Real-time Sliding Phase Vocoder using a Commodity GPU

We describe a new approach to the processing of audio by way of transformations to and from the frequency domain. In previous papers we described the Sliding Discrete Fourier Transform (SDFT), comprising an extension to the classic phase vocoder algorithm to perform a frame update every sample. We proposed this as offering musical advantages over the common STDFT, including lower latency and the potential for new classes of effect. Its major disadvantage has been the very high computational cost, which makes it intractable for real-time use even on high-specification consumer workstations. We report on a version of the SDFT that exploits the intrinsic parallelism of the scheme on a commodity GPU, to implement the transform and its inverse in real time for multiple audio channels. This implementation is used to provide previously uncomputable real-time effects. We describe a key new effect, enabled by the method, which we have called Transformational FM (TFM).</p

Streaming Spectral Processing with Consumer-level Graphics Processing Units

This paper describes the implementation of a streaming spectral processing system for realtime audio in a consumer-level onboard GPU (Graphics Processing Unit) attached to an off-the-shelf laptop computer. It explores the implementation of four processes: standard phase vocoder analysis and synthesis, additive synthesis and the sliding phase vocoder. These were developed under the CUDA development environment as plugins for the Csound 6 audio programming language. Following a detailed exposition of the GPU code, results of performance tests are discussed for each algorithm. They demonstrate that such a system is capable of realtime audio, even under the restrictions imposed by a limited GPU capability

Streaming Spectral Processing with Consumer-level Graphics Processing Units

This paper describes the implementation of a streaming spectral processing system for realtime audio in a consumer-level onboard GPU (Graphics Processing Unit) attached to an off-the-shelf laptop computer. It explores the implementation of four processes: standard phase vocoder analysis and synthesis, additive synthesis and the sliding phase vocoder. These were developed under the CUDA development environment as plugins for the Csound 6 audio programming language. Following a detailed exposition of the GPU code, results of performance tests are discussed for each algorithm. They demonstrate that such a system is capable of realtime audio, even under the restrictions imposed by a limited GPU capability

Compiler techniques for scalable performance of stream programs on multicore architectures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 211-222).Given the ubiquity of multicore processors, there is an acute need to enable the development of scalable parallel applications without unduly burdening programmers. Currently, programmers are asked not only to explicitly expose parallelism but also concern themselves with issues of granularity, load-balancing, synchronization, and communication. This thesis demonstrates that when algorithmic parallelism is expressed in the form of a stream program, a compiler can effectively and automatically manage the parallelism. Our compiler assumes responsibility for low-level architectural details, transforming implicit algorithmic parallelism into a mapping that achieves scalable parallel performance for a given multicore target. Stream programming is characterized by regular processing of sequences of data, and it is a natural expression of algorithms in the areas of audio, video, digital signal processing, networking, and encryption. Streaming computation is represented as a graph of independent computation nodes that communicate explicitly over data channels. Our techniques operate on contiguous regions of the stream graph where the input and output rates of the nodes are statically determinable. Within a static region, the compiler first automatically adjusts the granularity and then exploits data, task, and pipeline parallelism in a holistic fashion. We introduce techniques that data-parallelize nodes that operate on overlapping sliding windows of their input, translating serializing state into minimal and parametrized inter-core communication. Finally, for nodes that cannot be data-parallelized due to state, we are the first to automatically apply software-pipelining techniques at a coarse granularity to exploit pipeline parallelism between stateful nodes. Our framework is evaluated in the context of the StreamIt programming language. StreamIt is a high-level stream programming language that has been shown to improve programmer productivity in implementing streaming algorithms. We employ the StreamIt Core benchmark suite of 12 real-world applications to demonstrate the effectiveness of our techniques for varying multicore architectures. For a 16-core distributed memory multicore, we achieve a 14.9x mean speedup. For benchmarks that include sliding-window computation, our sliding-window data-parallelization techniques are required to enable scalable performance for a 16-core SMP multicore (14x mean speedup) and a 64-core distributed shared memory multicore (52x mean speedup).by Michael I. Gordon.Ph.D

Real-Time Polyphonic Octave Doubling for the Guitar

This thesis studies digital signal processing solutions for enriching live guitar sound by way of mixing-in octave-doubled versions of the chords and melodies performed on the instrument in real-time. Following a review of techniques applicable for real-time polyphonic octave doubling, four candidate solutions are proposed, amongst which two novel methods: ERB-PS2 and ERB-SSM2. Performance of said candidates is compared to that of three state of the art effect pedal offerings of the market. In particular, an evaluation of the added roughness and transient alterations introduced by each solution in the output sound is conducted. The ERB-PS2 method, which consists in doubling the instantaneous phases of the sub-bands signals extracted with a constant-ERB-bandwidth non-decimated IIR filter bank, is found to provide the best overall performance amongst the candidates. This novel solution provides greatly reduced latency compared to the baseline pedals, with comparable, and in some case improved, sound quality

