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

Multichannel acoustic signal processing has undergone major development in recent years due to the increased complexity of current audio processing applications, which involves the processing of multiple sources, channels, or filters. A general 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 computing 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 filtering 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.