Search: “audio-codec”

Apple Doesn’t Want You to Worry About the New Apple Watch's Listening Features

Apple Watch Series 12 and Ultra 4 add opt-in audio intelligence features that process microphone audio on-device via a new Secure Exclave.

The Apple Watch Series 12 and Ultra 4 ship with four opt-in audio intelligence features: Sound Recognition, Shazam music identification, Siri Recap conversation summaries, and Live Rewind 15-second transcription. Audio is held and processed in an isolated Secure Exclave buffer on the new S11 chips, with on-device speech recognition on iPhone producing a distilled transcript that foundation models in Private Cloud Compute then summarize. Apple says no raw audio is stored or accessible to the operating system, apps, the user, or Apple, and untransferred audio is automatically deleted.

WIRED · Security·updated Sep 10, 08:18 UTC · 6d ago·first Sep 9, 19:14 UTC · 6d agoAI industry 6 sources1

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

LACE introduces layer-wise compression for dynamic frame rate audio codecs, cutting sequence lengths and speeding TTS inference while preserving quality.

LACE (Layer-Adaptive Codec Encoding) applies an independent compression step at each quantization layer of a neural audio codec, enabling layer-specific segmentation boundaries instead of shared ones. Union alignment and boundary anchor mechanisms keep durations consistent for downstream text-to-speech. On LibriTTS, LACE achieves a better rate-quality tradeoff than prior dynamic frame rate codecs and improves TTS inference efficiency at competitive synthesis quality. Code is released in the ESPnet3 codec recipe.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:46 UTC · 18h agoAI research1

X-AuT: Progressive Audio-Encoder Compression for Speech LLMs with Cross-Scale Distillation

XPeng AI's X-AuT prunes speech LLM audio encoders, cutting Qwen3-ASR-0.6B error from 5.61% to 5.27% with fewer parameters.

X-AuT is a progressive compression framework for speech LLM audio encoders that selects layer combinations via short behavioral probes and restores pruned models using cross-scale distillation and LoRA finetuning while keeping the language-model backbone frozen. Compressing Qwen3-ASR-0.6B from 18 to 16 audio-encoder layers lowered macro-average error from 5.61% to 5.27% on ten Chinese-English benchmarks. A 14-layer model reached 5.75% error with 20.7% fewer audio-tower parameters, and progressive pruning outperformed direct pruning (5.75% vs 6.73%).

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

StepAudio 3 Gen Technical Report

StepAudio 3 Gen unifies TTS, voice design, music, and sound effects via discrete autoregressive modeling over RVQ tokens.

StepAudio 3 Gen is a general-purpose audio generation model covering zero-shot TTS, voice design, vocal generation, sound effects, music, vibe speech, and mixed audio in one framework. It uses discrete autoregressive modeling over residual vector quantization (RVQ) tokens rather than the diffusion Transformer paradigm, with a StepAudio Tokenizer representing audio at 12.5 Hz in a shared 16x2048 residual code space. Key design principles include interference-aware progressive pretraining, an RVQ Adaptor for multi-codebook acoustic representations, and shared discrete autoregressive modeling. The model reports state-of-the-art performance on TTS and voice design while retaining strong generation across speech, vocals, sound effects, and music.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

CodeTD detects hallucinations in code LLMs before execution by analyzing topological patterns of attention maps, outperforming recent baselines.

CodeTD applies topological data analysis (TDA) to code LLM attention maps to quantify prompt-generation mismatch as a pre-execution correctness signal. Experiments cover HumanEval, MBPP, BigCodeBench, and MultiPL-E across 5 programming languages and 10 code LLMs up to 34B parameters. The method outperforms recent baselines and transfers between coding benchmarks, helping catch code that fails the task or embeds security vulnerabilities.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:26 UTC · 8d agoAI research1

TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:55 UTC · 5d agoAI research2

StepAudio 3 Realtime Technical Report

StepAudio 3 Realtime debuts an audio-language model with Think-While-Speaking reasoning, delivering full-duplex voice dialogue with top benchmark results.

StepAudio 3 Realtime is an audio-language foundation model built around a continuous listen-converse-think-act loop for real-time spoken interaction. Think-While-Speaking runs private reasoning in parallel with speech, reaching a 73.0 macro average on StepAudioChat in reasoning mode. The model reports 90.6 on MMSU, 98.9 overall on the Artificial Analysis Full-Duplex Bench, and 56.0% macro task success on tau-Voice. An integrated Voice Agent handles asynchronous tool execution without disrupting dialogue flow.

