arXiv Machine Learning July 27, 2026 By Phurich Saengthong, Takahiro Shinozaki

MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection

Read the original on arXiv Machine Learning →

arXiv:2607. 22086v1 Announce Type: cross Abstract: Autoencoder-based anomalous sound detection is attractive for machine condition monitoring because it can be trained using only normal recordings and yields an interpretable anomaly score from reconstruction error.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 6

F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation

arXiv:2606. 06357v1 Announce Type: cross Abstract: Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio encoders capture semantics but are not directly decodable.

By Dinghao Zhou, Xingchen Song, Di Wu, Pengyu Cheng, Shengfan Shen, Sixiang Lv

llms nlp

arXiv AI

Jun 11

Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification

arXiv:2606. 11922v1 Announce Type: cross Abstract: Recent respiratory sound classification (RSC) studies largely rely on CLS-token driven self-attention architectures such as the Audio Spectrogram Transformer (AST).

By Hemansh Shridhar, Miika Toikkanen, June-Woo Kim

llms benchmarks

arXiv AI

Jul 21

SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

arXiv:2511. 21325v2 Announce Type: replace-cross Abstract: Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs.

By Ido Nitzan Hidekel, Gal lifshitz, Khen Cohen, Dan Raviv

rag diffusion benchmarks safety

arXiv AI

Jul 2

Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

arXiv:2607. 00720v1 Announce Type: cross Abstract: Despite the increasing sophistication of industrial AI systems, the ability to reliably detect subtle and noisy anomalies in complex time series data remains a critical yet unresolved challenge.

By Seung Hun Han, Hyeongwon Kang, Jinwoo Park, Pilsung Kang

arXiv AI

Jul 21

Time-Frequency Consistency Learning for Robust Speech Deepfake Detection

arXiv:2607. 17761v1 Announce Type: cross Abstract: Recently, speech deepfake detection (SDD) has achieved significant progress.

By Jun Xue, Zhuolin Yi, Yanzhen Ren, Yihuan Huang, Jiayu Xiong, Yi Chai, Guanxiang Feng, Jiajun Liu, Tong Zhang

benchmarks safety

arXiv AI

Aug 7

Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery

arXiv:2608. 05705v1 Announce Type: cross Abstract: Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings.

By Victor Gialis, Maxime Metz, David Esteve, Abdenour Soualhi

llms fine-tuning

Related stories

F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation

Lung-SRAD: Spectral-Aware Regularized Audio DASS with Dual-Axis Patch-Mix Contrastive Learning for Respiratory Sound Classification

SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection

Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning

Time-Frequency Consistency Learning for Robust Speech Deepfake Detection

Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery