Lossless compression of digital audio

Hans, Mat; Schafer, Ronald W.

doi:10.1109/79.939834

Cited by 98 publications

(43 citation statements)

References 5 publications

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“…Hans and Schafer [3] present an overview of lossless data compression in the context of audio data. Barr and Asanovi´c show in [4] that the energy required for transmitting a bit can be equivalent to the energy consumption of a thousand microcontroller operations.…”

Section: Related Workmentioning

confidence: 99%

Median Predictor based Data Compression Algorithm for Wireless Sensor Network

Maurya¹,

Singh²

2011

IJCA

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Large scale wireless sensor networks (WSNs) have emerged as the latest trend in revolutionizing the paradigm of collecting and processing data in diverse environments. Its advancement is fueled by development of tiny low cost sensor nodes which are capable of sensing, processing and transmitting data. Due to the small size of sensor nodes there are various resource constraints. It is the severe energy constraints and the limited computing resources that present the major challenge in converting the vision of WSNs to reality. In this paper, we propose a simple and efficient data compression algorithm which is lossless and particularly suited to the reduced memory and computational resources of a wireless sensor networks node. The proposed data compression algorithm gives good compression ratio for highly correlated data. Simulations for the proposed data compression algorithm are performed on TOSSIM.

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Section: Related Workmentioning

confidence: 99%

Median Predictor based Data Compression Algorithm for Wireless Sensor Network

Maurya¹,

Singh²

2011

IJCA

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“…Hans and Schafer [4] present an overview of lossless data compression in the context of audio data. With the emergence of many severely energyconstrained application domains, such as various forms of sensor networks, the topic of energy efficiency is becoming increasingly important.…”

Section: Background and Related Workmentioning

confidence: 99%

Energy-Aware Data Compression for Wireless Sensor Networks

Puthenpurayil

Bhattacharyya

2007

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07

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Data compression techniques have extensive applications in power-constrained digital communication systems, such as in the rapidly-developing domain of wireless sensor network applications. This paper explores energy consumption tradeoffs associated with data compression, particularly in the context of lossless compression for acoustic signals. Such signal processing is relevant in a variety of sensor network applications, including surveillance and monitoring. Applying data compression in a sensor node generally reduces the energy consumption of the transceiver at the expense of additional energy expended in the embedded processor due to the computational cost of compression. This paper introduces a methodology for comparing data compression algorithms in sensor networks based on the figure of merit D/E, where D is the amount of data (before compression) that can be transmitted under a given energy budget E for computation and communication. We develop experiments to evaluate, using this figure of merit, different variants of linear predictive coding. We also demonstrate how different models of computation applied to the embedded software design lead to different degrees of processing efficiency, and thereby have significant effect on the targeted figure of merit.Index Terms-DSP software, lossless data compression, linear predictive coding, low power design, wireless sensor networks.

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“…If this predictable component is removed, the residual signal will be smaller and so need fewer bits to represent. To take advantage of this redundancy, lossless audio compression algorithms generally incorporate two functional blocks (Hans and Schafer, 2001): A filter and a coder. The filter removes most of the inter-sample correlation by subtracting a prediction of the current sample obtained by filtering prior samples, i.e., by forming a residual signal…”

Section: A Lossless Audio Compressionmentioning

confidence: 99%

“…The success of a relatively simple lossless compressor, SHORTEN (Robinson, 1994), spurred the development of more sophisticated compressors such as FLAC and WAVPACK, which are now included in media players and multi-format data compression programs. These algorithms share the same basic structure (Hans and Schafer, 2001;Solomon, 2006) compressing sound by first removing the correlation between adjacent samples and then by efficiently coding the residual. But the need to adapt to a wide variety of music increases the complexity of these programs.…”

Section: Introductionmentioning

confidence: 99%

Low complexity lossless compression of underwater sound recordings

Johnson

Partan

Hurst

2013

The Journal of the Acoustical Society of America

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Autonomous listening devices are increasingly used to study vocal aquatic animals, and there is a constant need to record longer or with greater bandwidth, requiring efficient use of memory and battery power. Real-time compression of sound has the potential to extend recording durations and bandwidths at the expense of increased processing operations and therefore power consumption. Whereas lossy methods such as MP3 introduce undesirable artifacts, lossless compression algorithms (e.g., FLAC) guarantee exact data recovery. But these algorithms are relatively complex due to the wide variety of signals they are designed to compress. A simpler lossless algorithm is shown here to provide compression factors of three or more for underwater sound recordings over a range of noise environments. The compressor was evaluated using samples from drifting and animalborne sound recorders with sampling rates of 16-240 kHz. It achieves >87% of the compression of more-complex methods but requires about 1/10 of the processing operations resulting in less than 1 mW power consumption at a sampling rate of 192 kHz on a low-power microprocessor. The potential to triple recording duration with a minor increase in power consumption and no loss in sound quality may be especially valuable for battery-limited tags and robotic vehicles.

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Lossless compression of digital audio

Cited by 98 publications

References 5 publications

Median Predictor based Data Compression Algorithm for Wireless Sensor Network

Median Predictor based Data Compression Algorithm for Wireless Sensor Network

Energy-Aware Data Compression for Wireless Sensor Networks

Low complexity lossless compression of underwater sound recordings

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