CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
High-fidelity audio compression with improved rvqgan
6 Pith papers cite this work. Polarity classification is still indexing.
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Q2D2 uses 2D geometric grid projections to quantize feature pairs in neural audio codecs, yielding implicit codebooks that improve efficiency and utilization over RVQ, VQ, and FSQ while maintaining reconstruction quality.
HybridCodec combines discrete tokens with continuous residuals via a focal modulation codec and hybrid Transformer to improve speaker retention and reduce autoregressive steps in speech language models.
A Transformer predicts tokens from neural audio codecs (EnCodec, DAC, X-Codec) to convert expressive drum grids into audio, trained and evaluated on the E-GMD dataset using objective metrics.
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.
citing papers explorer
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Codec-Robust Attacks on Audio LLMs
CodecAttack perturbs audio in codec latent space with multi-bitrate EoT to achieve 85.5% average ASR on Opus-compressed Audio LLMs versus under 26% for waveform baselines, with transfer to MP3 and AAC.
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Two-Dimensional Quantization for Geometry-Aware Audio Coding
Q2D2 uses 2D geometric grid projections to quantize feature pairs in neural audio codecs, yielding implicit codebooks that improve efficiency and utilization over RVQ, VQ, and FSQ while maintaining reconstruction quality.
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HybridCodec: Modeling Discrete and Continuous Representations for Efficient Speech Language Models
HybridCodec combines discrete tokens with continuous residuals via a focal modulation codec and hybrid Transformer to improve speaker retention and reduce autoregressive steps in speech language models.
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Drum Synthesis from Expressive Drum Grids via Neural Audio Codecs
A Transformer predicts tokens from neural audio codecs (EnCodec, DAC, X-Codec) to convert expressive drum grids into audio, trained and evaluated on the E-GMD dataset using objective metrics.
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Woosh: A Sound Effects Foundation Model
Woosh is a new publicly released foundation model optimized for high-quality sound effect generation from text or video, showing competitive or better results than open alternatives like Stable Audio Open.
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Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents
Audio language models are benchmarked on five semantic and paralinguistic reasoning tasks to reveal limitations in handling spoken audio evidence, accent variation, and domain shifts.