We present a low latency approach for blind source separation (BSS). BSS algorithms generally require a long window to estimate the demixing parameters. In traditional approaches, the long analysis window leads to a long algorithmic delay. Hence, traditional BSS approaches cannot be used in real-time systems. In contrast, our approach reduces the algorithmic delay independently of the window length used for estimation, while retaining separation performance. The new method exploits that the information about the sources provided by additional microphones can be traded against algorithmic delay. The method can be integrated with existing BSS algorithms and can be implemented in the time domain or in the time-frequency domain. Our experimental results confirm the effectiveness of our approach.

In this paper we present a non-iterative impulse response shortening method aiming to reduce the latency of a system. Our method exploits that smoothing the frequency-domain response generally leads to a shorter time-domain response. The method is simple to implement and has a computational complexity that is significantly lower than that of competing methods. Yet it achieves good performance. It can be used for applications involving system identification such as blind source separation (BSS), cross-talk cancellation and channel equalization. Our experimental results confirm the effectiveness of the method, demonstrating the benefit of the approach in the BSS and cross-talk cancelling applications.