it models the human vocal tract. The key aspects of LPC encoding and decoding are described, including analysis, synthesis, and the Levinson-Durbin algorithm. Simulation results on compressing male and female speech are presented, showing compression ratios and signal-to-noise ratios. The document concludes that LPC is well-suited for secure telephone systems by preserving the meaning of speech at low bit rates.
Speech Compression using LPC
The document summarizes linear predictive coding (LPC), a speech compression technique. LPC works by modeling the human vocal tract and representing each speech segment as a linear combination of past speech samples. It analyzes speech signals by determining if segments are voiced or unvoiced, estimating the pitch period, and computing filter coefficients. The coefficients and other parameters are transmitted to allow reconstruction of the speech. LPC can achieve a bit rate of 2400 bps, making it suitable for secure communications. Simulation results show LPC can compress male and female speech but introduces noise, performing better on male voices which have less high frequencies.
Multimedia Compression and Communication
DPCM and ADPCM are audio compression techniques that exploit the fact that differences between successive audio samples are typically smaller than the sample amplitudes. DPCM encodes differences between original and predicted samples while ADPCM varies the number of bits used based on difference size. Higher compression can be achieved through predictive coding and linear predictive coding, which analyze audio to determine perceptual features like pitch, period, and loudness for encoding. Perceptual coding considers how the human ear perceives sound by exploiting frequency and temporal masking effects.
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Lpc vocoder implemented by using matlab
An LPC vocoder takes a speech waveform sampled at 8kHz and compresses it to a lower bitrate for transmission by modeling the human vocal tract as a linear system over frames of 25ms. It estimates the vocal tract spectrum and pitch in each frame, transmitting this data which is much smaller than sending the raw samples. At decoding, the estimated spectrum is excited with an impulse train of the estimated pitch to reproduce the speech. The document also describes a gain estimation method that utilizes speech waveform envelopes to estimate gains for voiced and unvoiced frames such that the synthetic speech amplitude matches the envelope.
Linear predictive coding documentation
This document provides an introduction and overview of linear predictive coding (LPC) and vocoding for speech processing.
LPC analyzes speech by estimating the spectral envelope (formants) and other parameters like intensity and pitch frequency. It removes the estimated formant effects to obtain the excitation signal. These parameters are transmitted instead of the full digital speech signal to reduce bandwidth.
Vocoding is a related speech analysis/synthesis technique. It uses a filter bank to extract the amplitude envelopes of different frequency bands, which are transmitted instead of the full speech signal. This also reduces bandwidth needed for voice transmission.
The document goes on to provide more details on the LPC analysis process, popular parameter representations like LAR
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This document discusses various audio compression techniques including:
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2. Third-order predictive DPCM which uses predictions of past 3 samples to improve accuracy over DPCM.
3. Adaptive Differential PCM (ADPCM) which varies the number of bits used based on signal amplitude.
It then covers more advanced techniques like Linear Predictive Coding (LPC) which analyzes perceptual features of audio to further reduce bitrates.
Harmonic speech coding
This document summarizes several techniques for harmonic speech coding at low bit rates. It describes sinusoidal analysis and synthesis, which separates speech into voiced and unvoiced components. Parameter estimation techniques are discussed, including voicing determination and harmonic amplitude estimation. Common harmonic coders are then summarized, such as Sinusoidal Transform Coding (STC) at 4.8 kbps, Improved Multi-Band Excitation (IMBE) at 4.15 kbps, and Split-Band Linear Predictive Coding (SB-LPC) at 4 kbps. The document concludes that basic sinusoidal analysis and synthesis can reproduce high quality speech using a harmonic set of sinusoids.
Sub band project
The document provides an introduction and overview of sub-band coding for speech signals. It discusses how sub-band coding works to divide the speech spectrum into multiple sub-bands using filter banks, then encode each sub-band separately to reduce the bit rate. The key steps involve analysis filtering, quantization and coding of sub-bands, then synthesis filtering to reconstruct the signal. MATLAB code is presented to implement a sub-band coder using quadrature mirror filters. Applications of sub-band coding include speech and audio coding standards.
H0814247
This document summarizes a research paper on pitch detection of speech synthesis using MATLAB. It discusses using an adaptable filter and peak-valley decision method to determine pitch marks for speech synthesis. Low-pass filtering and autocorrelation are used to detect pitch periods. An adaptive filter is designed to flatten spectral peaks. Peak and valley costs are calculated over each pitch period to determine pitch marks. Dynamic programming is then used to obtain the optimal pitch mark locations for high quality speech synthesis.
Design and Implementation of Speech Based Scientific Calculator
In recent days speech recognition based systems are gaining more prominence and are used in a wide range of applications. Calculator has become an indispensable part of our lives, used at home, at local retail shops or at workplaces like banks, IT companies, medical laboratories etc. Speech recognized calculator is one of the speech recognition systems, in which the digits and operators spoken by the user with pauses in between are analyzed using various algorithms and finally one of the 10 digits (0 to 9) and one of the 5 operators(+, -, *, /, =) The objective of our paper is to design and implement the speech recognized calculator in MATLAB and Standalone Microcontroller. This designed system is able to take detect the digits and operators of the arithmetic expression whose result is to be calculated. In this paper we have used linear predictive method for recognizing the digit/operator in MATLAB and Analog Speech Recognition Chip.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This document summarizes research on using linear predictive coding (LPC) and related techniques for speech recognition and compression. Key points discussed include:
1) LPC is used to compress and encode speech signals for transmission by determining a filter to predict samples from past values, minimizing error. Filter coefficients are encoded and decoded.
