The implementation of delayless subband active noise control algorithms Xiaojun Qiu a and Ningrong Li Key Laboratory of Modern Acoustics Nanjing University, 210093, China Guoyue Chen b Dep. of Electronics & Information Systems Akita Prefectural University, Japan Colin H. Hansen c Active Noise & Vibration Control Group School of Mechanical Engineering University of Adelaide South Australia 5005 Australia ABSTRACT Wideband active noise control systems usually have hundreds of taps for control filters and the cancellation path models, which results in high computational complexity and low convergence speed. Several active noise control algorithms based on subband adaptive filtering have been developed to reduce the computational complexity and to increase the convergence speed. The subband structure is similar to the frequency domain structure but differs in the time domain processing of the subband signals. This paper discusses several issues associated with implementing the delayless subband active noise control algorithms on a DSP platform, such as the modeling of the cancellation path in subbands and the partial update of different subbands. 1 INTRODUCTION Active noise control (ANC) has been successfully applied in some industrial applications such as the active control of sound in headsets, industrial air ducts and propeller aircraft cabins, as well as the active control of noise radiation from large transformers. However, one of the limitations of current active control systems is the limited bandwidth over which they operate. To increase the upper limiting frequency and to extend the attenuation zone of active noise control systems, a higher system sampling rate and multiple channel systems often have to be used. The number of control filter weights can be up to thousands and the number of control channels can be up to hundreds in some cases, and this brings a significant amount of computational load and sometimes even the fastest Digital Signal Processing (DSP) chips currently available cannot meet the need [1-3]. There are several different approaches that can be taken to solve the problem. One possible approach involves the use of a decentralized system, where the entire system is divided into a number of sub-systems, each of which independently adjusts a subset of the actuators’ signals to minimize a subset of the sensors’ signal [4]. A second possible approach is to apply the modal method on all error signals and all control signals to reorganize and decouple the inputs and outputs, so that the effective number of channels being processed can be reduced [5]. A third possible approach is to implement distributed adaptive algorithms with a network control structure, where a set of linearly connected computational modules are used, with each module having an input and output and transmitting data to and receiving data from its nearest neighbours [6]. None of the preceding approaches will be considered here; rather, this paper will focus on a Email address: xjqiu@nju.edu.cn; b Email address: chen@akita-pu.ac.jp c Email address: chansen@mecheng.adelaide.edu.au