SearcharxivSearch

arXiv · 2103.05719

Spheroidal Ambisonics: a Spatial Audio Framework Using Spheroidal Bases

Abstract

Ambisonics is an established framework to capture, process, and reproduce spatial sound fields based on its spherical harmonics representation. We propose a generalization of conventional spherical ambisonics to the spheroidal coordinate system and spheroidal microphone arrays, which represent sound fields by means of spheroidal wave functions. This framework is referred to as spheroidal ambisonics and a formulation for the case of prolate spheroidal coordinates is presented. Spheroidal ambisonics allows analytical encoding of sound fields using spheroidal microphone arrays. In addition, an analytical conversion formula from spheroidal ambisonics to spherical ambisonics is derived in order to ensure compatibility with the existing ecosystem of spherical ambisonics. Numerical experiments are performed to verify spheroidal ambisonic encoding and transcoding when used for spatial sound field recording. It is found that the sound field reconstructed from the transcoded coefficients has a zone of accurate reconstruction which is prolonged towards the long axis of a prolate spheroidal microphone array.

Explore related subjects

Keep this discovery

BibTeXRIS

Shoken Kaneko. 2021-03-09. Spheroidal Ambisonics: a Spatial Audio Framework Using Spheroidal Bases. https://doi.org/10.1121/10.0005942

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD