# Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos

arXiv: https://arxiv.org/abs/1810.08691 · HTML: https://mlsanity.com/paper/1810.08691/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
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- [SMART-Vision: Survey of Modern Action Recognition Techniques in Vision](https://mlsanity.com/paper/2501.13066/) (2025)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [G3R: Generating Rich and Fine-grained mmWave Radar Data from 2D Videos for Generalized Gesture Recognition](https://mlsanity.com/paper/2404.14934/) (2024)
- [HabitAction: A Video Dataset for Human Habitual Behavior Recognition](https://mlsanity.com/paper/2408.13463/) (2024)
- [Transferring Voice Knowledge for Acoustic Event Detection: An Empirical Study](https://mlsanity.com/paper/2110.03174/) (2021)
- [Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset](https://mlsanity.com/paper/2505.20788/) (2025)
- [RARR : Robust Real-World Activity Recognition with Vibration by Scavenging Near-Surface Audio Online](https://mlsanity.com/paper/2508.21167/) (2025)
- [D-CAT: Decoupled Cross-Attention Transfer between Sensor Modalities for Unimodal Inference](https://mlsanity.com/paper/2509.09747/) (2025)
- [Transformer-Driven Multimodal Fusion for Explainable Suspiciousness Estimation in Visual Surveillance](https://mlsanity.com/paper/2512.09311/) (2025)
- [RAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition](https://mlsanity.com/paper/2609.38780/) (2026)

## Abstract (arXiv)

Over the years, activity sensing and recognition has been shown to play a key enabling role in a wide range of applications, from sustainability and human-computer interaction to health care. While many recognition tasks have traditionally employed inertial sensors, acoustic-based methods offer the benefit of capturing rich contextual information, which can be useful when discriminating complex activities. Given the emergence of deep learning techniques and leveraging new, large-scaled multi-media datasets, this paper revisits the opportunity of training audio-based classifiers without the onerous and time-consuming task of annotating audio data. We propose a framework for audio-based activity recognition that makes use of millions of embedding features from public online video sound clips. Based on the combination of oversampling and deep learning approaches, our framework does not require further feature processing or outliers filtering as in prior work. We evaluated our approach in the context of Activities of Daily Living (ADL) by recognizing 15 everyday activities with 14 participants in their own homes, achieving 64.2% and 83.6% averaged within-subject accuracy in terms of top-1 and top-3 classification respectively. Individual class performance was also examined in the paper to further study the co-occurrence characteristics of the activities and the robustness of the framework.