GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

Lorente, Jorge; Ferrer, Miguel; Diego, María de; González, Alberto

doi:10.1109/taslp.2014.2344852

Cited by 41 publications

(29 citation statements)

References 28 publications

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“…Accelerators of this type have already been applied to different problems in acoustics and audio processing. Some applications include room acoustics modeling [24], [25], [26], [27], additive synthesis [28], [29], full 3-D model of drums in a large virtual room [30], sliding phase vocoder [31], beamforming [32], audio rendering [33], [34], [35], multichannel IIR filtering of audio signals [36], dynamic range reduction using multiple allpass filters [37], and adaptive filtering [38], [39], [40].…”

Section: Introductionmentioning

confidence: 99%

GPU-Based Dynamic Wave Field Synthesis Using Fractional Delay Filters and Room Compensation

Belloch

González

Quintana‐Ortí

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Wave Field Synthesis (WFS) is a multichannel audio reproduction method, of a considerable computational cost that renders an accurate spatial sound field using a large number of loudspeakers to emulate virtual sound sources. The moving of sound source locations can be improved by using fractional delay filters, and room reflections can be compensated by using an inverse filter bank that corrects the room effects at selected points within the listening area. However, both the fractional delay filters and the room compensation filters further increase the computational requirements of the WFS system. This paper analyzes the performance of a WFS system composed of 96 loudspeakers which integrates both strategies. In order to deal with the large computational complexity, we explore the use of a graphics processing unit (GPU) as a massive signal co-processor to increase the capabilities of the WFS system.The performance of the method as well as the benefits of the GPU acceleration are demonstrated by considering different sizes of room compensation filters and fractional delay filters of order 9. The results show that a 96-speaker WFS system that is efficiently implemented on a state-of-art GPU can room compensation filters having more than 4,000 coefficients each.

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Section: Introductionmentioning

confidence: 99%

GPU-Based Dynamic Wave Field Synthesis Using Fractional Delay Filters and Room Compensation

Belloch

González

Quintana‐Ortí

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

Self Cite

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“…The reason is because most common audio cards work with block data buffers. Moreover, hardware platforms that work with blocks of samples such as Digital Signal Processors (DSPs) or Graphics Processing Units (GPUs), use libraries of frequency-domain operations for a more efficient processing [23]. Furthermore, if the adaptive filters and the estimated secondary paths are longer than the sample block, they have to be split up into partitions [24].…”

Section: Collaborative Distributed Algorithm For An N-nodes Asn Basedmentioning

confidence: 99%

Blockwise Frequency Domain Active Noise Controller Over Distributed Networks

et al. 2016

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This work presents a practical active noise control system composed of distributed and collaborative acoustic nodes. To this end, experimental tests have been carried out in a listening room with acoustic nodes equipped with loudspeakers and microphones. The communication among the nodes is simulated by software. We have considered a distributed algorithm based on the Filtered-x Least Mean Square (FxLMS) method that introduces collaboration between nodes following an incremental strategy. For improving the processing efficiency in practical scenarios where data acquisition systems work by blocks of samples, the frequency-domain partitioned block technique has been used. Implementation aspects such as computational complexity, processing time of the network and convergence of the algorithm have been analyzed. Experimental results show that, without constraints in the network communications, the proposed distributed algorithm achieves the same performance as the centralized version. The performance of the proposed algorithm over a network with a given communication delay is also included.

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“…The computational problem associated with large-scale active noise control systems has also recently been addressed by exploiting the parallel processing provided by graphics processing units [11].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

Cheer

Daley

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, singleoutput broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance.The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation. Index TermsActive noise control, Active vibration control, Delayless subband adaptive filtering, MIMO J. Cheer is with the Institute

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GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

Cited by 41 publications

References 28 publications

GPU-Based Dynamic Wave Field Synthesis Using Fractional Delay Filters and Room Compensation

GPU-Based Dynamic Wave Field Synthesis Using Fractional Delay Filters and Room Compensation

Blockwise Frequency Domain Active Noise Controller Over Distributed Networks

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

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