# SincDPNet: Interpretable Raw-Waveform Bathroom Activity Recognition for Assistive Living

arXiv: https://arxiv.org/abs/2609.34907 · HTML: https://mlsanity.com/paper/2609.34907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection](https://mlsanity.com/paper/1702.06286/) (2017)
- [Acoustic Scene Classification](https://mlsanity.com/paper/1411.3715/) (2014)
- [Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions](https://mlsanity.com/paper/1911.02086/) (2019)
- [Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2105.12395/) (2021)
- [Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems](https://mlsanity.com/paper/2105.13734/) (2021)
- [Low-complexity acoustic scene classification in DCASE 2022 Challenge](https://mlsanity.com/paper/2206.03835/) (2022)
- [Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices](https://mlsanity.com/paper/2103.03483/) (2021)
- [Low-complexity CNNs for Acoustic Scene Classification](https://mlsanity.com/paper/2207.11529/) (2022)
- [Low-Complexity Acoustic Scene Classification with Device Information in the DCASE 2025 Challenge](https://mlsanity.com/paper/2505.01747/) (2025)
- [Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and Algorithms](https://mlsanity.com/paper/2506.11169/) (2025)
- [OASI: Objective-Aware Surrogate Initialization for Multi-Objective Bayesian Optimization in TinyML Keyword Spotting](https://mlsanity.com/paper/2512.19739/) (2025)

## Abstract (arXiv)

Bathroom acoustic-event recognition can support ambient assisted living in settings where continuous video monitoring is undesirable. However, practical deployment requires models that are compact, interpretable, and robust to changes in the recording environment. This work introduces dataset, a seven-class bathroom acoustic-event dataset containing 21,387 annotated clips recorded across five environments, and proposes SincDPNet, a compact raw-waveform classifier with a learnable sinc filter bank followed by a depthwise-separable convolutional body. Each sinc filter is controlled by two frequency parameters, allowing the learned passbands to be inspected directly in hertz while keeping the front end small. To reduce room-specific leakage, recording sessions and environments are separated before overlapping windows are assigned to the training, validation, and test partitions. We further use multi-objective Bayesian optimization as a design tool to examine the validation performance--model-size trade-off across 24 configurations. The selected designs span different operating points: the best-performing model achieves 80.2% accuracy and 0.760 macro-F1 with 14,040 parameters, while the compact Nf=25 configuration uses only 2,848 parameters and achieves 75.7% accuracy, 0.661 macro-F1, and 0.716 MCC on the held-out environment. Analysis of the learned filters and confusion patterns shows that spectral overlap contributes to confusion among water-related events, while the textitDoor/textitWalker/Crutch errors also reflect similarities in their transient temporal structure.