Julius Kammerl
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              Resonance Audio is an open source project designed for creating and controlling dynamic spatial sound in Virtual & Augmented Reality (VR/AR), gaming or video experiences. It also provides integrations with popular game development platforms and digital audio workstations as a preview plugin. Resonance Audio binaural decoder is used in YouTube VR to provide cinematic spatial audio experiences. This paper describes the core sound spatialization algorithms used in Resonance Audio.
              
  
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              Temporal Synchronization of Multiple Audio Signals
            
          
        
        
          
            
              
                
                  
                    
                
              
            
              
                
                  
                    
                    
                  
              
            
              
                
                  
                    
                    
    
    
    
    
    
                      
                        Sasi Inguva
                      
                    
                  
              
            
              
                
                  
                    
                    
                  
              
            
              
                
                  
                    
                    
                      
                        Andy Crawford
                      
                    
                  
              
            
              
                
                  
                    
                    
                      
                        Hugh Denman
                      
                    
                  
              
            
              
                
                  
                    
                    
                      
                        Anil Kokaram
                      
                    
                  
              
            
              
                
                  
                    
                    
                  
              
            
          
          
          
          
            Proceedings of the International Conference on Signal Processing (ICASSP), Florence, Italy (2014)
          
          
        
        
        
          
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              Given the proliferation of consumer media recording devices, events often give rise to a large number of recordings. These recordings are taken from different spatial positions and do not have reliable timestamp information. In this paper, we present two robust graph-based approaches for synchronizing multiple audio signals. The graphs are constructed atop the over-determined system resulting from pairwise signal comparison using cross-correlation of audio features. The first approach uses a Minimum Spanning Tree (MST) technique, while the second uses Belief Propagation (BP) to solve the system. Both approaches can provide excellent solutions and robustness to pairwise outliers, however the MST approach is much less complex than BP. In addition, an experimental comparison of audio features-based synchronization shows that spectral flatness outperforms the zero-crossing rate and signal energy.
              
  
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