On-line monaural ambience extraction algorithm for multichannel audio upmixing system based on nonnegative matrix factorization

The development of multichannel audio systems has increased the need for multichannel contents. However, the supply of multichannel audio contents is not sufficient for advanced multichannel systems. Therefore, home entertainment manufacturers need upmixing systems, including systems that utilize monaural time-frequency domain information. Therefore, a monaural ambience extraction algorithm based on nonnegative matrix factorization (NMF) has been developed recently. Ambience signals refer to sound components that do not have obvious spatial images, e.g., wind, rain, and diffuse sound. The developed algorithm provides good upmixing performance; however, the algorithm is a batch process and therefore, it cannot be used by home audio manufacturers. In this paper, we propose an on-line monaural ambience extraction algorithm. The proposed algorithm analyzes the dominant components with an on-line NMF algorithm, and extracts the remaining sound as ambience components. Experiments were performed with artificial mixed signals and real music signals, and the performance of the proposed algorithm was compared with the performance of the conventional batch algorithm as a reference. The experimental results show that the proposed algorithm extracts the ambience components as well as the batch algorithm, despite the on-line constraints.