Music in Virtual Space: Theories and Techniques for Sound Spatialization and Virtual Reality-Based Stage Performance

This research explores virtual reality as a medium for live concert performance. I have realized compositions in which the individual performing on stage uses a VR head-mounted display complemented by other performance controllers to explore a composed virtual space. Movements and objects within the space are used to influence and control sound spatialization and diffusion, musical form, and sonic content. Audience members observe this in real-time, watching the performer\u27s journey through the virtual space on a screen while listening to spatialized audio on loudspeakers variable in number and position. The major artistic challenge I will explore through this activity is the relationship between virtual space and musical form. I will also explore and document the technical challenges of this activity, resulting in a shareable software tool called the Multi-source Ambisonic Spatialization Interface (MASI), which is useful in creating a bridge between VR technologies and associated software, ambisonic spatialization techniques, sound synthesis, and audio playback and effects, and establishes a unique workflow for working with sound in virtual space

Space Time Exploration of Musical Instruments

Musical instruments are tools used to generate sounds for musical expression. Virtual Reality (VR) and Augmented Reality (AR) musical instruments create sounds that may be spatially disjointed from the instrument controls. Spatial audio processing can be used to position the Extended Reality (XR) musical instruments and their corresponding sounds in the same space. This dissertation investigates novel ways of combining spatial reverb models to improve the naturalness of XR musical instruments. Seven spatial reverb systems, combinations of a shoebox spatial reverb model, a raytracing spatial reverb model, and measured directional room impulse response convolution reverb, were compared in a pilot study. A novel hybrid system of synthetic early reflections and directional room impulse responses was preferred for naturalness when tested over headphones with three instruments created by the author: AR electric guitar, AR drumset, and VR Singing Kite. This research culminated in a concert, Spherical Sound Search, which showcased the preferred hybrid system, the three XR musical instruments, and four re-contextualized spatial audio effects (spatial looping, spatial delay, spatial feedback, and spatial compression). The three pieces in the concert explored different aspects of XR modalities and presented the novel system with spatial audio effects to a larger audience by rendering to an octophonic loudspeaker layout

Spatial Audio and Individualized HRTFs using a Convolutional Neural Network (CNN)

Spatial audio and 3-Dimensional sound rendering techniques play a pivotal and essential role in immersive audio experiences. Head-Related Transfer Functions (HRTFs) are acoustic filters which represent how sound interacts with an individual's unique head and ears anatomy. The use of HRTFs compliant to the subjects anatomical traits is crucial to ensure a personalized and unique spatial experience. This work proposes the implementation of an HRTF individualization method based on anthropometric features automatically extracted from ear images using a Convolutional Neural Network (CNN). Firstly, a CNN is implemented and tested to assess the performance of machine learning on positioning landmarks on ear images. The I-BUG dataset, containing ear images with corresponding 55 landmarks, was used to train and test the neural network. Subsequently, 12 relevant landmarks were selected to correspond to 7 specific anthropometric measurements established by the HUTUBS database. These landmarks serve as a reference for distance computation in pixels in order to retrieve the anthropometric measurements from the ear images. Once the 7 distances in pixels are extracted from the ear image, they are converted in centimetres using conversion factors, a best match method vector is implemented computing the Euclidean distance for each set in a database of 116 ears with their corresponding 7 anthropometric measurements provided by the HUTUBS database. The closest match of anthropometry can be identified and the corresponding set of HRTFs can be obtained for personnalized use. The method is evaluated in its validity instead of the accuracy of the results. The conceptual scope of each stage has been verified and substantiated to function correctly. The various steps and the available elements in the process are reviewed and challenged to define a greater algorithm entity designed for the desired task

Ambisonics

This open access book provides a concise explanation of the fundamentals and background of the surround sound recording and playback technology Ambisonics. It equips readers with the psychoacoustical, signal processing, acoustical, and mathematical knowledge needed to understand the inner workings of modern processing utilities, special equipment for recording, manipulation, and reproduction in the higher-order Ambisonic format. The book comes with various practical examples based on free software tools and open scientific data for reproducible research. The book’s introductory section offers a perspective on Ambisonics spanning from the origins of coincident recordings in the 1930s to the Ambisonic concepts of the 1970s, as well as classical ways of applying Ambisonics in first-order coincident sound scene recording and reproduction that have been practiced since the 1980s. As, from time to time, the underlying mathematics become quite involved, but should be comprehensive without sacrificing readability, the book includes an extensive mathematical appendix. The book offers readers a deeper understanding of Ambisonic technologies, and will especially benefit scientists, audio-system and audio-recording engineers. In the advanced sections of the book, fundamentals and modern techniques as higher-order Ambisonic decoding, 3D audio effects, and higher-order recording are explained. Those techniques are shown to be suitable to supply audience areas ranging from studio-sized to hundreds of listeners, or headphone-based playback, regardless whether it is live, interactive, or studio-produced 3D audio material

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc