An adaptive algorithm for nonstationary active sound-profiling

The use of active sound profiling (ASP) in the automobile industry has been under investigation for several years, and the applications have taken advantage of such techniques, balancing amplitudes instead of simply minimizing the sound pressure level (SPL). This paper presents a novel adaptive algorithm to profile nonstationary disturbances such as the noise generated by a gasoline engine. The new algorithm provides profiling capabilities for nonstationary disturbances and stability properties of the system, whilst expending minimum control effort. Mainly assisted through the use of a reshaping signal, necessary phase information is extracted from nonstationary disturbance signals. To deal with changes in sound as the operating conditions of the engine change, the short-time Fourier transform (STFT) filtered-x least mean square (FXLMS) scheme is introduced to improve the convergence rate. The stability properties are based on the command FXLMS approach, which prevents instabilities caused by magnitude errors in the estimated plant model. Moreover, modification of the STFT-FXLMS scheme improves stability and performance when phase error does exist. In this paper, the performance of the proposed algorithm is demonstrated through a series of simulations configured with either simulated noises or noises from a real engine. The results revealed the effectiveness of the proposed algorithm in profiling nonstationary harmonic noise, and enhancement of stability and performance due to the modification of the STFT-FXLMS scheme.