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Hybrid AHS: A Hybrid of Kalman Filter and Deep Learning for Acoustic Howling Suppression
During offline training, a pre-processed signal obtained from the Kalman filter and an ideal microphone signal generated via teacher-forced training strategy are used to train the deep neural network (DNN).
A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification
In this paper, we propose two techniques, namely joint modeling and data augmentation, to improve system performances for audio-visual scene classification (AVSC).
Robust Feature Learning on Long-Duration Sounds for Acoustic Scene Classification
For a more robust ASC system, We propose a robust feature learning (RFL) framework to train the CNN.
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
Enhancing Sound Texture in CNN-Based Acoustic Scene Classification
Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded.
Reducing Model Complexity for DNN Based Large-Scale Audio Classification
Audio classification is the task of identifying the sound categories that are associated with a given audio signal.
Sound Audio and Speech Processing
