Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top

2009 | Book

How is speech processed in a cell phone conversation? Read chapter Read first chapter

Applied Signal Processing

A MATLAB™-Based Proof of Concept

Authors: Thierry Dutoit, Ferran Marqués

Publisher: Springer US

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik"

Login to get access
insite
SEARCH

About this book

Applied Signal Processing: A MATLAB-Based Proof of Concept benefits readers by including the teaching background of experts in various applied signal processing fields and presenting them in a project-oriented framework. Unlike many other MATLAB-based textbooks which only use MATLAB to illustrate theoretical aspects, this book provides fully commented MATLAB code for working proofs-of-concept. The MATLAB code provided on the accompanying online files is the very heart of the material. In addition each chapter offers a functional introduction to the theory required to understand the code as well as a formatted presentation of the contents and outputs of the MATLAB code.

Each chapter exposes how digital signal processing is applied for solving a real engineering problem used in a consumer product. The chapters are organized with a description of the problem in its applicative context and a functional review of the theory related to its solution appearing first. Equations are only used for a precise description of the problem and its final solutions. Then a step-by-step MATLAB-based proof of concept, with full code, graphs, and comments follows. The solutions are simple enough for readers with general signal processing background to understand and they use state-of-the-art signal processing principles. Applied Signal Processing: A MATLAB-Based Proof of Concept is an ideal companion for most signal processing course books. It can be used for preparing student labs and projects.

