arXiv AI
Modeling Time-Dependent Responses of Optical Compressors with Selective State Space Models
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arXiv Machine Learning
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Time-Varying Audio Effect Modeling by End-to-End Adversarial Training
arXiv:2512. 15313v2 Announce Type: replace-cross Abstract: Deep learning has become a standard approach for the modeling of audio effects, yet strictly black-box modeling remains problematic for time-varying systems.
arXiv Machine Learning
Jun 29
Elastic Time: Dynamic Frame Rate Bottlenecks for Neural Audio Coding
arXiv:2606. 27320v1 Announce Type: cross Abstract: Neural audio autoencoders have become a core component of compression, feature extraction, and generation.
arXiv AI
Jun 16
Probing Low Frame Rate Degradation in Neural Audio Codecs
arXiv:2606. 16969v1 Announce Type: cross Abstract: Low frame rates in neural audio codecs are attractive for autoregressive speech synthesis, where the generation cost scales linearly with the sequence length.
arXiv Machine Learning
Sep 25
Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
The paper presents a redesign of the LiSenNet speech‑enhancement model for deployment on the STM32N6570‑DK Neural‑ART microcontroller accelerator. By replacing the recurrent bottleneck with convolutional mixers, converting unsupported operations to static int8 primitives, and using bounded decoder activations, the authors achieve an NPU‑compatible model that matches or surpasses the original LiSenNet in quality (PESQ 3.08 vs 3.01 FP32) while running each 16 ms input hop in 4.83 ms (real‑time factor 0.30). The study demonstrates that co‑designing parameter count, operator compatibility, quantization range, and streaming state is essential for efficient real‑time speech enhancement on constrained NPUs.
arXiv AI
Aug 5
UL-UNAS: Ultra-Lightweight U-Nets for Real-Time Speech Enhancement via Network Architecture Search
arXiv:2503. 00340v2 Announce Type: cross Abstract: Lightweight models are essential for real-time speech enhancement applications.
arXiv AI
Sep 16
Sample-Conditioned Representation Selection for Audio Few-Shot Learning
arXiv:2609.17076v1 Announce Type: new Abstract: Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting represe...