Frequency-domain Adaptive Algorithm Research Articles

An OFDM-Based Frequency Domain Equalization Algorithm for Underwater Acoustic Communication with a High Channel Utilization Rate

The underwater acoustic communication technique for high-speed and highly reliable information transmission in the ocean has been one of the popular research focuses facing the fast-growing information technology sector and the accelerating development of ocean resources. In order to achieve a high information transmission rate with limited underwater acoustic channel bandwidth, researchers have paid much attention to the underwater acoustic communication technique based on orthogonal frequency division multiplexing (OFDM). A traditional OFDM-based frequency domain equalization algorithm relies on cyclic prefixes for the effective resistance to the multipath effect of an underwater acoustic channel. However, a redundant cyclic prefix may lead to a severe waste of energy and bandwidth in the underwater acoustic system if it is too long. The high utilization rate of OFDM signal channel will not be practically achieved in this case. Based on the limitations of the existing frequency domain equalization algorithm, this paper studied the influence of the multipath effect on the OFDM signal transmission. Subsequently, the principles of the OFDM-based frequency domain equalization were further explored for an improved structural model design of the communication system. On this basis, a novel frequency domain adaptive equalization algorithm was put forward. In addition, the proposed algorithm was optimized to address the problem of increased computation. The simulation results proved that the novel frequency domain equalization algorithm delivers a better symbol error ratio than the existing algorithm, and the compensation for the multipath effect through frequency selective fading. The proposed algorithm can realize the information transmission at a low symbol error ratio when fewer cyclic prefixes are used, so that it takes up a lower number of channels with cyclic prefixes in the OFDM communication system.

Comb-partitioned frequency-domain constraint adaptive algorithm for active noise control

Active noise control (ANC) is gaining credence as an effective approach in mitigating low-frequency urban noise. Current ANC algorithms that attenuate noise across the full audio frequency band, often exert control effort excessively at higher frequencies. Moreover, such unrestrained control unavoidably attenuates some critical sounds, such as alarms and warning signals. In practice, excessive control effort in ANC usually results in output-saturation distortion, which affects the adaptive system stability and degrades the residual audio quality. To provide greater flexibility of control in frequency bands of interest without incurring output saturation, this paper proposes two variants of the filtered reference comb-partitioned frequency-domain adaptive filter (FxCFDAF) algorithm, namely the leaky FxCFDAF and drop-out FxCFDAF algorithms, which exert output-effort constraint at the control filter in the frequency domain. In addition, the comb-partitioning approach in the proposed FxCFDAF algorithms is delayless and computationally-efficient, which are essential for practical implementation, unlike the conventional frequency-domain adaptive algorithm. Experimental simulations carried out on measured primary and secondary paths validate the proposed algorithms’ advantage over the conventional FxLMS algorithm in mitigating large-amplitude noise without output saturation.

Set-Membership Constrained Frequency-Domain Algorithm

In this brief, we propose a new frequency-domain adaptive filtering algorithm within the set-membership filtering (SMF) paradigm. Algorithms employing the previously proposed frequency-domain SMF (FD-SMF) concept are not capable of performing linear convolutions (i.e., linear filtering operations), but only circular convolutions. Here, we overcome such limitation by generalizing the FD-SMF concept to allow for linear convolutions, and we propose a new algorithm following this extended FD-SMF concept. Synthetic and real-life simulation results confirm that the proposed algorithm can perform quite better than its non-SMF counterpart, achieving faster convergence and requiring much fewer computations.

A switching strategy of the frequency-domain adaptive algorithm for active noise control.

The bin-normalized frequency domain block least mean square (NFBLMS) algorithm is a good choice in the active noise control system due to its benefit of high convergence speed. However, it suffers from a biased steady-state solution with insufficient adaptive filter length, often unavoidable due to the influence of the secondary path. A modified frequency domain block least mean square (MFBLMS) algorithm with guaranteed optimal steady-state performance has been proposed recently. However, its convergence speed is generally lower than that of the NFBLMS algorithm. In this paper, a mixed algorithm combining the NFBLMS and MFBLMS algorithms is proposed based on the analysis of the initial convergence trajectory of the NFBLMS algorithm. An effective switching strategy is designed, enabling the MFBLMS algorithm after the NFBLMS algorithm approaches its steady state and shifting back to the NFBLMS algorithm when the change of environment is detected. The mixed algorithm has both the high convergence speed and the optimal steady-state performance, and its effectiveness is validated by simulations using measured acoustic transfer functions.

