@inproceedings{4d91c3fef6464975a7e45d878018c0e5,
title = "Sound Event Detection with Adaptive Frequency Selection",
abstract = "In this work, we present HIDACT, a novel network architecture for adaptive computation for efficiently recognizing acoustic events. We evaluate the model on a sound event detection task where we train it to adaptively process frequency bands. The model learns to adapt to the input without requesting all frequency sub-bands provided. It can make confident predictions within fewer processing steps, hence reducing the amount of computation. Experimental results show that HIDACT has comparable performance to baseline models with more parameters and higher computational complexity. Furthermore, the model can adjust the amount of computation based on the data and computational budget.",
keywords = "Sound event detection, adaptive computation, convolutional recurrent neural network, feature selection, weight sharing",
author = "Zhepei Wang and Jonah Casebeer and Adam Clemmitt and Efthymios Tzinis and Paris Smaragdis",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021 ; Conference date: 17-10-2021 Through 20-10-2021",
year = "2021",
doi = "10.1109/WASPAA52581.2021.9632798",
language = "English (US)",
series = "IEEE Workshop on Applications of Signal Processing to Audio and Acoustics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "41--45",
booktitle = "2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2021",
address = "United States",
}