Hugging Face daily papers·Sep 11, 20:00 UTC · 4d agoAI research

AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoModel release1

Inaudible sounds used to fingerprint browsers catch AliExpress red-handed

AliExpress was caught using inaudible audio signals to fingerprint visitors' browsers, an outdated but still-active cross-session tracking technique.

Ars Technica reports that AliExpress fingerprinted site visitors by sending inaudible sounds to their browsers, exposing a legacy audio-based fingerprinting technique in commercial use. The method allows persistent tracking despite the technique being widely considered outdated. The finding highlights that legacy tracking methods remain deployed on major e-commerce properties, raising privacy concerns for defenders and privacy teams.

Ars Technica · Security·Aug 24, 19:19 UTC · 22d agoResearch

Coop – Isolated VM Environments for Running Claude Code and Codex

Trail of Bits releases Coop, running Claude Code and OpenAI Codex agents inside isolated virtual machines for safer agentic coding.

Coop, published on GitHub by security firm Trail of Bits, provides isolated VM environments for executing AI coding agents such as Claude Code and Codex. Isolation contains the filesystem and network side effects of autonomous agent actions, reducing risk from unsupervised tool use. The project drew 61 points and 16 comments on Hacker News.

Hacker News · AI·Sep 7, 04:18 UTC · 9d agoAI tools & infraHN 61↑ · 16 comments1

StepAudio 3 Music Technical Report

StepAudio 3 Music introduces long-form text-controlled music generation using ABC-notation planning and flow-matching diffusion, ranking near the top music arena.

StepAudio 3 Music generates long-form, text-controlled music using a 50-Hz single-codebook tokenizer with 65,536 entries and a flow-matching diffusion Transformer over VAE latents. A Mixture-of-Experts autoregressive model first plans an arrangement in ABC notation (ABC-CoT) before predicting music tokens. With DPO fine-tuning, it tops AudioBox content and production quality scores and reaches Quality Elo 1105 on the Artificial Analysis Music Arena, behind Suno V5.5 and Mureka. Generation covers songs, accompaniment from dry vocals, and cover synthesis up to 5 minutes 30 seconds at 48-kHz output.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoAI research

Training-Free Speech-Centric Omni Understanding with Frozen VLMs

Audio-visual understanding remains challenging because models must jointly interpret spoken content, visual events, and their temporal relationships. Existing omni models typically introduce dedicated audio encoders and rely on expensive audio-video-text training, tightly coupling omni capability to specific VLM backbones and potentially weakening their existing visual and reasoning abilities.…

Hugging Face daily papers·Aug 6, 20:00 UTC · Aug 6, 2026AI research

What Did I Just Say? Self-Listening for Full-Duplex Speech Models

Researchers propose Self-Listening, a full-duplex speech approach feeding realized model speech back as input to improve interruption recovery.

Full-duplex spoken language models can listen and speak simultaneously, but asynchronous text generation, speech synthesis, and playback cause mismatches between what a model believes it said and what the user heard. The paper defines the resulting recovery problem as anchor interruption and proposes Self-Listening, which interleaves user speech, model text, and played speech as input streams. The authors also release AnchorSpeech, a benchmark with homogeneous training and test splits tracking which ordered response items were actually spoken. Experiments show self-listening models achieve better anchoring performance than full-duplex baselines.

Hugging Face daily papers·Sep 3, 20:00 UTC · 12d agoAI research

Building and Evaluating Fixed-Voice Thai TTS from Synthetic Speech

Researchers distill a compact 82M-parameter Thai TTS from synthetic OmniVoice data, enabling on-device fixed-voice synthesis without reference audio.

The paper uses a large voice-cloning model (OmniVoice) as a synthetic data source to train Wayu-Paxa-TTS-Edge, an 82M-parameter fixed-voice Thai TTS student. The model achieves 68.2% Challenge-Set Keyword Accuracy (85.5% of Gemini 3.1), 91.4% pause precision, and CERs of 3.7% on Thai and 1.1% on English. It outperforms its teacher on pause placement and is open-sourced with its evaluation framework.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

Roland is getting into generative AI music with Melody Flip

Roland launched Melody Flip, a DAW plugin that generates genre-themed MIDI loops rather than complete songs like Suno or Udio.

Roland's Melody Flip is a DAW plug-in offering around 250 genre-based 'Palettes' for generating melodies, chord progressions, basslines, and drum patterns, either from scratch or derived from a reference track. Users can control genre, note density, BPM, and key but cannot use text prompts, and outputs are simple loops with General MIDI-style tones intended for MIDI export into a DAW. The launch follows well-received hardware releases like the SH-4d, Gaia 2, and TR-1000, though music-community sentiment toward generative AI may limit goodwill.

The Verge · AI·Sep 4, 17:51 UTC · 11d agoAI industry

microsoft/VibeVoice-ASR-Streaming-7B — new model trending #27 on Hugging Face

Microsoft released VibeVoice-ASR-Streaming-7B, an open streaming ASR model with speaker attribution, custom hotwords, and support for 10 languages under MIT license.

Microsoft Research released VibeVoice-ASR-Streaming-7B on Hugging Face, a unified streaming speech recognition model that continuously transcribes who said what as speech arrives. The 7B model supports customized hotwords for domain-specific terms and 10 languages including Chinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish. Code is available at github.com/microsoft/VibeVoice with a live demo, and a technical report is on arXiv (2609.02812). The model is licensed under MIT.

Hugging Face trending models·Sep 2, 15:46 UTC · 13d agoModel release1

Kraken: LLM-based Speech-to-Speech Translation via Low-bitrate VQ and Dual-path Source Conditioning

Kraken speech-to-speech translation model builds on Qwen3-8B with low-bitrate vector quantization and source-conditioned vocoding.

Kraken augments a pre-trained Qwen3-8B LLM with speech feature inputs and low-bitrate single-layer vector-quantized tokens trained to reconstruct SSL features. A separate token-to-waveform decoder, Autowave-X, is conditioned on source speech to improve non-linguistic transfer. Training used 150k hours of multilingual and multitask speech data. The model reportedly beats SeamlessM4T-Large v2 and Qwen2.5-Omni in translation quality and speaker/prosody transfer.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:41 UTC · 4d agoAI research1

Skullcandy Dime 3 earbuds expose users to Bluetooth hijacking

CERT/CC warns Skullcandy Dime 3 earbuds accept silent Bluetooth pairings via CVE-2025-20701, letting nearby attackers hijack audio and microphone.

CERT/CC reports the Skullcandy Dime 3 (model S2DCW) running firmware 1.0.0.28 is affected by CVE-2025-20701, a high-severity missing-authentication flaw in the Airoha Bluetooth Audio SDK. An attacker in close range can pair without user interaction, then hijack audio playback, access the headset profile, and capture live microphone audio. Skullcandy fixed the issue in firmware 1.0.0.30, but existing units have no consumer-accessible update path via the app. The flaw was discovered by ERNW researchers and affects earbud and headphone products from multiple vendors; Apple patched it for Beats Studio Buds in June.

BleepingComputer·updated Sep 11, 04:16 UTC · 5d ago·first Sep 9, 21:02 UTC · 6d agoVulnerability 3 sourcesCVE-2025-20701

ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation

ENCP calibrates conformal prediction per navigation episode, giving step-level coverage guarantees for vision-language navigation agents despite within-episode dependence.

Episode-Normalized Conformal Prediction (ENCP) rescales a nonconformity score by a VLN policy's residual confidence and calibrates one maximum score per episode, preserving step-level coverage of at least 1−α despite dependence among steps within an episode. Across four VLN policies and three nonconformity scores on R2R and REVERIE, ENCP meets all reported empirical step-coverage targets in seen-to-unseen evaluation. The model-agnostic uncertainty estimates can signal when an agent should defer to a stronger predictor or human assistance.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:42 UTC · 18h agoAI research

IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing

Researchers release IndicTriMix benchmarks and fine-tuned MuRIL and XLM-RoBERTa models for token-level language identification in tri-language code-mixed text.

The paper formulates token-level language identification in code-mixed text as a sequence labeling task and fine-tunes MuRIL and XLM-RoBERTa transformer models for Indian languages. It evaluates on Hindi, Gujarati, and Bengali configurations with manually annotated test sets and proposes two code-mixed generation approaches using parallel trilingual sentences. A public benchmark, annotated test sets, and fine-tuned models are released for reproducibility.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:36 UTC · 5d agoAI research1

Intelligent transcription with Gemini 3.5 Transcribe

Google DeepMind launched Gemini 3.5 Transcribe, a speech-to-text model offering more intelligent transcription as part of the Gemini family.

Google DeepMind announced Gemini 3.5 Transcribe, a new speech-to-text model described as delivering more intelligent transcription. The blog post provides limited technical detail in the available text, with no benchmarks or model sizes given. The release adds a dedicated audio transcription model to the Gemini family.

Google DeepMind·Aug 26, 17:01 UTC · 20d agoModel release

Putting models to the secure coding test: Plan vs default mode

Datadog Security Labs tested Sonnet 5, Composer 2.5, and GPT 5.5 to see if plan mode yields more secure code than default mode.

