Z24 4 Speech Compression
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The document discusses several speech compression techniques: 1) Uncompressed audio rates are 64kbps for voice and 1.5Mbps for CD audio. 2) ADPCM reduces the data rate to 32kbps by transmitting differences between predicted and actual sample values. 3) SB-ADPCM splits audio into lower and upper sub-bands, compressing each at different rates (48kbps and 16kbps respectively) for total of 64kbps. 4) LPC represents speech as a combination of previous samples and residual error, transmitting just coefficients, enabling lower rates like those in GSM and other standards.
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![Linear Predictive Coding (LPC) LPC
• Introduced in the 1960s • coefficients (‘a’s) correspond to those of a vocal
• nth signal sample is represented as a linear tract filter and the error signal (‘e’) corresponds to
combination of the previous p samples, plus a a source signal
residual representing the prediction error: • Source signal will approximate either a voiced
signal (which looks like a series of impulses) or a
x(n) = a1x(n-1) + a2x(n-2) + … + apx(n-p) + e(n)
white noise source
• So, LPC involves “exciting” a source signal with a
• If the error (‘e’) is small enough, we can just
transmit the coefficients (‘a’s) vocal tract filter
Impulses and Filters LPC – Autocorrelation
• Minimise the error signal by choosing optimal
coefficients (‘a’s)
• Use the autocorrelation criteria (aka root mean
squared criterion):
for 1<=j<=p,
where R is the autocorrelation of x(n) defined as
R(i) = E[x(n)x(n-i)]
LPC – Solving the autocorrelation
LPC
formula
• In matrix form the equation can be written as • Used in:
R*a=r
– GSM (Groupe Speciale Mobile) (Residual
where the autocorrelation matrix R is a symmetric Toeplitz Pulse Excited-LPC) (13kbps)
matrix with elements ri,j = R(i - j), vector r is the
autocorrelation vector rj = R(j), and vector a is the – LD-CELP (Low-Delay Code Excited Linear
parameter vector of ai Prediction) (G.728) (16kbps)
• An algorithm by N. Levinson (proposed in 1947) and – CS-ACELP (Conjugate Structure-Algebraic
modified by J. Durbin (in 1959) recursively calculates the CELP) (G.729) (8kbps)
solution to the Toeplitz matrix.
• GSM coder uses an integer version of the Schur recursion – MP-MLQ (Multi Pulse – Maximum Likelihood
(1917) Quantisation) (G.723.1) (6.3kbps)…
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Pulse code modulation (PCM) is a method of digitally representing sampled analog signals. In PCM, the instantaneous voltage of an analog signal is sampled regularly at uniform intervals, then quantized to a series of digital codes. This allows the analog signals to be transmitted over digital communication networks or stored in digital memory. Key aspects of PCM include sampling the analog signal, quantizing the samples to discrete levels, encoding the quantized levels into binary code words, and transmitting the encoded binary data as a serial bit stream. PCM provides advantages like noise immunity, easy processing and storage, and the ability to multiplex and transmit multiple signals over the same channel.
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.
DC_PPT.pptx
This document discusses digital communication and modulation techniques. It covers topics such as random variables, digital communication basics, digital modulation schemes including ASK, FSK, PSK, and their demodulation. It also discusses digital receivers, information theory concepts like entropy and channel capacity, and error correction coding methods like linear block codes and convolutional codes. The key aspects of digital communication systems like sampling, quantization, line coding, and modulation methods including PCM and delta modulation are explained.
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1. The document discusses various waveform coding techniques including PCM, DPCM, ADPCM, DM, and LPC. PCM involves sampling and quantizing an analog signal. DPCM and ADPCM encode differences between signal values to reduce bitrate. DM encodes changes in signal amplitude. LPC models speech as a linear system to remove redundancy.
2. LPC uses linear prediction to estimate future signal values from previous samples. The prediction error is minimized to find predictor coefficients. Different algorithms like autocorrelation, covariance, and reflection solve the LPC equations.
3. LPC parameters like reflection coefficients, line spectral frequencies, and impulse response relate to each other mathematically. The roots of the
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.
Waveform_codingUNIT-II_DC_-PPT.pptx
This document provides an overview of waveform coding techniques including prediction filtering, DPCM, delta modulation, ADPCM, LPC, and their principles. It discusses key concepts such as:
1. DPCM and ADPCM encode the difference between actual and predicted sample values, allowing lower bit rates by exploiting redundancy between samples.
2. Delta modulation uses adaptive quantization step sizes determined by the slope of the input signal to improve quality.
