Exponential Weighting Method for Sample-by-Sample Update of Warped AR-Model

Kari Roth; Ismo Kauppinen
DAFx-2004 - Naples
Auto-regressive (AR) modeling is a powerful tool having many ap­ plications in audio signal processing. The modeling procedure can be focused to low or high frequency range using frequency warp­ ing. Conventionally the AR-modeling procedure is accomplished with frame-by-frame processing which introduces latency. As with any frame-by-frame algorithm full frame has to be available for the algorithm before any output can be produced. This latency makes AR-modeling more or less unusable in real-time sound effects es­ pecially when long frame lengths are required. In this paper we introduce an exponential weighting (EW) method for sample-bysample update of the warped AR-model. This method reduces the latency down to the order of the AR-model.