MemNMF: Memory-Augmented NMF on LPC Spectra for Anomalous Sound Detection
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. Most prior work uses spectrogram autoencoders, but reconstructing detailed time--frequency patterns is sensitive to noise and transients, and models can reconstruct some anomalous inputs well, weakening normal--anomaly separation. We propose MemNMF, a constrained reconstruction method that operates on the Linear Predictive Coding spectrum, a compact estimate of the spectral envelope. MemNMF initializes a memory module from an NMF dictionary learned on normal LPC spectra and rec...
| Authors | Phurich Saengthong, Takahiro Shinozaki |
|---|---|
| Primary category | cs.SD |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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