CONTINUOUS OPTIMIZATION OF DISTRIBUTED STREAM PROGRAMS

Ph.DDOCTOR OF PHILOSOPH

Language and compiler support for stream programs

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 153-166).Stream programs represent an important class of high-performance computations. Defined by their regular processing of sequences of data, stream programs appear most commonly in the context of audio, video, and digital signal processing, though also in networking, encryption, and other areas. Stream programs can be naturally represented as a graph of independent actors that communicate explicitly over data channels. In this work we focus on programs where the input and output rates of actors are known at compile time, enabling aggressive transformations by the compiler; this model is known as synchronous dataflow. We develop a new programming language, StreamIt, that empowers both programmers and compiler writers to leverage the unique properties of the streaming domain. StreamIt offers several new abstractions, including hierarchical single-input single-output streams, composable primitives for data reordering, and a mechanism called teleport messaging that enables precise event handling in a distributed environment. We demonstrate the feasibility of developing applications in StreamIt via a detailed characterization of our 34,000-line benchmark suite, which spans from MPEG-2 encoding/decoding to GMTI radar processing. We also present a novel dynamic analysis for migrating legacy C programs into a streaming representation. The central premise of stream programming is that it enables the compiler to perform powerful optimizations. We support this premise by presenting a suite of new transformations. We describe the first translation of stream programs into the compressed domain, enabling programs written for uncompressed data formats to automatically operate directly on compressed data formats (based on LZ77). This technique offers a median speedup of 15x on common video editing operations.(cont.) We also review other optimizations developed in the StreamIt group, including automatic parallelization (offering an 11x mean speedup on the 16-core Raw machine), optimization of linear computations (offering a 5.5x average speedup on a Pentium 4), and cache-aware scheduling (offering a 3.5x mean speedup on a StrongARM 1100). While these transformations are beyond the reach of compilers for traditional languages such as C, they become tractable given the abundant parallelism and regular communication patterns exposed by the stream programming model.by William Thies.Ph.D

PERFORMANCE IMPROVEMENT OF MULTICHANNEL AUDIO BY GRAPHICS PROCESSING UNITS

Multichannel acoustic signal processing has undergone major development in recent years due to the increased complexity of current audio processing applications. People want to collaborate through communication with the feeling of being together and sharing the same environment, what is considered as Immersive Audio Schemes. In this phenomenon, several acoustic e ects are involved: 3D spatial sound, room compensation, crosstalk cancelation, sound source localization, among others. However, high computing capacity is required to achieve any of these e ects in a real large-scale system, what represents a considerable limitation for real-time applications. The increase of the computational capacity has been historically linked to the number of transistors in a chip. However, nowadays the improvements in the computational capacity are mainly given by increasing the number of processing units, i.e expanding parallelism in computing. This is the case of the Graphics Processing Units (GPUs), that own now thousands of computing cores. GPUs were traditionally related to graphic or image applications, but new releases in the GPU programming environments, CUDA or OpenCL, allowed that most applications were computationally accelerated in elds beyond graphics. This thesis aims to demonstrate that GPUs are totally valid tools to carry out audio applications that require high computational resources. To this end, di erent applications in the eld of audio processing are studied and performed using GPUs. This manuscript also analyzes and solves possible limitations in each GPU-based implementation both from the acoustic point of view as from the computational point of view. In this document, we have addressed the following problems: Most of audio applications are based on massive ltering. Thus, the rst implementation to undertake is a fundamental operation in the audio processing: the convolution. It has been rst developed as a computational kernel and afterwards used for an application that combines multiples convolutions concurrently: generalized crosstalk cancellation and equalization. The proposed implementation can successfully manage two di erent and common situations: size of bu ers that are much larger than the size of the lters and size of bu ers that are much smaller than the size of the lters. Two spatial audio applications that use the GPU as a co-processor have been developed from the massive multichannel ltering. First application deals with binaural audio. Its main feature is that this application is able to synthesize sound sources in spatial positions that are not included in the database of HRTF and to generate smoothly movements of sound sources. Both features were designed after di erent tests (objective and subjective). The performance regarding number of sound source that could be rendered in real time was assessed on GPUs with di erent GPU architectures. A similar performance is measured in a Wave Field Synthesis system (second spatial audio application) that is composed of 96 loudspeakers. The proposed GPU-based implementation is able to reduce the room e ects during the sound source rendering. A well-known approach for sound source localization in noisy and reverberant environments is also addressed on a multi-GPU system. This is the case of the Steered Response Power with Phase Transform (SRPPHAT) algorithm. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. The solutions implemented in this thesis are evaluated both from localization and from computational performance points of view, taking into account different acoustic environments, and always from a real-time implementation perspective. Finally, This manuscript addresses also massive multichannel ltering when the lters present an In nite Impulse Response (IIR). Two cases are analyzed in this manuscript: 1) IIR lters composed of multiple secondorder sections, and 2) IIR lters that presents an allpass response. Both cases are used to develop and accelerate two di erent applications: 1) to execute multiple Equalizations in a WFS system, and 2) to reduce the dynamic range in an audio signal.Belloch Rodríguez, JA. (2014). PERFORMANCE IMPROVEMENT OF MULTICHANNEL AUDIO BY GRAPHICS PROCESSING UNITS [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/40651TESISPremios Extraordinarios de tesis doctorale

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010