Digital audio, video, images, and documents are flying through cyberspace to their respective owners. Unfortunately, along the way, individuals may choose to intervene and take this content for themselves. Digital watermarking and steganography technology greatly reduces the instances of this by limiting or eliminating the ability of third parties to decipher the content that he has taken. The many techiniques of digital watermarking (embedding a code) and steganography (hiding information) continue to evolve as applications that necessitate them do the same. The authors of this second edition provide an update on the framework for applying these techniques that they provided researchers and professionals in the first well-received edition. Steganography and steganalysis (the art of detecting hidden information) have been added to a robust treatment of digital watermarking, as many in each field research and deal with the other. New material includes watermarking with side information, QIM, and dirty-paper codes. The revision and inclusion of new material by these influential authors has created a must-own book for anyone in this profession.

*This new edition now contains essential information on steganalysis and steganography
*New concepts and new applications including QIM introduced
*Digital watermark embedding is given a complete update with new processes and applications

Digital Watermarking and Steganography

Media objects are transformed into active, connected objects via identifiers embedded into them or their containers. In the context of a user's playback experience, a decoding process extracts the identifier from a media object and possibly additional context information and forwards it to a server. The server, in turn, maps the identifier to an action, such as returning metadata, re-directing the request to one or more other servers, requesting information from another server to identify the media object, etc. The linking process applies to broadcast objects as well as objects transmitted over networks in streaming and compressed file formats.

Connected audio and other media objects

Imagine the following situation. You’re in your car, listening to the radio and suddenly you hear a song that catches your attention. It’s the best new song you have heard for a long time, but you missed the announcement and don’t recognize the artist. Still, you would like to know more about this music. What should you do? You could call the radio station, but that’s too cumbersome. Wouldn’t it be nice if you could push a few buttons on your mobile phone and a few seconds later the phone would respond with the name of the artist and the title of the music you’re listening to? Perhaps even sending an email to your default email address with some supplemental information. In this paper we present an audio fingerprinting system, which makes the above scenario possible. By using the fingerprint of an unknown audio clip as a query on a fingerprint database, which contains the fingerprints of a large library of songs, the audio clip can be identified. At the core of the presented system are a highly robust fingerprint extraction method and a very efficient fingerprint search strategy, which enables searching a large fingerprint database with only limited computing resources.

https://ismir2002.ismir.net/proceedings/02-FP04-2.pdf

A Highly Robust Audio Fingerprinting System.

The steep rise in music downloading over CD sales has created a major shift in the music industry away from physical media formats and towards online products and services. Music is one of the most popular types of online information and there are now hundreds of music streaming and download services operating on the World-Wide Web. Some of the music collections available are approaching the scale of ten million tracks and this has posed a major challenge for searching, retrieving, and organizing music content. Research efforts in music information retrieval have involved experts from music perception, cognition, musicology, engineering, and computer science engaged in truly interdisciplinary activity that has resulted in many proposed algorithmic and methodological solutions to music search using content-based methods. This paper outlines the problems of content-based music information retrieval and explores the state-of-the-art methods using audio cues (e.g., query by humming, audio fingerprinting, content-based music retrieval) and other cues (e.g., music notation and symbolic representation), and identifies some of the major challenges for the coming years.

/pdf/content-based-music-information-retrieval-current-directions-59qqdfprz9.pdf

Content-Based Music Information Retrieval: Current Directions and Future Challenges

The present technology concerns cell phones and other portable devices, and more particularly concerns use of such devices in connection with media content (electronic and physical) and with other systems (e.g., televisions, digital video recorders, and electronic program directories). Some aspects of the technology allow users to easily transfer displayed content from cell phone screens onto a television screens for easier viewing, or vice versa for content portability. Others enable users to participate interactively in entertainment content, such as by submitting plot directions, audio input, character names, etc., yielding more engaging, immersive, user experiences. Still other aspects of the technology involve a program directory database, compiled automatically from information reported by network nodes that watch and identify content traffic passing into (and/or out of) networked computers. By identifying content resident at a number of different repositories (e.g., web sites, TV networks, P2P systems, etc.), such a directory allows cell phone users to identify the diversity of sources from which desired content can be obtained—some available on a scheduled basis, others available on demand. Depending on the application, the directory information may be transparent to the user—serving to identify sources for desired content, from which application software can pick for content downloading, based, e.g., on context and stored profile data. A great number of other features and arrangements are also detailed.

Media processing methods and arrangements

An audio fingerprint is a compact content-based signature that summarizes an audio recording. Audio Fingerprinting technologies have attracted attention since they allow the identification of audio independently of its format and without the need of meta-data or watermark embedding. Other uses of fingerprinting include: integrity verification, watermark support and content-based audio retrieval. The different approaches to fingerprinting have been described with different rationales and terminology: Pattern matching, Multimedia (Music) Information Retrieval or Cryptography (Robust Hashing). In this paper, we review different techniques describing its functional blocks as parts of a common, unified framework.

