# Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings

arXiv: https://arxiv.org/abs/2208.12753 · HTML: https://mlsanity.com/paper/2208.12753/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 25 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification](https://mlsanity.com/paper/1504.01561/) (2015)
- [Multi-View Self-Attention Based Transformer for Speaker Recognition](https://mlsanity.com/paper/2110.05036/) (2021)

## Built on by (most cited first, 5 of 5)

- [Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom](https://mlsanity.com/paper/2410.07834/) (2024)
- [A Closed-Loop Personalized Learning Agent Integrating Neural Cognitive Diagnosis, Bounded-Ability Adaptive Testing, and LLM-Driven Feedback](https://mlsanity.com/paper/2510.22559/) (2025)
- [TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis](https://mlsanity.com/paper/2510.23062/) (2025)
- [Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Training](https://mlsanity.com/paper/2510.25042/) (2025)
- [Mask-Robust Face Verification for Online Learning via YOLOv5 and Residual Networks](https://mlsanity.com/paper/2510.25184/) (2025)

## Abstract (arXiv)

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNUMobile dataset, and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition.