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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 Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation Alethia: A Foundational Encoder for Voice Deepfakes From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation DiffAnon: Diffusion-based Prosody Control for Voice Anonymization Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost
The Intrinsic Memorability of Everyday Sounds
David B. Ramsay, Ishwarya Ananthabhotla, Joseph A. Paradiso · 2018-11-17 · via eess.AS updates on arXiv.org

Our aural experience plays an integral role in the perception and memory of the events in our lives. Some of the sounds we encounter throughout the day stay lodged in our minds more easily than others; these, in turn, may serve as powerful triggers of our memories. In this paper, we measure the memorability of everyday sounds across 20,000 crowd-sourced aural memory games, and assess the degree to which a sound's memorability is constant across subjects. We then use this data to analyze the relationship between memorability and acoustic features like harmonicity, spectral skew, and models of cognitive salience; we also assess the relationship between memorability and high-level features with a dependence on the sound source itself, such as its familiarity, valence, arousal, source type, causal certainty, and verbalizability. We find that (1) our crowd-sourced measures of memorability and confusability are reliable and robust across participants; (2) that the authors' measure of collective causal uncertainty detailed in our previous work, coupled with measures of visualizability and valence, are the strongest individual predictors of memorability; (3) that acoustic and salience features play a heightened role in determining "confusability" (the false positive selection rate associated with a sound) relative to memorability, and that (4), within the framework of our assessment, memorability is an intrinsic property of the sounds from the dataset, shown to be independent of surrounding context. We suggest that modeling these cognitive processes opens the door for human-inspired compression of sound environments, automatic curation of large-scale environmental recording datasets, and real-time modification of aural events to alter their likelihood of memorability.