On the performance analysis of the subband adaptive digital filter

Yang, Yooseong; Cho, Nam Ik; Lee, S.U.

doi:10.1109/iscas.1993.393658

Cited by 6 publications

(4 citation statements)

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“…According to [8], [9] analysis-synthesis model, an audio signal may be considered as the sum of a varying number of sinusoids, so a subband decomposition allows us to select only a restricted number. In [15], has been demonstrated that the optimum subband adaptive filter is the same as the full-band adaptive filter. The exception is in filter bank group time-delay and in the fact that the multirate adaptive filter provides better performance for highly correlated input signals than the full-band adaptive filter, which is the case with music signal inputs.…”

Section: Convergence Properties Of Subband Prediction Algorithmmentioning

confidence: 99%

“…It is well known that its upper bound can be written as (12) where and are maximum and minimum values of the associated power spectral density. Defining and , respectively, as the maximum and minimum power spectral density of the th subband input signal, we have 13) (14and so we obtain (15) where and are the eigenvalue spread of the subband and the full-band signal, respectively. Given that eigenvalues are bounded by the maximum and minimum values of the associated power spectral density (16) we may observe that for subband input signals we have (17) (18) which ensure a better convergence speed of the LMS algorithm for the subband adaptive filter than the full-band adaptive filter allowing a reduced time constant and an increased step size.…”

Section: Convergence Properties Of Subband Prediction Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Subband neural networks prediction for on-line audio signal recovery

Cocchi

Uncini

2002

IEEE Trans. Neural Netw.

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In this paper, a subbands multirate architecture is presented for audio signal recovery. Audio signal recovery is a common problem in digital music signal restoration field, because of corrupted samples that must be replaced. The subband approach allows for the reconstruction of a long audio data sequence from forward-backward predicted samples. In order to improve prediction performances, neural networks with spline flexible activation function are used as narrow subband nonlinear forward-backward predictors. Previous neural-networks approaches involved a long training process. Due to the small networks needed for each subband and to the spline adaptive activation functions that speed-up the convergence time and improve the generalization performances, the proposed signal recovery scheme works in online (or in continuous learning) mode as a simple nonlinear adaptive filter. Experimental results show the mean square reconstruction error and maximum error obtained with increasing gap length, from 200 to 5000 samples for different musical genres. A subjective performances analysis is also reported. The method gives good results for the reconstruction of over 100 ms of audio signal with low audible effects in overall quality and outperforms the previous approaches.

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Section: Convergence Properties Of Subband Prediction Algorithmmentioning

confidence: 99%

Section: Convergence Properties Of Subband Prediction Algorithmmentioning

confidence: 99%

Subband neural networks prediction for on-line audio signal recovery

Cocchi

Uncini

2002

IEEE Trans. Neural Netw.

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“…They also imply positive gains for vector coding, linear prediction, and DPCM in subbands of the same sources. The memory reduction properties inherent in subbands lead also to the faster convergence of subband adaptive lters over fullband adaptive lters in adaptive line enhancers and adaptive channel equalizers 24,25]. The existence of a \super-optimal" Prediction Error Filter has been proved and a framework has been provided to further investigate this issue.…”

Section: Resultsmentioning

confidence: 99%

Analysis of linear prediction, coding, and spectral estimation from subbands

Rao

Pearlman

1996

IEEE Trans. Inform. Theory

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“…The basic principle of a filter depends on whether it is an analog or digital filter [2]. Analog filters use electronic components to achieve frequency selectivity of a signal, while digital filters [3] utilize digital processing techniques for discrete-time signals. Both have filter characteristics, but there are significant differences in their implementation and application scenarios.…”

Section: Fundamentals Of Filtersmentioning

confidence: 99%

Advancements in Filter Technology: Evolution and Applications

Mao

2024

HSET

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This paper explores the historical trajectory and contemporary applications of signal processing filters. From the early days of analog filters to the current digital era, the paper traces their evolution, emphasizing key developments in active RC filters, monolithic integrated op amps, and the transition to switched-capacitor and continuous-time filters. Fundamentals of analog and digital filters are outlined, distinguishing their processing of continuous-time and discrete-time signals. The classification of filters, including low-pass, high-pass, band-pass, and band-stop, is discussed with insights into their specific applications. The paper delves into crucial applications in audio processing, video processing, communication systems, power management, and sensor signal processing, showcasing filters' pivotal role in enhancing signal quality across diverse fields. Addressing emerging technologies, the paper highlights adaptive and deep learning filters, projecting the future trends in filter technology. It anticipates developments in analog integrated filters, emphasizing the growing significance of integrated circuits technology. This comprehensive review aims to deepen readers' understanding and inspire continued innovation in signal processing.

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On the performance analysis of the subband adaptive digital filter

Cited by 6 publications

References 6 publications

Subband neural networks prediction for on-line audio signal recovery

Subband neural networks prediction for on-line audio signal recovery

Analysis of linear prediction, coding, and spectral estimation from subbands

Advancements in Filter Technology: Evolution and Applications

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