Datadog Security Labs evaluated whether plan mode produces measurably more secure code than default mode. The test covered three frontier coding models: Sonnet 5, Composer 2.5, and GPT 5.5. The results inform how engineering teams should configure AI coding assistants to reduce insecure code. This is an AI security evaluation, not an incident report.

Datadog Security Labs·Aug 19, 00:00 UTC · 28d agoAI safety & security

Domain-Incremental Learning for Multi-Channel Replay Speech Detection

First continual learning benchmark for multi-channel replay speech detection shows task-specific beamforming cuts catastrophic forgetting across 24 acoustic environments.

Researchers frame replay-attack detection for voice-controlled systems as domain-incremental learning over acoustic environments, evaluating a beamformer-based detector across all 24 environment orderings of the ReMASC corpus with five seeds. Naive sequential fine-tuning raises error rates on previously learned environments by 18.8 points, while elastic weight consolidation halves forgetting but loses plasticity and gradient projection memory is statistically indistinguishable from naive fine-tuning. A task-specific beamformer keeping one spatial front-end per environment significantly improves final and incremental accuracy, and the last environment in a sequence dominates final performance.

arXiv cs.CR·Sep 10, 08:03 UTC · 6d agoResearch1

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 15d agoAI safety & security1

ExecCritic: Learn to Test, Test to Improve for Coding Agents

ExecCritic separates test generation from patching for coding agents, lifting SWE-bench Verified resolution to 72.6%.

ExecCritic pairs a test-verify-revise scaffold with role-specific reinforcement learning: a Test agent writes repository-native tests and a Repair agent fixes code from execution feedback, both using Qwen-3.5-35B-A3B backbones. Post-trained Qwen agents compose to 72.6% on SWE-bench Verified, an 11.4-point gain over the 61.2% no-test baseline, without stronger-model or oracle feedback at evaluation time. The work shows test quality is the key variable: base-agent tests lowered resolution to 57.3% while GPT-5.6-sol tests raised it to 65.3%.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:53 UTC · 7d agoAI research1

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

A survey catalogs inference-efficiency techniques for video and audiovisual LLMs, mapping bottlenecks in sampling, encoding, token reduction, and LLM decoding.

This survey covers inference-efficiency mechanisms for visual and audiovisual video LLMs, reporting reductions in parameters, FLOPs, latency, memory, and token counts. It organizes methods by pipeline stage, covering frame sampling, modality encoding, connector-level token reduction, and LLM prefilling and decoding for systems built since late 2022. The authors compile accuracy-cost comparisons under shared host models and input protocols, identify gaps in audiovisual efficiency and standardized evaluation, and maintain a public repository.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

A Princeton Researcher Proposes Recurrent Looped Transformer (RLT) that Carries Decoder State across Every Token, Fixing 96 Blocks per Token with Unbounded Temporal Depth

Princeton researcher Yifan Zhang proposes Recurrent Looped Transformer, carrying full decoder state across every token for unbounded temporal depth.

Yifan Zhang's technical report defines the Recurrent Looped Transformer (RLT), pairing a causal encoder with a recurrent decoder whose final output and layerwise sliding-window attention cache carry into every subsequent token with no prompt-response boundary reset. The reference configuration ties 48 encoder and 48 decoder layers, executing 96 logical blocks per token while the state path grows to 48t blocks after t tokens at fixed per-token compute. The report details RL replay contracts that rebuild all states under current parameters and exact prefix snapshots for multi-turn serving, but explicitly reports no measured efficiency, reasoning quality, or scaling results.

MarkTechPost·Sep 13, 17:02 UTC · 2d agoAI research1

Sound Debloating of Redundant Checks in Zero-Knowledge Machine-Learning Circuits

Automated framework soundly removes up to 48.7% of redundant constraints in ezkl and zkml ZK-ML circuits, cutting prover time by up to 72.8%.

The framework uses whole-circuit abstract interpretation and a provenance graph to verify that each removed redundant check (range proofs, sign lookups, bit decompositions) remains entailed by the rest of the circuit, provably preserving soundness. It was evaluated on MLP, CNN, RNN, and transformer circuits generated by ezkl and zkml, with up to 25.3 million constraints. It removes up to 48.7% of constraints and reduces prover time by up to 72.8% without weakening security. Under-constrained circuits in deployed ZK systems have previously enabled attackers to forge transactions and bypass verification.

arXiv cs.CR·Sep 9, 13:27 UTC · 6d agoResearch1