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Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognition Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models PSP: An Interpretable Per-Dimension Accent Benchmark for Indic Text-to-Speech Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost Korean aegyo speech shows systematic F1 increase to signal childlike qualities All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation RAS: a Reliability Oriented Metric for Automatic Speech Recognition Speech Enhancement Based on Drifting Models HeadRouter: Dynamic Head-Weight Routing for Task-Adaptive Audio Token Pruning in Large Audio Language Models Hallo-Live: Real-Time Streaming Joint Audio-Video Avatar Generation with Asynchronous Dual-Stream and Human-Centric Preference Distillation Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling Robust Audio-Text Retrieval via Cross-Modal Attention and Hybrid Loss Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition Materialistic RIR: Material Conditioned Realistic RIR Generation SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation ONOTE: Benchmarking Omnimodal Notation Processing for Expert-level Music Intelligence From Image to Music Language: A Two-Stage Structure Decoding Approach for Complex Polyphonic OMR ATIR: Towards Audio-Text Interleaved Contextual Retrieval Enhancing Speaker Verification with Whispered Speech via Post-Processing Environmental Sound Deepfake Detection Using Deep-Learning Framework Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps Deep Supervised Contrastive Learning of Pitch Contours for Robust Pitch Accent Classification in Seoul Korean HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models UAF: A Unified Audio Front-end LLM for Full-Duplex Speech Interaction Voice of India: A Large-Scale Benchmark for Real-World Speech Recognition in India Tadabur: A Large-Scale Quran Audio Dataset
A Sonification of the zCOSMOS Galaxy Dataset
S. Bardelli, Claudia Ferretti, Luca Andrea Ludovico, Giorgio Pre · 2022-02-11 · via cs.SD updates on arXiv.org

Sonification is the transformation of data into acoustic signals, achievable through different techniques. Sonification can be defined as a way to represent data values and relations as perceivable sounds, aiming at facilitating their communication and interpretation. Like data visualization provides meaning via images, sonification conveys meaning via sound. Sonification approaches are useful in a number of scenario. A first case is the possibility to receive information while keeping other sensory channels free, like in medical environment, in driving experience, etc. Another scenario addresses an easier recognition of patterns when data present high dimensionality and cardinality. Finally, sonification can be applied to presentation and dissemination initiatives, also with artistic goals. The zCOSMOS dataset contains detailed data about almost 20000 galaxies, describing the evolution of a relatively small portion of the universe in the last 10 million years in terms of galaxy mass, absolute luminosity, redshift, distance, age, and star formation rate. The present paper proposes a sonification for the mentioned dataset, with the following goals: i) providing a general description of the dataset, accessible via sound, which could also make unnoticed patterns emerge; ii) realizing an artistic but scientifically accurate sonic portrait of a portion of the universe, thus filling the gap between art and science in the context of scientific dissemination and so-called "edutainment"; iii) adding value to the dataset, since also scientific data and achievements must be considered as a cultural heritage that needs to be preserved and enhanced. Both scientific and technological aspects of the sonification are addressed.