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An established model for the signal analysis performed by the human cochlea is the overcomplete gammatone filterbank. The high correlation of this signal model with human speech and environmental sounds [E. Smith and M. Lewicki, Nature (London) 439, 978-982 (2006)], combined with the increased time-frequency resolution of sparse overcomplete signal models,(More)
This paper studies the fine-grain scalable compression problem with emphasis on 1-D signals such as audio signals. Like in the successful 2-D still image compression techniques embedded zerotree wavelet coder (EZW) and set partitioning in hierarchical trees (SPIHT), the desired fine-granular scalability and high coding efficiency are benefited from a(More)
We present strategies for perceptual improvements of embedded audio coding based on psychoacoustic weighting and spectral envelope restoration. The encoding schemes exhibit fine-grain bitrate scalability via the set partitioning in hierarchical trees (SPIHT) algorithm. Weighting factors and envelope parameters are transmitted under careful consideration of(More)
Evidence that neurosensory systems use sparse signal representations as well as improved performance of signal processing algorithms using sparse signal models raised interest in sparse signal coding in the last years. For natural audio signals like speech and environmental sounds, gammatone atoms have been derived as expansion functions that generate a(More)
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