2) LPC and PARCOR parameters can characterize phonemes and have potential for speech recognition by analyzing short frames of speech. Recognition rates of 65% for vowels and 94% for consonants were achieved.
3) An LPC-based speech coding system was implemented and tested for mobile radio communications, achieving a bit error rate performance suitable for speech transmission.
Speaker recognition using MFCC
This document discusses speaker recognition using Mel Frequency Cepstral Coefficients (MFCC). It describes the process of feature extraction using MFCC which involves framing the speech signal, taking the Fourier transform of each frame, warping the frequencies using the mel scale, taking the logs of the powers at each mel frequency, and converting to cepstral coefficients. It then discusses feature matching techniques like vector quantization which clusters reference speaker features to create codebooks for comparison to unknown speakers. The document provides references for further reading on speech and speaker recognition techniques.
Subband Coding
Subband coding decomposes a source signal into constituent frequency bands using digital filters like low-pass and high-pass filters. This separation into subbands allows each frequency component to be encoded and decoded separately, improving compression performance over techniques that treat the whole signal as one. The basic subband coding algorithm involves analysis using filtering and decimation to separate the signal, quantization and coding of the subband signals, and synthesis by decoding, upsampling and reconstruction filtering to reconstruct the original signal. Applications of subband coding include speech coding, audio coding and image compression, with MPEG audio standards using subband coding with 32 filters and bandwidths of f/64.
How to play audio from a microcontroller
This tutorial has a brief description of sound, how its perceived, how its recorded , stored and how it can be reproduced.
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Design and Implementation of Speech Based Scientific Calculator
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原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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#Abstract:
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#Prerequisites:
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二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
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◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
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- 2. INTRODUCTION • Linear Predictive Coding (LPC) is one of the most powerful speech analysis techniques, and one of the most useful methods for encoding good quality speech at a low bit rate. It provides extremely accurate estimates of speech parameters, and is relatively efficient for computation. • The most important aspect of LPC is the linear predictive filter which allows the value of the next sample to be determined by a linear combination of previous samples
- 3. • This provides a rate of 64000 bits/second. Linear predictive coding reduces this to 2400 bits/second. At this reduced rate the speech has a distinctive synthetic sound and there is a noticeable loss of quality. However, the speech is still audible and it can still be easily understood. Since there is information loss in linear predictive coding, it is a lossy form of compression. • LPC starts with the assumption that the speech signal is produced by a buzzer at the end of a tube. The glottis (the space between the vocal cords) produces the buzz, which is characterized by its intensity (loudness) and frequency (pitch). The vocal tract (the throat and mouth) forms the tube, which is characterized by its resonances, which are called formants.
- 4. • A signal processing is an activity to extract a signal information. Linear Predictive Coding (LPC) is a powerful speech analysis technique and facilitating a features extraction which has a good quality and efficient result for computing. In 1978, LPC uses to make a speech synthesis. LPC doing an analysis with predicting a formant decided a formant from signal called inverse filtering, then estimated an intensity and frequency from residue speech signal. Because speech signal has many variations depending on a time, the estimation will do to cut a signal called frame
- 6. LPC METHODS
- 7. Preemphasis • On processing of speech signal, preemphasis filter needed after sampling process. The filtering purpose is to get a smooth spectral shape of the speech signal. A spectral which have a high value for the low-frequency field and decrease for field frequency higher than 2000 Hz. Preemphasis filter based on the relation of input/output on time domain which is shown by the equation (1), • • a is a constant of preemphasis filter, ordinary have 0.9 < a < 1.0.
- 8. Frame Blocking Frame Blocking: • On this process, segmented of speech signal become some frame which overlaps. So that no signal is lost (deletion).
- 9. Windowing. • Analog signal which converts become digital signal read frame by frame and each frame is windowing with the certain window function. This windowing process purpose to minimize discontinue signal from initial to end of each frame. If window as w(n), 0 ≤ n ≤ N – 1, when N is total of sample of each frame, thus result of windowing is a signal:
- 10. Auto-correlation Analysis • The next step is autocorrelation analysis toward each frame result by windowing y1 (n) with equation (4), • (4) Where p is ordered from LPC. LPC order which usually used is between 8 until 16.
- 11. • This step will convert each frame from p+1 autocorrelation become compilation of “LPC parameter” • This compilation becomes LPC coefficient or become other LPC transformation. The formal method to change autocorrelation coefficient become parameter LPC compilation called Durbin method, the form as:
- 15. LPC: Vocoder • It has two key components: analysis or encoding and synthesis or decoding. The analysis part of LPC involves examining the speech signal and breaking it down into segments or blocks. • Each segment is than examined further to find the answers to several key questions: • Is the segment voiced or unvoiced? • What is the pitch of the segment? • What parameters are needed to build a filter that models the vocal tract for the current segment? LPC analysis is usually conducted by a sender who answers these questions and usually transmits these answers onto a receiver.
- 17. LPC Decoder
- 18. • Each segment of speech has a different LPC filter that is eventually produced using the reflection coefficients and the gain that are received from the encoder. • 10 reflection coefficients are used for voiced segment filters and 4 reflection coefficients are used for unvoiced segments. These reflection coefficients are used to generate the vocal tract coefficients or parameters which are used to create the filter. • The final step of decoding a segment of speech is to pass the excitement signal through the filter to produce the synthesized speech signal.