Multichannel massive audio processing for a generalized crosstalk cancellation and equalization application using GPUs

Abstract

[EN] Multichannel acoustic signal processing has undergone major development in recent years due to the increased com- plexity of current audio processing applications, which involves the processing of multiple sources, channels, or filters. A gen- eral scenario that appears in this context is the immersive reproduction of binaural audio without the use of headphones, which requires the use of a crosstalk canceler. However, generalized crosstalk cancellation and equalization (GCCE) requires high com- puting capacity, which is a considerable limitation for real-time applications. This paper discusses the design and implementation of all the processing blocks of a multichannel convolution on a GPU for real-time applications. To this end, a very efficient fil- tering method using specific data structures is proposed, which takes advantage of overlap-save filtering and filter fragmentation. It has been shown that, for a real-time application with 22 inputs and 64 outputs, the system is capable of managing 1408 filters of 2048 coefficients with a latency time less than 6 ms. The proposed GPU implementation can be easily adapted to any acoustic environment, demonstrating the validity of these co-processors for managing intensive multichannel audio applications.