Orthogonal Transforms

Akansu, Ali N.; Haddad, R.A.

doi:10.1016/b978-0-12-047140-9.50007-x

Cited by 44 publications

(43 citation statements)

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“…Beyond the references cited below, the reader in need of greater detail or further analytical development is referred to in-depth tutorials on filter banks that have appeared in the literature [50,51], as well as in classical [52] and recent texts [53,54,55]. The reader may also wish to explore the connection between filter banks and wavelets that has been well documented in the literature [56,57] and in several texts [54,58,59,133]. These notions are of particular relevance in the case of audio codecs that make use of discrete wavelet and wavelet packet analysis.…”

Section: Time-frequency Analysis: Filter Banks and Transformsmentioning

confidence: 99%

“…As far as quantization sensitivity is concerned, there are available expressions [88] for the reconstruction error of the quantized system in terms of signal-correlated and uncorrelated components that can be used to identify perceptually disturbing reconstruction artifacts. Quantization issues for PR cosine modulated filter banks in general are also addressed in [58]. …”

Section: ) Fast Algorithms Complexity and Implementation Issuesmentioning

confidence: 99%

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Perceptual coding of digital audio

2000

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I. INTRODUCTIONAudio coding or audio compression algorithms are used to obtain compact digital representations of high-fidelity (wideband) audio signals for the purpose of efficient transmission or storage. The central objective in audio coding is to represent the signal with a minimum number of bits while achieving transparent signal reproduction, i.e., generating output audio that cannot be distinguished from the original input, even by a sensitive listener ("golden ears"). This paper gives a review of algorithms for transparent coding of high-fidelity audio.The introduction of the compact disk (CD) in the early eighties [1] brought to the fore all of the advantages of digital audio representation, including unprecedented high-fidelity, dynamic range, and robustness. These advantages, however, came at the expense of high data rates. Conventional CD and digital audio tape (DAT) systems are typically sampled at either 44.1 or 48 kilohertz (kHz) using pulse code modulation (PCM) with a sixteen bit sample resolution. This results in uncompressed data rates of 705.6/768 kilobits per second (kbps) for a monaural channel, or 1.41/1.54 megabits per second (Mbps) for a stereo pair at 44.1/48 kHz, respectively. Although high, these data rates were accommodated successfully in first generation digital audio applications such as CD and DAT. Unfortunately, second generation multimedia applications and wireless systems in particular are often subject to bandwidth and cost constraints that are incompatible with high data rates. Because of the success enjoyed by the first generation, however, end users have come to expect "CD-quality" audio reproduction from any digital system. Therefore, new network and wireless multimedia digital audio systems must reduce data rates without compromising reproduction quality. These and other considerations have motivated considerable research during the last decade towards formulation of compression schemes that can satisfy simultaneously the conflicting demands of high compression ratios and transparent reproduction quality for high-fidelity audio signals

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Section: Time-frequency Analysis: Filter Banks and Transformsmentioning

confidence: 99%

Section: ) Fast Algorithms Complexity and Implementation Issuesmentioning

confidence: 99%

Perceptual coding of digital audio

2000

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“…In the case of GOFDM, K and M are power of 2 numbers where K is ranged from 16 GFDM and GOFDM configurations are not equal; thus, we consider GOFDM configuration parameters as the reference and calculate its complexity relative to GFDM complexity with equal K but M values increased by 1. As seen in Fig.…”

Section: Computational Complexitymentioning

confidence: 99%

Optimum low complexity filter bank for generalized orthogonal frequency division multiplexing

Abbaszadeh

Khalaj

Haghbin

2018

J Wireless Com Network

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Generalized frequency division multiplexing (GFDM) is one of the multicarrier modulation candidates proposed for the 5th generation of wireless networks. Among GFDM linear receivers, GFDM MMSE receiver achieves the best error performance for multipath fading channels at the cost of high numerical complexity. Hence, the combination of GFDM match filter (MF) receiver and double-side successive interference cancellation (DSIC) method is used instead. However, there is a significant gap between the error performance of GFDM MMSE and DSIC/MF receivers for the case of employing modern channel coding. Recently, we have proposed a new multicarrier scheme based on GFDM architecture called generalized orthogonal frequency division multiplexing (GOFDM). This study derives an optimized cyclic tree-structured perfect reconstruction-quadrature mirror filter (PR-QMF) bank for GOFDM transceiver and then introduces a novel method for implementation of the optimum filter bank in the frequency domain. Employing such a fast and optimum filter bank provides several advantages for GOFDM transceiver. GOFDM transmitter mitigates out-of-band spectrum leak to the level of that of GFDM. In addition, choosing an appropriate configuration of filter bank yields lower peak to average power ratio in transmit signal of GOFDM compared to that of OFDM. On the other hand, while GOFDM MMSE receiver has lower numerical complexity compared with GFDM DSIC/MF receiver, its coded bit error rate curve is close to that of GFDM MMSE receiver. The aforementioned advantages envision GOFDM as a competitive candidate to be employed in the physical layer of new wireless applications.

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“…The DWT of the signal is then obtained by concatenating all DWT coefficients in the previous levels. The details of this algorithm can be found in [4].…”

Section: Time Scalingmentioning

confidence: 99%