The eWatch is a wearable sensing, notification, and computing platform built into a wrist watch form factor making it highly available, instantly viewable, ideally located for sensors, and unobtrusive to users. Bluetooth communication provides a wireless link to a cellular phone or stationary computer. eWatch senses light, motion, audio, and temperature and provides visual, audio, and tactile notification. The system provides ample processing capabilities with multiple day battery life enabling realistic user studies. This paper provides the motivation for developing a wearable computing platform, a description of the power aware hardware and software architectures, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations.

/pdf/ewatch-a-wearable-sensor-and-notification-platform-2c8aoauiiv.pdf

eWatch: a wearable sensor and notification platform

The eWatch is a wearable sensing, notification, and computing platform built into a wrist watch form factor making it highly available, instantly viewable, ideally located for sensors, and unobtrusive to users. Bluetooth communication provides a wireless link to a cellular phone or stationary computer. eWatch senses light, motion, audio, and temperature and provides visual, audio, and tactile notification. The system provides ample processing capabilities with multiple day battery life enabling realistic user studies. This paper provides the motivation for developing a wearable computing platform, a description of the power aware hardware and software architectures, and results showing how online nearest neighbor classification can identify and recognize a set of frequently visited locations. We then design an activity recognition and monitoring system that identifies the user's activity in realtime using multiple sensors. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.

Location and activity recognition using ewatch: a wearable sensor platform

Preface. 1. Introduction. 2. Signal Processing Essentials. 3. Quantization and Entropy Coding. 4. Linear Prediction in Narrowband and Wideband Coding. 5. Psychoacoustic Principles. 6. Time-Frequency Analysis: Filter Banks and Transforms. 7. Transform Coders. 8. Subband Coders. 9. Sinusoidal Coders. 10. Audio Coding Standards and Algorithms. 11. Lossless Audio Coding and Digital Watermarking. 12. Quality Measures for Perceptual Audio Coding. References. Index.

http://jdsp.engineering.asu.edu/books/cover-page-audio+contents.pdf

Audio Signal Processing and Coding

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 compare it to current state-of-the-art algorithms for lossless audio compression.

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

In recent years audio coding has become a very popular field for research and applications. Especially perceptual audio coding schemes, such as MPEG-1 Layer-3 (MP3) and MPEG-2 Advanced Audio Coding (AAC), are widely used for efficient storage and transmission of music signals. Nevertheless, for professional applications, such as archiving and transmission in studio environments, lossless audio coding schemes are considered more appropriate. Traditionally, the technical approaches used in perceptual and lossless audio coding have been separate worlds. In perceptual audio coding, the use of filter banks, such as the lapped orthogonal transform “Modified Discrete Cosine Transform” (MDCT), has been the approach of choice being used by many state of the art coding schemes. On the other hand, lossless audio coding schemes mostly employ predictive coding of waveforms to remove redundancy. Only few attempts have been made so far to use transform coding for the purpose of lossless audio coding. This work presents a new approach of applying the lifting scheme to lapped transforms used in perceptual audio coding. This allows for an invertible integer-tointeger approximation of the original transform, e.g. the IntMDCT as an integer approximation of the MDCT. The same technique can also be applied to low-delay filter banks. A generalized, multi-dimensional lifting approach and a noise-shaping technique are introduced, allowing to further optimize the accuracy of the approximation to the original transform. Based on these new integer transforms, this work presents new audio coding schemes and applications. The audio coding applications cover lossless audio coding, scalable lossless enhancement of a perceptual audio coder and fine-grain scalable perceptual and lossless audio coding. Finally an approach to data hiding with high data rates in uncompressed audio signals based on integer transforms is described.

/pdf/audio-coding-based-on-integer-transforms-2tfvc3yz97.pdf

Audio Coding based on Integer Transforms

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.

https://www.hpl.hp.com/techreports/1999/HPL-1999-144.pdf

R.W. Schafer

Papers

Lossless compression of digital audio