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Journal ArticleDOI

Lossless compression of digital audio

TLDR
It is found that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio, and a new lossless Audio coder is described called AudioPak, which low algorithmic complexity and performs well or even better than most of the losslessaudio coders that have been described in the literature.
Abstract
Lossless audio compression is likely to play an important part in music distribution over the Internet, DVD audio, digital audio archiving, and mixing. The article is a survey and a classification of the current state-of-the-art lossless audio compression algorithms. This study finds that lossless audio coders have reached a limit in what can be achieved for lossless compression of audio. It also describes a new lossless audio coder called AudioPak, which low algorithmic complexity and performs well or even better than most of the lossless audio coders that have been described in the literature.

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Citations
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Proceedings ArticleDOI

eWatch: a wearable sensor and notification platform

TL;DR: The motivation for developing a wearable computing platform is provided, a description of the power aware hardware and software architectures are described, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations are shown.
Book ChapterDOI

Location and activity recognition using ewatch: a wearable sensor platform

TL;DR: An activity recognition and monitoring system that identifies the user's activity in realtime using multiple sensors is designed and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations are shown.
Book

Audio Signal Processing and Coding

TL;DR: This chapter discusses signal processing Essentials, audio Coding Standards and Algorithms, and quality measures for Perceptual Audio Coding.
Proceedings ArticleDOI

MPEG-4 ALS: an emerging standard for lossless audio coding

TL;DR: This paper provides a brief overview of an emerging ISO/IEC standard for lossless audio coding, MPEG-4 ALS, and explains the choice of algorithms used in its design, and compares it to current state-of-the-art algorithms for Lossless audio compression.
Journal Article

Audio Coding based on Integer Transforms

TL;DR: A new approach of applying the lifting scheme to lapped transforms used in perceptual audio coding allows for an invertible integer-tointeger approximation of the original transform, and an approach to data hiding with high data rates in uncompressed audio signals based on integer transforms is described.
References
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Journal Article

Run-length encodings.

TL;DR: Run-length encodings are used for determining explicit form of Huffman coding when applied to geometric distribution in the context of discrete geometry.
Journal ArticleDOI

Run-length encodings (Corresp.)

TL;DR: To obtain the reduction, the authors use operational relations [lo] to get The integral with the special parameters of (11) has been previously recognized as a Q function [12]-[14] so that the reduction is essentially complete.
Proceedings ArticleDOI

LOCO-I: a low complexity, context-based, lossless image compression algorithm

TL;DR: LOCO-I as discussed by the authors combines the simplicity of Huffman coding with the compression potential of context models, thus "enjoying the best of both worlds." The algorithm is based on a simple fixed context model, which approaches the capability of the more complex universal context modeling techniques for capturing high-order dependencies.
Journal Article

Optimal Source Codes for Geometrically Distributed Integer Alphabets

TL;DR: An approach is shown for using the Huffman algorithm indirectly to prove the optimality of a code for an infinite alphabet if an estimate concerning the nature of the code can be made.
Journal ArticleDOI

Optimal source codes for geometrically distributed integer alphabets (Corresp.)

TL;DR: In this paper, an optimal binary source code for probability assignment on the set of nonnegative integers is constructed as follows: P(i)= (1 - \theta)-theta)\theta^i be a probability assignment, where the real number is an arbitrary real number, 0.