# Octave Mix: Data augmentation using frequency decomposition for activity recognition

arXiv: https://arxiv.org/abs/2101.02882 · HTML: https://mlsanity.com/paper/2101.02882/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution](https://mlsanity.com/paper/1904.05049/) (2019)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks](https://mlsanity.com/paper/1706.00527/) (2017)
- [Adversarial AutoAugment](https://mlsanity.com/paper/1912.11188/) (2019)
- [Style Augmentation: Data Augmentation via Style Randomization](https://mlsanity.com/paper/1809.05375/) (2018)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data](https://mlsanity.com/paper/1909.09148/) (2019)
- [Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors](https://mlsanity.com/paper/1708.08989/) (2017)
- [Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks](https://mlsanity.com/paper/1609.03323/) (2016)
- [Attention-based Convolutional Neural Network for Weakly Labeled Human Activities Recognition with Wearable Sensors](https://mlsanity.com/paper/1903.10909/) (2019)
- [HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition](https://mlsanity.com/paper/1810.10929/) (2018)
- [DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition](https://mlsanity.com/paper/1808.04228/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition](https://mlsanity.com/paper/2203.04153/) (2022)

## Abstract (arXiv)

In the research field of activity recognition, although it is difficult to collect a large amount of measured sensor data, there has not been much discussion about data augmentation (DA). In this study, I propose Octave Mix as a new synthetic-style DA method for sensor-based activity recognition. Octave Mix is a simple DA method that combines two types of waveforms by intersecting low and high frequency waveforms using frequency decomposition. In addition, I propose a DA ensemble model and its training algorithm to acquire robustness to the original sensor data while remaining a wide variety of feature representation. I conducted experiments to evaluate the effectiveness of my proposed method using four different benchmark datasets of sensing-based activity recognition. As a result, my proposed method achieved the best estimation accuracy. Furthermore, I found that ensembling two DA strategies: Octave Mix with rotation and mixup with rotation, make it possible to achieve higher accuracy.