Miguel Ferrer

Sound quality of low-frequency and car engine noises after active noise control

Abstract The ability of active noise control (ANC) systems to achieve a more pleasant sound has been evaluated by means of sound quality analysis of a real multi-channel active noise controller. Recordings of real car engine noises had been carried out using a Head acoustics TM binaural head simulator seated in a typical car seat, and these signals together with synthesized noise have been actively controlled in an enclosed room. The sound quality study has focused on the estimation of noise quality changes through the evaluation of the sense of comfort. Two methods have been developed: firstly, a predictive method based on psychoacoustic parameters (loudness, roughness, tonality and sharpness); and secondly, a subjective method using a jury test. Both results have been related to the spectral characteristics of the sounds before and after active control. It can be concluded from both analyses that ANC positively affects acoustic comfort. The engine noise mathematical comfort predictor is based on loudness and roughness (two psychoacoustic parameters directly influenced by ANC), and has satisfactorily predicted the improvements in the pleasantness of the sounds. As far as the subjective evaluation method is concerned, the jury test has showed that acoustic comfort is, in most cases, directly related to the sense of quietness. However, ANC has also been assessed negatively by the jury in the cases that it was unable to reduce the loudness, perhaps because of the low amplitudes of the original sounds. Finally, from what has been shown, it can be said that the subjective improvements strongly depends on the attenuation level achieved by the ANC system operation, as well as the spectral characteristics of the sounds before and after control.

Evolutionary and variable step size strategies for multichannel filtered-x affine projection algorithms

This study is focused on the necessity to improve the performance of the affine projection (AP) algorithm for active noise control (ANC) applications. The proposed algorithms are evaluated regarding their steady-state behaviour, their convergence speed and their computational complexity. To this end, different strategies recently applied to the AP for channel identification are proposed for multichannel ANC. These strategies are based either on a variable step size, an evolving projection order, or the combination of both strategies. The developed efficient versions of the AP algorithm use the modified filtered-x structure, which exhibits faster convergence than other filtering schemes. Simulation results show that the proposed approaches exhibit better performance than the conventional AP algorithm and represent a meaningful choice for practical multichannel ANC applications.

An affine projection algorithm with variable step size and projection order

It is known that the performance of adaptive algorithms is constrained by their computational cost. Thus, affine projection adaptive algorithms achieve higher convergence speed when the projection order increases, which is at the expense of a higher computational cost. However, regardless of computational cost, a high projection order also leads to higher final error at steady state. For this reason it seems advisable to reduce the computational cost of the algorithm when high convergence speed is not needed (steady state) and to maintain or increase this cost only when the algorithm is in transient state to encourage rapid transit to the permanent regime. The adaptive order affine projection algorithm presented here addresses this subject. This algorithm adapts its projection order and step size depending on its convergence state by simple and meaningful rules. Thus it achieves good convergence behavior at every convergence state and very low computational cost at steady state.

GPU implementation of multichannel adaptive algorithms for local active noise control

Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.

Transient Analysis of the Conventional Filtered-x Affine Projection Algorithm for Active Noise Control

Affine projection (AP) algorithms have been proposed in recent years for use in active noise control systems. This is due to their potential high convergence speed along with their robustness and moderate computational cost. However, these algorithms can exhibit an excessive computational cost for high projection orders (just when higher convergence speed is achieved). Thus, computationally efficient versions of these algorithms have been proposed. For the particular case of the AP algorithms applied to active noise control, the use of the conventional filtered-x structure instead of the commonly used modified filtered-x method can be understood as an efficient strategy, since it needs fewer operations to update the adaptive filter coefficients. However, the use of this structure implies different algorithm behavior for the following two reasons: the signals needed in the coefficient updates do not correspond exactly to the AP algorithm and this structure introduces a delay between the update of the adaptive filter coefficients and its effect on the noise signal. In practice, this dual effect mainly affects convergence of the algorithms in the transient regime. This correspondence presents a mathematical model so that the transient behavior of the conventional filtered-x AP algorithm can be predicted from the reference signal statistics and algorithm parameters.

Some practical insights in multichannel active noise control equalization

In this paper, a novel adaptive algorithm for equalization of periodic signals using active control is developed. This algorithm actively cancels out a single frequency without changing the amplitude levels at close frequencies. The algorithm performance is compared with that of an adaptive equalizer previously reported (Kuo and Ji 1995 and Kuo and Morgan 1996). Both algorithms are used for a real multichannel system in order to equalize some given frequencies of a primary signal. The local active noise control system has been used to create an equalization zone. A two channel system is used and the total pressure is controlled at two microphones. A 4100 type Bruel manikin with two calibrated microphones at the ear canals has been used to obtain sound levels in a hypothetic listeners ears. The equalization points are not only the error sensors positions but an area around them which is measured using the manikin.

Headphone-based spatial sound with a GPU accelerator

Multichannel acoustic signal processing has undergone major development in recent years. The incorporation of spatial information into an immersive audiovisual virtual environment or into video games provides better sense of “presence” to applications. Spatial sound consists in reproducing audio signals with spatial cues (spatial information embedded in the sound) through headphones. This spatial information allows the listener to identify the virtual positions of the sources corresponding to different sounds. Headphone-based spatial sound is obtained by filtering different sound sources through special filters called Head Related Transfer Functions (HRTFs) prior to render them through headphones. Efficient computation plays an important role when the number of sources to be managed is high. This situation increases the number of filtering operations, requiring high computing capacity specially when the virtual sources are moving. Graphics Processing Units (GPUs) are high parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. This paper discusses the design, the implementation and the performance of a headphone-based spatial audio application whose processing is totally carried out on a GPU. This application is able to interact with the listener who can select and change the location of the sound sources in real-time. This work analyzes also specific computational aspects inside the CUDA environment in order to successfully exploit GPU resources. Results show that the proposed application is able to move up to 2500 sources simultaneously, while leaving free CPU resources for other tasks. This work emphasizes the importance of analyzing all CUDA aspects, since they can influence drastically the performance.

Block-based distributed adaptive filter for active noise control in a collaborative network

This paper considers the implementation of an Active Noise Control (ANC) system over a network of distributed acoustic nodes. Single-channel nodes composed of one microphone, one loudspeaker, and a processor with communication capabilities have been considered. An equivalent solution to the Multiple Error Filtered-x Least Mean Square algorithm (Me-FxLMS) has been chosen because is a widely used algorithm in ANC systems with centralized processing. The proposed algorithm has been implemented with block-data processing as commonly happens in practical systems. Furthermore, the algorithm works in the frequency domain and with partitioning of the filters for improving its efficiency. Therefore, we present a new formulation to introduce a distributed algorithm based on the Me-FxLMS together with an incremental collaborative strategy in the network. Results demonstrate that the scalable and versatile distributed algorithm exhibits the same performance than the centralized version. Moreover, the computational complexity and some implementation aspects have been analyzed.

The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel Active Noise Control

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems.

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

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 synthesize the movements of 94 sound sources in real time and, at the same time, can manage 9216 room compensation filters having more than 4000 coefficients each.