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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
A Novel Approach for Object Based Audio Broadcasting
Mohammad Reza Hasanabadi · 2023-10-25 · via eess.AS updates on arXiv.org

Object Based Audio (OBA) provides a new kind of audio experience, delivered to the audience to personalize and customize their experience of listening and to give them choice of what and how to hear their audio content. OBA can be applied to different platforms such as broadcasting, streaming and cinema sound. This paper presents a novel approach for creating object-based audio on the production side. The approach here presents Sample-by-Sample Object Based Audio (SSOBA) embedding. SSOBA places audio object samples in such a way that allows audiences to easily individualize their chosen audio sources according to their interests and needs. SSOBA is an extra service and not an alternative, so it is also compliant with legacy audio players. The biggest advantage of SSOBA is that it does not require any special additional hardware in the broadcasting chain and it is therefore easy to implement and equip legacy players and decoders with enhanced ability. Input audio objects, number of output channels and sampling rates are three important factors affecting SSOBA performance and specifying it to be lossless or lossy. SSOBA adopts interpolation at the decoder side to compensate for eliminated samples. Both subjective and objective experiments are carried out to evaluate the output results at each step. MUSHRA subjective experiments conducted after the encoding step shows good-quality performance of SSOBA with up to five objects. SNR measurements and objective experiments, performed after decoding and interpolation, show significant successful recovery and separation of audio objects. Experimental results show that a minimum sampling rate of 96 kHz is indicated to encode up to five objects in a Stereo-mode channel to acquire good subjective and objective results simultaneously.