Paper Reading AI Learner

Optimized Decoders for Mixed-Order Ambisonics

2022-10-01 21:24:26
Aaron Heller (1), Eric Benjamin (2), Fernando Lopez-Lezcano (3) ((1) Artificial Intelligence Center, SRI International, (2) Surround Research, (3) Center for Computer Research in Music and Acoustics (CCRMA), Stanford University)

Abstract

In this paper we discuss the motivation, design, and analysis of ambisonic decoders for systems where the vertical order is less than the horizontal order, known as mixed-order Ambisonic systems. This can be due to the use of microphone arrays that emphasize horizontal spatial resolution or speaker arrays that provide sparser coverage vertically. First, we review Ambisonic reproduction criteria, as defined by Gerzon, and summarize recent results on the relative perceptual importance of the various criteria. Then we show that using full-order decoders with mixed-order program material results in poorer performance than with a properly designed mixed-order decoder. We then introduce a new implementation of a decoder optimizer that draws upon techniques from machine learning for quick and robust convergence, discuss the construction of the objective function, and apply it to the problem of designing two-band decoders for mixed-order signal sets and non-uniform loudspeaker layouts. Results of informal listening tests are summarized and future directions discussed.

Abstract (translated)

URL

https://arxiv.org/abs/2210.00378

PDF

https://arxiv.org/pdf/2210.00378.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot