mlsanityData to 7 Oct 2026

ESTM: An Enhanced Dual-Branch Spectral-Temporal Mamba for Anomalous Sound Detection

arXiv 2509.02471 ↗First posted 2 Sep 2025cs.SD cs.LGcited by 0

On the map: State-space sequence models (S4, Mamba) → Speech and audio SSMs

Abstract (arXiv)

The core challenge in industrial equipment anoma lous sound detection (ASD) lies in modeling the time-frequency coupling characteristics of acoustic features. Existing modeling methods are limited by local receptive fields, making it difficult to capture long-range temporal patterns and cross-band dynamic coupling effects in machine acoustic features. In this paper, we propose a novel framework, ESTM, which is based on a dual-path Mamba architecture with time-frequency decoupled modeling and utilizes Selective State-Space Models (SSM) for long-range sequence modeling. ESTM extracts rich feature representations from different time segments and frequency bands by fusing enhanced Mel spectrograms and raw audio features, while further improving sensitivity to anomalous patterns through the TriStat-Gating (TSG) module. Our experiments demonstrate that ESTM improves anomalous detection performance on the DCASE 2020 Task 2 dataset, further validating the effectiveness of the proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba). Builds on 24 of its 32 references that are in the map. First posted on arXiv 2 Sep 2025.

Builds on

24 of 32 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024TF-Mamba: A Time-Frequency Network for Sound Source Localization8 Sep
  2. 2024Audio Mamba: Bidirectional State Space Model for Audio Representation Learning5 Jun
  3. 2024Mamba in Speech: Towards an Alternative to Self-Attention21 May
  4. 2024SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model20 May

Other references · 20

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  2. 2019Root Mean Square Layer Normalizationcited by 1,425
  3. 2024Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Dualitycited by 1,283
  4. 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
  5. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 935
  6. 2023Selective Structured State-Spaces for Long-Form Video Understandingcited by 105
  7. 2019MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspectioncited by 57
  8. 2020Pooling Methods in Deep Neural Networks, a Reviewcited by 52
  9. 2019ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detectioncited by 43
  10. 2020Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 43
  11. 2020Anomalous sound detection based on interpolation deep neural networkcited by 33
  12. 2021Flow-based Self-supervised Density Estimation for Anomalous Sound Detectioncited by 15
8 more
  1. 2022Anomalous Sound Detection using Spectral-Temporal Information Fusioncited by 12
  2. 2023CST-former: Transformer with Channel-Spectro-Temporal Attention for Sound Event Localization and Detectioncited by 8
  3. 2020ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detectioncited by 7
  4. 2023Time-weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detectioncited by 4
  5. 2023Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Soundscited by 3
  6. 2023Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detectioncited by 3
  7. 2023First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generationcited by 3
  8. 2024A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detectioncited by 3

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  1. 2024MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System
  2. 2026UD-ASD: A Unified Diffusion Model for Anomalous Sound Detection
  3. 2024Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection
  4. 2026Relative Mismatch: Local-Reference Calibration of Feature-Space Flows for Anomalous Sound Detection ↗
  5. 2025MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection
  6. 2026TLDiffGAN: A Latent Diffusion-GAN Framework with Temporal Information Fusion for Anomalous Sound Detection
  7. 2025Fourier-KAN-Mamba: A Novel State-Space Equation Approach for Time-Series Anomaly Detection
  8. 2024SELD-Mamba: Selective State-Space Model for Sound Event Localization and Detection with Source Distance Estimation