Scott G. Norcross

Factors influencing the perception of bass

It is commonly assumed that low‐frequency reverberation time determines the perception of bass in concert halls. Laboratory experiments were conducted to evaluate whether this and other factors influence subjects’ perceptions of the strength of bass sounds in simulated sound fields. The levels of the early and late arriving low‐frequency sounds as well as low‐frequency reverberation times were systematically varied. Ten subjects rated the strength of the bass content of the music for each sound field on a five point scale relative to a reference sound field. Both the levels of the early and late arriving low‐frequency sound had significant effects on the judgments of the bass content. Low‐frequency reverberation time was not significantly related to subjective ratings of bass. The direction of arrival of low‐frequency sound had smaller effects on the assessments of the bass content in the sounds. The results suggest that increased low‐frequency reverberation time is not important for increasing the sense ...

Computational load reduction of fast convergence algorithms for multichannel active noise control

In this paper, the computational load of fast convergence recursive least-squares algorithms for multichannel active noise control (ANC) is reduced by the use of an inverse model of the acoustic plant between the actuators and the error sensors. The complexity reduction applies to both classical recursive least-squares algorithms or their fast time series or order-recursive implementations. To develop the new algorithm, a comparison of several control structures (filtered-x, adjoint, filtered-e, inverse filtered-x, delay-compensated) available for the training of adaptive FIR filters in ANC is performed, based on three main factors that affect the convergence speed of the learning algorithms: correlation of input signals and acoustic plant, delay between the filters and the error signals, and filtering of the error signals. Stochastic gradient descent algorithms and recursive least-squares algorithms are combined with the different structures, and the resulting algorithms are compared based on the three factors. Several of the resulting algorithms have never been published, but of those new algorithms only one algorithm has the potential for optimal convergence speed, based on the three factors. Not only can this algorithm provide fast convergence, but for multichannel systems it also provides a large reduction of the computational load compared to the previously published algorithm with the fastest convergence. Therefore it is introduced in detail in the paper, and simulation results are presented to validate the convergence behavior of the new proposed algorithm.

A comparison of algorithms and the development of a new fast convergence and reduced computational load algorithm for multichannel active noise control

In this presentation, the three main factors that affect the convergence speed of learning algorithms for adaptive FIR filters used in multichannel active noise control are described. Based on these three factors, a comparison of several adaptive FIR filter algorithms for multichannel active noise control is done, including several existing algorithms and a few unpublished algorithms. Of the unpublished algorithms, one algorithm has the potential for optimal convergence speed, and this algorithm is described in more detail in the presentation. The algorithm combines the use of recursive‐least‐squares algorithms with the use of an inverse model of the multichannel acoustic plant between the actuators and the error sensors. The resulting algorithm is called the multichannel inverse delay‐compensated filtered‐x RLS algorithm for active noise control. This algorithm can not only provide fast convergence, but for multichannel systems it also provides a significant reduction of the computational load compared t...