3. Linear predictive coding estimates future sample values as a linear combination of past samples to model the signal generation process and extract prediction errors for coding.
4. Various algorithms like Durbin and Schur can solve the autocorrelation or covariance matrices to determine the
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.
Ncc2004 ofdm tutorial part ii-apal
This document summarizes design issues in OFDM systems. It discusses the OFDM modem block diagram and signal description. It then focuses on key receiver design issues like time and frequency synchronization through techniques like frequency offset correction, carrier phase tracking, and channel equalization. It also addresses signal dynamic range issues and how they are handled in standards like IEEE 802.11a/g. The document provides illustrations of effects of different impairments and solutions used in practical OFDM systems.
Acoustic echo cancellation
This document summarizes adaptive signal processing techniques for acoustic echo cancellation. It defines acoustic echo as sound from a loudspeaker picked up by a microphone in the same room. Acoustic echo cancellation uses an adaptive filter to model the echo path and subtract the predicted echo from the microphone signal. The document reviews common adaptive algorithms for echo cancellation, including LMS, NLMS, RLS, APA, FAP, and VSS-APA, comparing their convergence speed, complexity, and performance in different noise conditions. FAP provides faster convergence than NLMS for speech signals while having lower complexity than APA. VSS-APA uses variable step sizes to improve performance during double-talk and under-modeling scenarios.
Arithmetic Coding
The document provides an overview of digital media basics including digitization, compression, representation, and standards. It discusses signal digitization through pulse code modulation and sampling. It also covers quantization, digitization examples, and lossless versus lossy compression. Specific compression techniques covered include transform coding, variable rate coding, predictive coding, and entropy coding like Huffman coding. The document also discusses psychoacoustic modeling and perceptual coding. It provides examples of speech, audio, image and video compression standards and techniques.
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.
Software-defined white-space cognitive systems: implementation of the spectru...
S. Benco, F. Crespi, A. Ghittino, A. Perotti, "Software-defined
white-space cognitive systems: implementation of the spectrum sensing
unit", Proceedings of the 2nd International Workshop of COST Action
IC0902 October 5–7 2011, Castelldefels and Barcelona, Spain
Analog mixed vlsi notes
The document provides an overview of data converters and their fundamentals. It discusses:
1) Why data conversion is needed to interface between analog and digital domains.
2) The basic types of data converters - analog-to-digital converters (ADCs) and digital-to-analog converters (DACs).
3) Key parameters that characterize data converters like resolution, sampling frequency, signal-to-noise ratio, effective number of bits, and more.
It provides details on the building blocks, working principles, and specifications of ADCs and DACs. Uniform sampling and quantization techniques are also covered.
A1mpeg12 2004
The document discusses MPEG audio coding standards. Key points:
- MPEG-1 and MPEG-2 standards support audio compression for low bitrate streaming. MPEG-1 supports mono, stereo up to 48KHz. MPEG-2 adds support for 5.1 surround sound and lower sampling rates.
- Layer 1 is a basic implementation while Layers 2 and 3 provide increased compression using psychoacoustic models and more advanced quantization techniques. Layer 3 (MP3) is commonly used.
- Psychoacoustic models estimate masking thresholds to allow quantization noise below audible levels, improving compression ratios. Critical band models approximate the ear's frequency resolution.
Carrier to Noise Versus Signal to Noise.ppt
The document discusses the differences between carrier-to-noise ratio (CNR) and signal-to-noise ratio (SNR). CNR is generally a pre-detection measurement made at RF, measuring the power of a carrier signal relative to noise in a specified bandwidth. SNR is usually a post-detection measurement made on a baseband signal, measuring the power of the signal relative to noise within that signal. For digital cable systems, modulation error ratio (MER) and error vector magnitude (EVM) provide better metrics than SNR for characterizing modulation quality and estimating bit error rate.
03_04-AnalogDigital-HYanikomeroglu-12Jan2011_14Jan2011_Old1.ppt
This document discusses analog and digital signals and pulse code modulation (PCM) systems for digital transmission. It covers key topics such as:
1) Analog signals are continuous over time and amplitude, while digital signals are discrete. PCM allows analog signals to be represented and transmitted digitally via sampling, quantizing, and encoding.
2) The sampling theorem states an analog signal can be reconstructed if sampled at twice its bandwidth. PCM systems sample voice bandwidth signals at 8 kHz and quantize samples with 8-bit coding.