/pdf/a-review-of-audio-fingerprinting-udd4kr4d4m.pdf

A Review of Audio Fingerprinting

A system capable of describing the musical content of any kind of sound file or sound stream, as it is supposed to be done in MPEG7-compliant applications, should provide an account of the different moments where a certain instrument can be listened to. In this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. As most of the techniques to be discussed are usable only in "solo" performances we will evaluate their applicability to the more complex case of describing sound mixes. We conclude this survey discussing the necessity of developing new strategies for classifying sound mixes without a priori separation of sound sources.

/pdf/towards-instrument-segmentation-for-music-content-gyihgiq8ba.pdf

Towards instrument segmentation for music content description a critical review of instrument classification techniques

This paper describes the development of an audio fingerprint called AudioDNA designed to be robust against several distortions including those related to radio broadcasting. A complete system, covering also a fast and efficient method for comparing observed fingerprints against a huge database with reference fingerprints is described. The promising results achieved with the first prototype system observing music titles as well as commercials are presented. INTRODUCTION A monitoring system able to automatically generate play lists of registered songs can be a valuable tool for copyright enforcement organizations and for companies reporting statistics on the music broadcast. Technology able to listen to audio in order to identify songs without using external metadata or embedded watermarking is commonly known as Audio Fingerprinting. In a general Audio Fingerprinting scheme, the system generates a unique fingerprint of audio material based on an analysis of the acoustic properties of the audio itself. The fingerprint is compared against a database of fingerprints to identify a song. The difficulty inherent in the task of identifying broadcast audio material is mainly due to the difference of quality of the original titles, usually stored on Audio CD and the quality of the broadcast ones. The song is transmitted partially, the presenter talks on top of different fragments, the piece is maybe played faster and several manipulation effects are applied to increase the listener’s psychoacoustic impact (compressors, enhancers, equalization, bassbooster, etc). Moreover, in broadcast audio streams there are no markers indicating the begin and the end of the songs. Such a system also has to be fast because it must do comparisons with several thousand songs. This affects the memory and computation requisites since the system should observe several radio stations, give results on-line and should not be very expensive in terms of hardware. The paper describes an approach to the problem. After introducing the concept of audio fingerprinting, it proposes a modeling of audio aimed at being robust to different distortions in an adverse environment: radio broadcast. Along with an explanation of the CANO ET AL. ROBUST MODELING FOR SONG DETECTION AES 112 CONVENTION, MUNICH, GERMANY, 2002 MAY 10–13 2 fingerprint matching algorithms, we present some preliminary results and conclusions. AUDIO FINGERPRINTING Fingerprinting, or content-based identification (CBID), technologies work by extracting acoustic relevant characteristics of a piece of audio content and storing them in a database. When presented with an unidentified piece of audio content, characteristics of that piece are calculated and matched against those stored in the database. Using complex matching algorithms and acoustic fingerprints different versions of a single recording can be identified as the same music title [1], [2]. This is different to an alternative existing solution to monitor audio content: Audio Watermarking. In Audio Watermarking [3], research on psychoacoustics is conducted so that an arbitrary message, the watermark, can be embedded in a recording without altering the perception of the sound. In Audio Fingerprinting, the message is automatically derived from the perceptually most relevant components of sound. This makes it less vulnerable in theory to attacks and distortions since trying to modifying this message, the fingerprint, means alteration of the quality of the sound [4]. Independently of the specific approach to extract the content-based compact signature, a common architecture can be devised to describe the functionality of fingerprinting [1]. METADATA: Track Id Artist

Robust Sound Modeling for Song Detection in Broadcast Audio

Although not a new issue, music piracy has acquired a new status in the digital era, as recordings can be easily copied and distributed. Watermarking has been proposed as a solution to this problem. It consists in embedding into the audio signal an inaudible mark containing copyright information. A different approach, called fingerprinting, consists in extracting a “fingerprint” from the audio signal. In association with a database, this fingerprint can be used to identify a recording, which is useful, for example, to monitor audio excerpts played by broadcasters and webcasters. There are far more applications to watermarking and fingerprinting. After a brief technical review, this article describes potential applications of both methodologies, showing which one is more suitable for each application.

/pdf/audio-watermarking-and-fingerprinting-for-which-applications-jls3q34izs.pdf

Audio watermarking and fingerprinting: for which applications?

An audio fingerprint is a unique and compact digest derived from perceptually rele- vant aspects of a recording. Fingerprinting technologies allow the monitoring of audio content without the need of metadata or watermark embedding. However, additional uses exist for audio fingerprinting. This paper aims to give a vision on Audio Fingerprinting. The rationale is presented along with the differences with respect to watermarking. The main requirements of fingerprinting systems are described. The basic modes of employing audio fingerprints, namely identification, authentication, content-based secret key generation for watermarking and content-based audio retrieval and processing are depicted. Some concrete scenarios and business models where the technology is used are presented, as well as an example of an au- dio fingerprinting extraction algorithm which has been proposed for both identification and verification.

Eloi Batlle

Papers

A Review of Audio Fingerprinting

Towards instrument segmentation for music content description a critical review of instrument classification techniques

Robust Sound Modeling for Song Detection in Broadcast Audio

Audio watermarking and fingerprinting: for which applications?

Audio Fingerprinting Concepts and Applications