Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection

no code implementations • 5 Apr 2022 • Bingqing Chen, Luca Bondi, Samarjit Das

Anomaly detection has many important applications, such as monitoring industrial equipment.

Anomaly Detection Density Estimation +1

A Comparison of deep learning methods for environmental sound

1 code implementation • 20 Mar 2017 • Juncheng Li, Wei Dai, Florian Metze, Shuhui Qu, Samarjit Das

On these features, we apply five models: Gaussian Mixture Model (GMM), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolutional Deep Neural Net- work (CNN) and i-vector.

Avg

Learning Filter Banks Using Deep Learning For Acoustic Signals

no code implementations • 29 Nov 2016 • Shuhui Qu, Juncheng Li, Wei Dai, Samarjit Das

Based on the procedure of log Mel-filter banks, we design a filter bank learning layer.

Very Deep Convolutional Neural Networks for Raw Waveforms

10 code implementations • 1 Oct 2016 • Wei Dai, Chia Dai, Shuhui Qu, Juncheng Li, Samarjit Das

Our CNNs, with up to 34 weight layers, are efficient to optimize over very long sequences (e. g., vector of size 32000), necessary for processing acoustic waveforms.

Representation Learning