Use of Transform Domain Least Mean Square Algorithm in Active Noise Control for Music Noise Reduction

This study focuses on the use of the Transform Domain Least Mean Square (TDLMS) algorithm to reduce music noise from outdoor concerts. Outdoor concerts are ambivalent noise sources because concert sounds please the audience, while irritating nearby residents. Active noise control based on the Least Mean Square (LMS) algorithm can be an applicable way for noise reduction. However, when music noise serves as an input signal in the algorithm, the statistical characteristics of music noise degrade the convergence rate of the LMS algorithm. In this study, TDLMS is applied to compensate for the degraded convergence behavior caused by the music noise. Discrete Cosine Transform (DCT) is selected as a fixed orthogonal transform of TDLMS algorithm. To evaluate the improvement of convergence behavior in music noise control, an outdoor experiment with single-channel active noise control based on the Filteredx LMS algorithm and TDLMS is conducted. Root Mean Square (RMS) between 100 [Hz] to 1 [kHz] is used as a performance index to analyze the convergence rate analysis. Improvement of convergence rate with TDLMS is validated in three different sample music controls. The experiment result shows a faster convergence rate upon the TDLMS application compared to the Filtered-x LMS convergence rate.