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cs.SD updates on arXiv.org

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
Characteristics-Based Design of Generalized-Exponent Band...
Samiya A Alkhairy · 2024-04-05 · via cs.SD updates on arXiv.org

We develop characteristics-based filter design methods for a class of IIR bandpass filters, which we refer to as Generalized-Exponent Filters (GEFs) and that are represented as second-order filters raised to non-unitary exponents. GEFs have a peak, are effectively linear phase, and are useful for seismic signal phase-picking, cochlear implants, and equalizers. The native frequency-domain specifications for GEFs are not on given frequency responses but rather on filter characteristics such as peak frequency, bandwidth, and group delay. Our characteristics-based method for filter design accommodates direct specification of a trio of frequency-domain characteristics from amongst the peak frequency, convexity, ndB quality factors, equivalent rectangular bandwidth, maximum group delay, and phase accumulation. We achieve this by deriving filter parameterizations with sets of filter characteristics which involves deriving closed-form analytic expressions mapping sets of filter characteristics to the original filter constants by making sharp-filter approximations. This results in parameterizations for GEFs including ones with simultaneous specification of magnitude-based and phase-based characteristics (e.g. bandwidths and group delays). This in turn enables designing sharply tuned filters without significant group delay, and simultaneous control over frequency selectivity and synchronization which is important in designing filterbanks. Our filter design methods with direct control over characteristics may also be utilized beyond static filter design for higher-order variable bandpass filter design and may be useful for characteristics-based adaptive filtering. Our methods are inherently stable, highly accurate in meeting strict specifications on desired characteristics, simple, and computationally efficient. The methods extend to the design of related bandpass and multiband filters.