59 research outputs found

    Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

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    Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

    Novi normirani pojasni adaptivni filtar s promjenjivom duljinom koraka

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    This paper presents a new variable step-size normalized subband adaptive filter (VSS-NSAF) algorithm. In the proposed VSS-NSAF, the step-size changes in order to have largest decrease in themean square deviation (MSD) for sequential iterations. To reduce the computational complexity of VSS-NSAF, the variable step-size selective partial update normalized subband adaptive filter (VSS-SPU-NSAF) is proposed. In this algorithm the filter coefficients are partially updated in each subband at every iteration. Simulation results show the good performance of the proposed algorithms in convergence speed and steady-state MSD.U ovom radu prikazan je novi algoritam za normirani adaptivni filtar s promjenjivim korakom. Kod predloženog filtra, veličina koraka mijenja se kako bi se dobilo najveće smanjenje srednje vrijednosti odstupanja za uzastopne iteracije. Kako bi se smanjila računska složenost filtra, predložen je normirani pojasni adaptivni filtar s promjenjivim korakom i selektivnim parcijalnim osvježavanjem. Kod tog algortima koeficijenti filtra parcijalno se osvježavaju u svakom pojasu i pri svakoj iteraciji. Simulacijski rezultati pokazuju dobru brzinu konvergencije i malu srednju vrijednost odstupanja u stacionarnom stanju za predloženi filtar

    Analiza prijelazne pojave adaptivnih filtara primjenom općeg radnog okvira

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    Employing a recently introduced framework in which a large number of adaptive filter algorithms can be viewed as special cases, we present a generalized transient analysis. An important implication of this is that while the theoretical analysis is performed for a generic filter coefficient update equation the results are directly applicable to a large range of adaptive filter algorithms simply by specifying some parameters of this generic filter coefficient update equation. In particular we point out that theoretical learning curves for the Least Mean Square (LMS), Normalized Least Mean Square (NLMS), the Affine Projection Algorithm (APA) and its relatives, as well as the Recursive Least Squares (RLS) algorithm are obtained as special cases of a general result. Subsequently, the recently introduced Fast Euclidian Direction Search (FEDS) algorithms as well as the Pradhan-Reddy subband adaptive filter (PRSAF) are used as non-trivial examples when we demonstrate the usefulness and versatility of the proposed approach to adaptive filter transient analysis through an experimental evaluation.U radu se predstavlja poopćena analiza prijelaznih pojava adaptivnih filtara, koja se zasniva na primjeni nedavno predstavljenog radnog okvira koji velik broj raznih algoritama adaptivnih filtara promatra kao specijalne slučajeve. Važna posljedica toga je da su rezultati, iako se teoretska analiza provodi na generičkoj jednadžbi za osvježavanje koeficijenta filtra, izravno primjenjivi na razne algoritme adaptivnih filtara jednostavnom specificikacijom nekih parametara generičke jednadžbe za osvježavanje koeficijenata filtra. Posebno se naglašava da su teoretske krivulje učenja za algoritam najmanjih kvadrata (LMS), normalizirani algoritam najmanjih kvadrata (NLMS), afini projekcijski algoritam (APA) i njemu srodnih algoritama, kao i za rekurzivni algoritam najmanjih kvadrata (RLS) dobivene kao posebni slučajevi poopćenog rješenja. Potom se nedavno predstavljeni algoritmi brze euklidske usmjerene pretrage (FEDS) te Pradhan-Reddy pojasni adaptivni filtar (PRSAF) koriste kao netrivijalni primjeri za dokazivanje korisnosti i univerzalnosti predloženog pristupa analizi prijelaznih pojava adaptivnih filtara kroz eksperimentalnu evaluaciju

    Frequency Controlled Noise Cancellation for Audio and Hearing Purposes

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    Methods for hearing aids sought to compensate for loss in hearing by amplifying signals of interest in the audio band. In real-world, audio signals are prone to outdoor noise which can be destructive for hearing aid.  Eliminating interfering noise at high speed and low power consumption became a target for recent researches. Modern hearing compensation technologies use digital signal processing which requires minimum implementation costs to reduce power consumption, as well as avoiding delay in real time processing. In this paper, frequency controlled noise cancellation (FCNC) strategy for hearing aid and audio communication is developed with low complexity and least time delay. The contribution of the current work is made by offering a method that is capable of removing inherent distortion due filter-bank insertion and assigning adaptive filtering to a particular sub-band to remove external noise. The performance of the proposed FCNC was examined under frequency-limited noise, which corrupts particular parts of the audio spectrum. Results showed that the FCNC renders noise-immune audio signals with minimal number of computations and least delay. Mean square error (MSE) plots of the proposed FCNC method reached below -30 dB compared to -25 dB using conventional sub-band method and to -10 dB using standard full-band noise canceller. The proposed FCNC approach gave the lowest number of computations compared to other methods with a total of 346 computations per sample compared to 860 and 512 by conventional sub-band and full-band methods respectively. The time delay using FCNC is the least compared to the other methods

    A Novel Adaptive method for Acoustic Echo Cancellation

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    Speech is compulsory in audio teleconferenceing system. In present scenareo acoustic echo is a major setback for user and causes a lessening in the quality of the communication.By means of some adaptive filtering methods acoustic echo canbe eliminated and can be reachrd in a desired value. A detail performance assessment is reported, including echo return loss enhancement (ERLE), convergence time and system distance metrics.We have also compared two different signals and how noise can be cancelled out using NLMS algorithm
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