SoundCount: Sound Counting from Raw Audio with Dyadic Decomposition Neural Network

Yuhang He, Zhuangzhuang Dai, Niki Trigoni, Long Chen*, Andrew Markham

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


In this paper, we study an underexplored, yet important and challenging problem: counting the number of distinct sounds in raw audio characterized by a high degree of polyphonicity. We do so by systematically proposing a novel end-to-end trainable neural network (which we call DyDecNet, consisting of a dyadic decomposition front-end and backbone network), and quantifying the difficulty level of counting depending on sound polyphonicity. The dyadic decomposition front-end progressively decomposes the raw waveform dyadically along the frequency axis to obtain time-frequency representation in multi-stage, coarse-to-fine manner. Each intermediate waveform convolved by a parent filter is further processed by a pair of child filters that evenly split the parent filter’s carried frequency response, with the higher-half child filter encoding the detail and lower-half child filter encoding the approximation. We further introduce an energy gain normalization to normalize sound loudness variance and spectrum overlap, and apply it to each intermediate parent waveform before feeding it to the two child filters. To better quantify sound counting difficulty level, we further design three polyphony-aware metrics: polyphony ratio, max polyphony and mean polyphony. We test DyDecNet on various datasets to show its superiority.

Original languageEnglish
Title of host publicationAAAI-24 Technical Tracks 11
EditorsMichael Wooldridge, Jennifer Dy, Sriraam Natarajan
Number of pages9
ISBN (Electronic)9781577358879, 1577358872
Publication statusPublished - 24 Mar 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468


Conference38th AAAI Conference on Artificial Intelligence, AAAI 2024


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