3) Digital transmission provides advantages over analog like reduced noise sensitivity and lower maintenance costs. PCM and techniques like ADPCM and regenerative repeating allow transmission of voice and data over
Logsv2
This document provides an overview of computer networks, including different topologies for high-speed switching fabrics, common transmission mediums like twisted pair, fiber optics, radio, and Ethernet coax. It also reviews concepts like logarithms, channel capacity, the Hartley-Shannon law, and the seven layers of the OSI model from the physical layer to the application layer. Key networking technologies and protocols are defined at each layer of the OSI model.
05 signal encodingtechniques
This document discusses various techniques for encoding digital signals for transmission, including:
1) Non-return-to-zero (NRZ) encoding schemes which use different voltage levels to represent 1s and 0s without returning to a baseline between bits.
2) Manchester and differential Manchester encoding which add transitions in the middle or start of each bit to provide clocking functionality.
3) Phase-shift keying (PSK) and quadrature PSK (QPSK) which represent data by shifting the phase of the carrier signal.
4) Amplitude-shift keying (ASK), frequency-shift keying (FSK), and quadrature amplitude modulation (QAM) which are used to transmit
An audio quality evaluation of digital radio system
This document provides an audio quality evaluation of digital radio systems. It compares analog radio systems to proposed digital systems in terms of audio quality, channel capacity, spectrum efficiency and more. The evaluation involves collecting audio samples, encoding them using different codecs and bitrates, transmitting them over various digital radio systems, then having listeners rate the quality. The goal is to determine the optimal bitrate needed for a given audio quality across different digital radio systems.
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An audio quality evaluation of digital radio system
An audio quality evaluation of digital radio system
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The document discusses Shannon-Fano encoding, which is an early method for data compression that constructs efficient binary codes for information sources without memory. It works by assigning shorter codes to more frequent messages and longer codes to less frequent messages. The encoding process involves recursively splitting the set of messages in half based on probability until each message has its own unique code. While Shannon-Fano codes are reasonably efficient, they are not always optimal and the codes generated can depend on how the initial splitting of messages is done.
Ch 04 Arithmetic Coding ( P P T)
Arithmetic coding is an entropy encoding technique that maps a sequence of symbols to a number between 0 and 1. Each possible sequence is assigned a unique interval within this range. As symbols are processed, the interval boundaries are updated based on the symbol probabilities. This allows arithmetic coding to efficiently encode sequences without needing to pre-determine codes for all possible sequences. It can achieve compression close to the entropy limit for long sequences, and easily supports adaptive and context modeling to handle non-IID sources. The interval updates ensure a unique number can be sent to decode the full sequence.
Compression
The document discusses different types of data compression techniques. It explains that compression reduces the size of data to reduce bandwidth and storage requirements when transmitting or storing audio, video, and images. It describes how compression works by exploiting redundancies in the data as well as properties of human perception. Various compression methods are outlined, including lossless techniques that preserve all information as well as lossy methods for audio and video that can tolerate some loss of information. Specific algorithms discussed include Huffman coding, run-length coding, LZW coding, and arithmetic coding. The document also provides details on JPEG image compression and MPEG video compression standards.
Datacompression1
The document discusses entropy and Shannon's first theorem. It defines entropy as the average amount of information received per symbol from a source where symbols have different probabilities. Entropy measures the uncertainty in the probability distribution of the source. The entropy of a source is equal to the minimum expected code length needed to encode symbols from that source.
Speech Compression
This document summarizes various audio compression techniques used to reduce the required storage space and transmission bandwidth for digital audio. It discusses lossless compression techniques that remove redundant data without degrading quality, as well as lossy techniques that remove inaudible or irrelevant information, resulting in smaller file sizes but some loss of quality. The key techniques described include psychoacoustic modeling to determine inaudible components, spectral analysis using transforms or filter banks, noise allocation to minimize quantization noise, and additional methods like predictive coding, coupling/delta encoding, and Huffman coding.
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The document discusses different data compression techniques, focusing on lossless dictionary-based compression algorithms like Lempel-Ziv (LZ). It explains how LZ algorithms like LZW work by building an adaptive dictionary during compression and decompression. The LZW algorithm is described through examples. Advantages of LZW include no need to explicitly transfer the dictionary and fast encoding/decoding through table lookups. Problems like running out of dictionary space are also addressed.
Dictionary Based Compression
This document discusses various data compression techniques. It begins with an introduction to compression and its goals of reducing storage space and transmission time. Then it discusses lossless techniques like Huffman coding, Lempel-Ziv coding, run-length encoding and pattern substitution. The document also briefly covers lossy compression and entropy encoding algorithms like Shannon-Fano coding and arithmetic coding. Key compression methods and their applications are summarized throughout.
Module 4 Arithmetic Coding
This document discusses arithmetic coding, a technique for data compression. It begins by explaining how real numbers can be represented in binary and how this relates to encoding strings based on their probabilities. The document then provides examples of how to generate codes for strings and explains the coding and decoding algorithms. It discusses issues like scaling, integer arithmetic coding, adaptation of probabilities, and handling zero frequency and end of file problems. In the end, it compares arithmetic coding to Huffman coding, noting their similarities and differences in terms of compression performance, context handling, and adaptation.
Ch 04 Arithmetic Coding (Ppt)
Arithmetic coding is an entropy encoding technique that maps a sequence of symbols to a numeric interval between 0 and 1. Each symbol maps to a sub-interval of the current interval based on the symbol probabilities. As symbols are processed, the interval boundaries are updated according to the cumulative distribution function of the symbol probabilities. Arithmetic coding achieves better compression than Huffman coding by allowing coding of variable-length blocks without pre-specifying code lengths. It also handles conditional probability models more efficiently by updating interval boundaries based on context without needing pre-specified codebooks for all contexts.
Compression Ii
The document discusses various techniques for image compression including:
1) Variable length coding which reduces the first order entropy but not the second order entropy due to inter-pixel redundancy.
2) Predictive coding which codes only the difference between actual and predicted pixel values to remove inter-pixel redundancy.
3) Lossy compression techniques like transform coding and delta modulation which allow for higher compression ratios than lossless techniques by introducing some loss of information.
06 Arithmetic 1
This document discusses arithmetic coding, an entropy encoding technique. It begins with an introduction comparing arithmetic coding to Huffman coding. The document then provides pseudocode for the basic encoding and decoding algorithms. It describes how scaling techniques like E1 and E2 scaling allow for incremental encoding and decoding as well as achieving infinite precision with finite-precision integers. The document outlines applications of arithmetic coding in areas like JBIG, H.264, and JPEG 2000.
Lassy
The document discusses lossy compression techniques. It begins by explaining that lossy compression algorithms compress data by discarding some information, yielding much higher compression ratios than lossless compression but resulting in distorted approximations of the original data. It then covers various lossy compression methods including quantization, transform coding using the discrete cosine transform (DCT), wavelet-based coding using the discrete wavelet transform (DWT), and techniques like vector quantization (VQ) and the Karhunen-Loeve transform (KLT) that aim to decorrelate signal components before quantization. Key aspects like rate-distortion theory, various distortion measures, and algorithms for quantization are also described.
Compression Ii
The document discusses various image compression techniques including:
1) Variable length coding such as Huffman coding which reduces redundancy but is not optimal for the sample image due to inter-pixel redundancy.
2) Predictive coding which encodes pixel differences to remove redundancy, achieving higher compression ratios.
3) Lossy techniques like JPEG use quantization and discrete cosine transform to compress images further, introducing some distortion.
Lossy
The document discusses lossy compression techniques such as quantization and transform coding. It explains that lossy compression achieves higher compression rates by introducing some loss or distortion to the reconstructed data compared to the original. It describes various distortion measures used to evaluate the quality of lossy compression, including mean squared error and peak signal-to-noise ratio. It also discusses the discrete cosine transform, a widely used transform coding technique that decomposes images or signals into different frequency components for more efficient compression.
Planning
The document discusses various aspects of project planning and control, including defining control variables, product/process/resource characteristics, different control situations, and techniques for project planning and control. It covers archetypical control situations like realization, allocation, design, and exploration problems. Risk management strategies are also summarized, including identifying risk factors, determining risk exposure, and developing risk mitigation strategies.
Lossless
The document discusses various lossless compression techniques including entropy coding methods like Huffman coding and arithmetic coding. It also covers dictionary-based coding like LZW, as well as spatial compression techniques like run-length coding, quadtrees for images, and lossless JPEG.
Losseless
This document discusses lossless data compression and introduces key concepts such as codecs, prefix codes, entropy, and Shannon's source coding theorem. Specifically:
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- A codec consists of a coder that maps messages to codewords and a decoder that maps codewords back to the original messages. Prefix codes are codes where no codeword is a prefix of another.
- Entropy measures the uncertainty in a probabilistic source, with the entropy of a distribution being the expected self-entropy of messages drawn from that distribution.
- Shannon's source coding theorem states that the expected
Lec32
The document discusses Huffman coding, a lossless data compression algorithm that uses variable-length codewords to encode symbols based on their frequency of occurrence. It works by building a binary tree from the frequency of symbols, where more frequent symbols are encoded by shorter codewords. This allows for more efficient representation of frequent symbols and achieves compression close to the theoretical minimum possible given the frequencies. The algorithm and encoding/decoding process are explained step-by-step with an example.
Lec5 Compression
1. The document discusses different compression techniques for text, audio, images, and video.
2. It provides examples of compression ratios achieved using lossy and lossless compression methods. For example, text compression can achieve 3:1 ratios using Lempel-Ziv coding while audio compression can achieve ratios between 3:1 to 24:1 using MP3.
3. The techniques discussed include entropy encoding, run-length encoding, Huffman coding, discrete cosine transforms, and differential encoding which takes advantage of redundancies in the data. The best approach depends on the type of data and acceptable quality.
Huffman Student
The document discusses Huffman coding, which is a variable-length binary coding technique used for lossless data compression. It describes how Huffman codes are constructed using a Huffman tree to assign codewords to symbols based on their frequency of occurrence, with more frequent symbols assigned shorter codewords. The document outlines the Huffman coding algorithm and provides examples of how it works to generate an optimal prefix code with the minimum average codeword length.
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Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...
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Z24 4 Speech Compression
- 1. Speech Compression Uncompressed audio data rates • Recommended Reading: J. Harrington and S. • Voice: 8000samples/sec, 8bits/sample, Cassidy, “Techniques in Speech Acoustics”, mono Kluwer, 1999 = 64000bits/sec (64kbps) • Contents • CD: 44100samples/sec, 16bits/sample, – Uncompressed audio data rates stereo – ADPCM – SB-ADPCM =1411200bits/sec (~1.5Mbps) – LPC ADPCM (Adaptive Differential PCM) ADPCM • Uses the statistical properties of human speech (=> not Measured Transmitted compatible with fax/modem signals) value value • Makes a prediction about the size of the next sample, based Adaptive on previous info quantiser - • Transmitter then sends only the difference between real value and predicted value Predictor • Receiver uses the same prediction algorithm, together with the differences to reconstruct the speech data • Enables the data rate to be reduced to 32kbps • Used on international telephone links • Specified in G.721, G.722, G.723, G.726, G.727 SB-APDCM (Sub-band ADPCM) SB-ADPCM • Given 64kbps: ADPCM could produce Upper sub-band ADPCM encoder better than toll voice quality (eg radio) Input 4-7KHz 16kbps MUX • Sub-bands are 0-4kHz (given 48kbps), 4- filters 7kHz (given 16kbps) Lower sub-band ADPCM encoder • Low band contains more audio energy, high 50Hz-4KHz 48kbps band contains intelligibility info. • Standardised in G.722 Analogue Digital signal signal in out 1
- 2. Linear Predictive Coding (LPC) LPC • Introduced in the 1960s • coefficients (‘a’s) correspond to those of a vocal • nth signal sample is represented as a linear tract filter and the error signal (‘e’) corresponds to combination of the previous p samples, plus a a source signal residual representing the prediction error: • Source signal will approximate either a voiced signal (which looks like a series of impulses) or a x(n) = a1x(n-1) + a2x(n-2) + … + apx(n-p) + e(n) white noise source • So, LPC involves “exciting” a source signal with a • If the error (‘e’) is small enough, we can just transmit the coefficients (‘a’s) vocal tract filter Impulses and Filters LPC – Autocorrelation • Minimise the error signal by choosing optimal coefficients (‘a’s) • Use the autocorrelation criteria (aka root mean squared criterion): for 1<=j<=p, where R is the autocorrelation of x(n) defined as R(i) = E[x(n)x(n-i)] LPC – Solving the autocorrelation LPC formula • In matrix form the equation can be written as • Used in: R*a=r – GSM (Groupe Speciale Mobile) (Residual where the autocorrelation matrix R is a symmetric Toeplitz Pulse Excited-LPC) (13kbps) matrix with elements ri,j = R(i - j), vector r is the autocorrelation vector rj = R(j), and vector a is the – LD-CELP (Low-Delay Code Excited Linear parameter vector of ai Prediction) (G.728) (16kbps) • An algorithm by N. Levinson (proposed in 1947) and – CS-ACELP (Conjugate Structure-Algebraic modified by J. Durbin (in 1959) recursively calculates the CELP) (G.729) (8kbps) solution to the Toeplitz matrix. • GSM coder uses an integer version of the Schur recursion – MP-MLQ (Multi Pulse – Maximum Likelihood (1917) Quantisation) (G.723.1) (6.3kbps)… 2