Real-time massive convolution for audio applications on GPU

Abstract

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.

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

“…This kind of configuration is detailed in [5]. The value P indicates the number of the overlap-save frames that is configured from the n samples of the input-buffer.…”

“…First approaches to our real-time convolution algorithm were carried out in [5]. In that article, it was presented a GPU implementation that executes multiple convolutions concurrently.…”

GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

“…Moreover, there are many recent contributions that leverage GPUs to accelerate acoustic and audio simulations or realtime applications like: room acoustics [16], acoustics likelihood computation [17], speech recognition [18], RIR (Room Impulse Response) reshaping [19], beamforming [20], sound localization [21] or wave-field synthesis [22]. Furthermore, the filtering on GPU where real-time filtering of multiple data is carried out concurrently has recently been introduced in [23], [24]. However, very few publications [25], [26], [27] deal with the GPU implementation of real-time acoustic applications based on adaptive filtering.…”

High-Speed RS(255, 239) Decoder Based on LCC Decoding

“…The use of many-core processors such as general purpose Graphic Processing Units (GPUs) has recently become attractive for the efficient implementation of signal processing algorithms with high computation requirements, such as the massive convolution implementation in [8], the GPU-based 3D human interface addressed in [12], the visualization system in [26] and many other image-related applications [11,20,22]. Signal processing for wireless communication systems is also a field which requires high computation capabilities.…”