Table of Contents

Frontmatter
Chapter 1. How is speech processed in a cell phone conversation?
Abstract
Although most people see the cell phone as an extension of conventional wired phone service or POTS (plain old telephone service), the truth is that cell phone technology is extremely complex and a marvel of technology. Very few people realize that these small devices perform hundreds of millions of operations per second to be able to maintain a phone conversation. If we take a closer look at the module that converts the electronic version of the speech signal into a sequence of bits, we see that for every 20 ms of input speech, a set of speech model parameters is computed and transmitted to the receiver. The receiver converts these parameters back into speech. In this chapter, we will see how linear predictive (LP) analysis- synthesis lies at the very heart of mobile phone transmission of speech. We first start with an introduction to linear predictive speech modeling and follow with a MATLAB-based proof of concept.
T. Dutoit, N. Moreau, P. Kroon
Chapter 2. How are bits played back from an audio CD?
Abstract
Loading a CD player with one’s favorite CD has become an ordinary action. It is taken for granted that the stream of 16-bit digital information it contains can easily be made available to our ears, i.e., in the analog world in which we live. The essential tool for this is the digital-to-analog converter (DAC).
T. Dutoit, R. Schreier
Chapter 3. How is sound processed in an MP3 player?
Abstract
In his 1929 painting “La trahison des images,” Belgian painter René Magritte highlighted the power of illusions by painting a pipe and commenting it with “Ceci n’est pas une pipe” (“This is not a pipe”) as Magritte himself explained: “Try to stuff the painting with tobacco… .“
T. Dutoit, N. Moreau
Chapter 4. How does a dictation machine recognize speech?
Abstract
There is magic (or is it witchcraft?) in a speech recognizer that transcribes continuous radio speech into text with a word accuracy of even not more than 50%. The extreme difficulty of this task, though, is usually not perceived by the general public. This is because we are almost deaf to the infinite acoustic variations that accompany the production of vocal sounds, which arise not only from physiological constraints (coarticulation) but also from the acoustic environment (additive or convolutional noise, Lombard effect) or from the emotional state of the speaker (voice quality, speaking rate, hesitations, etc.)2. Our consciousness of speech is indeed not stimulated until after it has been processed by our brain to make it appear as a sequence of meaningful units: phonemes and words.
T. Dutoit, L. Couvreur, H. Bourlard
Chapter 5. How does an audio effects processor perform pitch shifting?
Abstract
The old-fashioned charm of modified high-pitched voices has recently been revived by the American movie Alvin and the Chipmunks, based on the popular group and animated series of the same name which dates back to the 1950s. These voices were originally performed by recording speech spoken (or sung) slowly (typically at half the normal speed) and then accelerating (“pitching up”) the playback (typically at double the speed, thereby increasing the pitch by one octave). The same effect can now be created digitally and in real time; it is widely used today in computer music.
T. Dutoit, J. Laroche
Chapter 6. How can marine biologists track sperm whales in the oceans?
Abstract
Whale watching has become a trendy occupation these last years. It is carried on in the waters of some 40 countries, plus Antarctica. Far beyond its touristic aspects, being able to spot the position of whales in real time has several important scientific applications, such as censusing (estimation of animal population), behavior studies, and mitigation efforts concerning fatal collisions between marine mammals and ships and exposure of marine mammals to loud sounds of anthropogenic origin (seismic surveys, oil and gas exploitation and drilling, naval and other uses of sonar).
T. Dutoit, V. Kandia, Y. Stylianou
Chapter 7. How could music contain hidden information?
Abstract
Audio watermarking started in the 1990s as a modern and very technical version of playing cat and mouse.1 The music industry, dominated by the “big four” record groups, also known as the “Majors” (Sony BMG, EMI, Universal, and Warner), quickly realized that the availability of digital media for music recordings and the possibility to transfer it fast and degradation-free (thanks to the availability of high data-rate networks and of efficient data compression standards) would not only offer many benefits in terms of market expansion, but also expose their business to a great danger: that of piracy of intellectual property rights. Being able to insert proprietary marks in the media without affecting audio quality (i.e., in a “transparent way”) was then recognized as a first step toward solving this issue. Additionally, ensuring the robustness of the proprietary mark to not only to usual media modifications (such as cropping, filtering, gain modification, or compression) but also to more severe piracy attacks quickly became a hot research topic worldwide.
C. Baras, N. Moreau, T. Dutoit
Chapter 8. How are digital images compressed in the web?
Abstract
In 1992, a joint committee between the International Organization for Standardization (ISO) and the Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T) known as the Joint Photographic Experts Group (JPEG) issued a standard that was approved in 1994 as ISO/IEC 10918-1 or ITU-T Recommendation T.8l. This standard received the name of Information technology — Digital compression and coding of continuous-tone still images requirements and guidelines but it is commonly known as the JPEG standard.
F. Marqués, M. Menezes, J. Ruiz-Hidalgo
Chapter 9. How are digital TV programs compressed to allow broadcasting?
Abstract
In 1982, the CCIR defined a standard for encoding interlaced analogue video signals in digital form mainly for studio applications. The current name of this standard is ITU-R BT.601 (ITU 1983). Following this standard, a video signal sampled at 13.5 MHz with a 4:2:2 sampling format (double the number of samples for the luminance component than for the two chrominance components) and quantized with 8 bits per component produces a raw bit rate of 216 Mbps. This rate can be reduced by removing the blanking intervals present in the interlaced analogue signal leading to a bit rate of 166 Mbps, which is still a figure far above the main capacity of usual transmission channels or storage devices.
F. Marqués, M. Menezes, J. Ruiz-Hidalgo
Chapter 10. How does digital cinema compress images?
Abstract
The development of digital technologies has drastically modified the requirements and constraints that a good image representation format should meet. Originally, the requirements were to achieve good compression efficiency while keeping the computational complexity low. This has led in 1992 to the standardization of the JPEG format, which is still widely used today (see Chapter 8). Over the years however, many things have evolved: more computing power is available and the development of Internet has required image representation formats to be more flexible and network- oriented, to enable efficient access to images through heterogeneous devices.
A. Descampe, C. De Vleeschouwer, L. Jacques, F. Marqués
Chapter 11. How can physicians quantify brain degeneration?
Abstract
Life expectation is increasing every year. Along with this aging population, the risk of neurological diseases (e.g., dementia)1 is considerably increasing2 as well. Such disorders of the human nervous system affect the patient from both a physical and a social point of view, as in the case of Alzheimer’s disease, one of the most known brain disorders (Mazziotta et al. 2000).
M. Bach Cuadra, J.-Ph. Thiran, F. Marqués
Backmatter
Metadata
Title
Applied Signal Processing
Authors
Thierry Dutoit
Ferran Marqués
Copyright Year
2009
Publisher
Springer US
Electronic ISBN
978-0-387-74535-0
Print ISBN
978-0-387-74534-3
DOI
https://doi.org/10.1007/978-0-387-74535-0