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eess.AS updates on arXiv.org

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling Predictive-Generative Drift Decomposition for Speech Enhancement and Separation Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions Phoneme-Level 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Evaluating MCC-PHAT for the LOCATA Challenge - Task 1 and Task 3
Shoufeng Lin · 2018-09-20 · via eess.AS updates on arXiv.org

This report presents test results for the \mbox{LOCATA} challenge \cite{lollmann2018locata} using the recently developed MCC-PHAT (multichannel cross correlation - phase transform) sound source localization method. The specific tasks addressed are respectively the localization of a single static and a single moving speakers using sound recordings of a variety of static microphone arrays. The test results are compared with those of the MUSIC (multiple signal classification) method. The optimal subpattern assignment (OSPA) metric is used for quantitative performance evaluation. In most cases, the MCC-PHAT method demonstrates more reliable and accurate location estimates, in comparison with those of the MUSIC method.

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