Adaptive encryption of digital images based on lifting wavelet optimization

A large number of digital pictures have many security threats in the transmission on the Internet, and various encryption technologies for images have been proposed. The existing image encryption technology has a simple structure and a small key space, which cannot meet the requirement for image information security at this stage. In order to improve the effectiveness of image encryption and enhance the adaptive ability of image encryption, based on the theory of wavelet transform, chaos theory and adaptive encryption, this paper designs the overall framework of image encryption, and proposes a digital image adaptive encryption method based on lifting wavelet optimization. Firstly, the framework of image frequency domain adaptive encryption algorithm is designed, wavelet transform based on lifting is studied and analyzed, and lifting wavelet optimization method based on improved threshold method and particle swarm algorithm is given to improve the adaptability to image decomposition, so that the frequency domain coefficients obtained after wavelet decomposition can better represent the image content, thus making frequency domain encryption more targeted. Chaos mapping is used to encrypt the pixel gray value and scramble the pixel position of the decomposed low frequency coefficients of the image, thus achieving good image adaptive encryption.

An Improved Double-talk Detection Algorithm for Echo Cancellation in Teleconference System

Echo cancellation commonly employs a double-talk detector (DTD), which is essential to keep the adaptive filter from diverging in the presence of near-end speech. An improved double-talk detection algorithm is proposed to promote the performance of the echo cancellation in teleconference system in the presence of speech issued from the near-end speaker (double-talk). Based on the partitioned block frequency domain adaptive filter algorithm (PBFDAF), the advanced double-talk detection method is presented in this paper constructs a joint correlation coefficient variable to judge the communication state and the variable is applied as a smoothing parameter to the error signal and then get the final output signal. Simulation results show that the improved algorithm can effectively detect the current voice state, eliminate the echo signal, and keep the near-end speaker’s voice well. The performance of the echo cancellation algorithm is improved when both echo and near end signals are simultaneously active by the proposed technique.

Multichannel Acoustic Echo Cancellation in the Wave Domain With Increased Robustness to Nonuniqueness

Acoustic echo cancellation (AEC) is significantly more challenging in the case of multichannel reproduction compared to the single-channel case. This is mainly due to the so-called nonuniqueness problem, which becomes especially severe for massive multichannel reproduction systems like, e.g., wave field synthesis systems or higher-order ambisonics, where the different loudspeaker signals are often strongly correlated. In this paper, a multichannel wave-domain AEC structure is proposed that maintains robustness without altering the loudspeaker signals, as is commonly required to preserve the reproduction quality. The proposed method exploits the dominance of certain couplings in the wave-domain representation of the loudspeaker-enclosure-microphone system. In this representation, preferring a strong weight of the dominant couplings in the identified system improves the system identification performance. A modification of the well-known generalized frequency-domain adaptive filtering algorithm is then presented to implement the proposed approach, including a self-contained description of all necessary components. An experimental evaluation compares the proposed method to competing approaches and shows that the proposed method compares well in terms of convergence behavior while avoiding the performance degradation, which is intrinsic to other approaches.

The generalized frequency-domain adaptive filtering algorithm as an approximation of the block recursive least-squares algorithm

Acoustic echo cancellation (AEC) is a well-known application of adaptive filters in communication acoustics. To implement AEC for multichannel reproduction systems, powerful adaptation algorithms like the generalized frequency-domain adaptive filtering (GFDAF) algorithm are required for satisfactory convergence behavior. In this paper, the GFDAF algorithm is rigorously derived as an approximation of the block recursive least-squares (RLS) algorithm. Thereby, the original formulation of the GFDAF algorithm is generalized while avoiding an error that has been in the original derivation. The presented algorithm formulation is applied to pruned transform-domain loudspeaker-enclosure-microphone models in a mathematically consistent manner. Such pruned models have recently been proposed to cope with the tremendous computational demands of massive multichannel AEC. Beyond its generalization, a regularization of the GFDAF is shown to have a close relation to the well-known block least-mean-squares algorithm.

Partitioned Block Frequency Domain Adaptive Filtering Algorithm for Nonlinear Acoustic Echo Cancellation

This paper proposes a robust nonlinear acoustic echo canceller (NAEC) which is effective for modeling the nonlinearity of a speaker module and the long acoustic echo path within a speech communication environment. The proposed NAEC utilizes a sigmoid pre-processor for modeling the speaker nonlinearity and a partitioned block frequnecy-domain adaptive filter for identifying the acoustic echo path with small delay. Simulation results confirmed that the proposed algorithm achieves excellent performance with much lower computational complexity than the previous NAEC.

Adaptive Algorithm for Anti-Jamming in Broadband Communication

This paper presents a method to suppress narrowband interference in the direct sequence spread spectrum communication based frequency domain adaptive algorithm. The method determines the interference threshold by N-sigma algorithm and calculates the cut-off condition by separating the signal. The method suppresses the narrowband interference by frequency domain batch algorithm. This paper gives a detailed design method and simulation results, and makes a performance comparison with other methods. The results show that, the method in this paper has better performance than other methods. It is suitable for narrowband interference suppression in broadband systems.

A better frequency domain adaptive algorithm for active noise control in a short duct

For the feedforward active noise control in a short duct, the noncausality of the whole system is often inevitable, since the reference sensor needs to be placed very close to the control source, and the acoustic transmission delay between them is often surpassed by the inherent AD/DA and anti-aliasing filters latency in the controller. The commonly used normalized frequency domain LMS algorithm possesses the benefit of fast convergence speed, but suffers from deteriorated steady-state performance when used in non-causal circumstances. In this paper, an efficient modification of the normalized frequency domain LMS algorithm is proposed, which can significantly improve wide-band noise reduction level in non-causal circumstances. Both simulations and experiments demonstrate the superiority of the proposed algorithm.

Blind System Identification Using Sparse Learning for TDOA Estimation of Room Reflections

Localization of early room reflections can be achieved by estimating the time-differences-of-arrival (TDOAs) of reflected waves between elements of a microphone array. For an unknown source, we propose to apply sparse blind system identification (BSI) methods to identify the acoustic impulse responses, from which the TDOAs of temporally sparse reflections are estimated. The proposed time- and frequency-domain adaptive algorithms based on crossrelation formulation are regularized by incorporating an l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm sparseness constraint, which is realized using a split Bregman method. These algorithms are shown to outperform standard crossrelation-based BSI techniques when estimating TDOAs of reflections in the presence of background noise.

A Frequency Domain LMS Algorithm with Dynamic Selection of Frequency Bins

Frequency-domain (FD) adaptive filter algorithms are able to achieve a low computational complexity by using the overlap-and-save implementation means compared to time-domain (TD) ones. In this article, we propose a new FD least-mean-square (FD-LMS) algorithm which dynamically selects frequency bins in order to reduce the computational complexity and maintain the convergence performance of the conventional FD-LMS. The optimal selection of frequency bins is derived by the largest decrease between the successive FD mean square deviations (MSDs) at every data block. Simulation results show that the proposed algorithm provides a low steady-state normalized MSD (NMSD) and similar convergence rate compared to the conventional FD-LMS algorithm. In addition, it gains a low computational complexity.

Active Control against Impact Noise Using Frequency Domain Adaptive Algorithm

Active Control against Impact Noise Using Frequency Domain Adaptive Algorithm

522 振動モード情報を利用した衝撃音の能動制御

Noise control has been developed to establish the comfortable soundscape even if our house life. Floor impact noise such as the sound of footsteps or falling object becomes a problem in an apartment house. Floor impact noise has a peak at the low-frequency range. Passive noise control is not effective against low-frequency noise. Therefore, the purpose of this paper is to control the impact noise using active noise control. In our previous research, the situation of fixed vibration source and control point was considered. In this paper, we consider about changing vibration source and control point. First, this research focus on changing control point. In this paper, the effectiveness of active noise control using frequency domain adaptive algorithm with processing that compute the control noise through the use of vibration modes is confirmed through simulations.

632 周波数領域適応アルゴリズムを用いた衝撃音の能動制御

632 周波数領域適応アルゴリズムを用いた衝撃音の能動制御

A Windowing Frequency Domain Adaptive Filter for Acoustic Echo Cancellation

This letter proposes a windowing frequency domain adaptive algorithm, which reuses the filtering error to apply window function in the filter updating symmetrically. By using a proper window function to reduce the negative influence of the spectral leakage, the proposed algorithm can significantly improve the performance of the acoustic echo cancellation for speech signals.

Frequency-Domain Adaptive Algorithm for Network Echo Cancellation in VoIP

We propose a new low complexity, low delay, and fast converging frequency-domain adaptive algorithm for network echo cancellation in VoIP exploiting MMax and sparse partial (SP) tap-selection crite...

Frequency-Domain Adaptive Algorithm for Network Echo Cancellation in VoIP

We propose a new low complexity, low delay, and fast converging frequency-domain adaptive algorithm for network echo cancellation in VoIP exploiting MMax and sparse partial (SP) tap-selection criteria in the frequency domain. We incorporate these tap-selection techniques into the multidelay filtering (MDF) algorithm in order to mitigate the delay inherent in frequency-domain algorithms. We illustrate two such approaches and discuss their tradeoff between convergence performance and computational complexity. Simulation results show an improvement in convergence rate for the proposed algorithm over MDF and significantly reduced complexity. The proposed algorithm achieves a convergence performance close to that of the recently proposed, but substantially more complex improved proportionate MDF (IPMDF) algorithm.

A Time/Frequency-Domain Unified Delayless Partitioned Block Frequency-Domain Adaptive Filter

In this letter, a delayless counterpart of the partitioned frequency-domain adaptive filter algorithm is straightforwardly derived from the block least-mean-square (BLMS) algorithm. To improve its tracking capability, a time/frequency-domain unified (TFU) adaptation scheme is proposed. It is further shown that other delayless realizations can be seen as simplified implementations of the TFU technique. Simulations are presented to support the analyses and demonstrate the good convergence and tracking performance of the proposed algorithm.