REVIEW 2 cited by
aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on Raw Audio
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present aTENNuate, a simple deep state-space autoencoder configured for efficient online raw speech enhancement in an end-to-end fashion. The network's performance is primarily evaluated on raw speech denoising, with additional assessments on tasks such as super-resolution and de-quantization. We benchmark aTENNuate on the VoiceBank + DEMAND and the Microsoft DNS1 synthetic test sets. The network outperforms previous real-time denoising models in terms of PESQ score, parameter count, MACs, and latency. Even as a raw waveform processing model, the model maintains high fidelity to the clean signal with minimal audible artifacts. In addition, the model remains performant even when the noisy input is compressed down to 4000Hz and 4 bits, suggesting general speech enhancement capabilities in low-resource environments. Try it out by pip install attenuate
Forward citations
Cited by 2 Pith papers
-
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions
Treating state-space layers as tensor networks with CNN-style connectivity and optimized contraction orders yields hybrid SSM networks that outperform homogeneous SSMs on raw audio tasks and enable competitive streami...
-
DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN
DPDFNet inserts dual-path RNN blocks into DeepFilterNet2's encoder, adds an over-attenuation loss and long-context fine-tuning, and reports superior causal speech enhancement on a 12-language low-SNR test set.
Discussion (0). Continue with ORCID to comment.