# On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

arXiv: https://arxiv.org/abs/2403.17181 · HTML: https://mlsanity.com/paper/2403.17181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 431 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [A Survey on Time-Series Pre-Trained Models](https://mlsanity.com/paper/2305.10716/) (2023)
- [TimeNet: Pre-trained deep recurrent neural network for time series classification](https://mlsanity.com/paper/1706.08838/) (2017)
- [A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications](https://mlsanity.com/paper/2004.04373/) (2020)
- [EEG2IMAGE: Image Reconstruction from EEG Brain Signals](https://mlsanity.com/paper/2302.10121/) (2023)
- [A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions](https://mlsanity.com/paper/2307.05638/) (2023)
- [ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification](https://mlsanity.com/paper/1904.12546/) (2019)
- [An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach](https://mlsanity.com/paper/1801.05412/) (2018)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2101.03655/) (2021)
- [Distance-Based Anomaly Detection for Industrial Surfaces Using Triplet Networks](https://mlsanity.com/paper/2011.04121/) (2020)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2201.09172/) (2022)
- [A Transfer Learning Framework for Anomaly Detection Using Model of Normality](https://mlsanity.com/paper/2011.06210/) (2020)
- [A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems](https://mlsanity.com/paper/2210.01985/) (2022)
- [Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis](https://mlsanity.com/paper/2008.03071/) (2020)
- [Intelligent Radio Signal Processing: A Survey](https://mlsanity.com/paper/2008.08264/) (2020)
- [Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines](https://mlsanity.com/paper/2101.11539/) (2021)
- [Roadmap on Signal Processing for Next Generation Measurement Systems](https://mlsanity.com/paper/2111.02493/) (2021)
- [A Hybrid Method for Condition Monitoring and Fault Diagnosis of Rolling Bearings With Low System Delay](https://mlsanity.com/paper/2208.06051/) (2022)
- [Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments](https://mlsanity.com/paper/2006.02610/) (2020)
- [Real-time noise cancellation with Deep Learning](https://mlsanity.com/paper/2011.03466/) (2020)
- [Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case](https://mlsanity.com/paper/2205.02706/) (2022)
- [Decomposition of Higher-Order Spectra for Blind Multiple-Input Deconvolution, Pattern Identification and Separation](https://mlsanity.com/paper/1901.11395/) (2019)
- [A Data-Driven Approach for Linear and Nonlinear Damage Detection Using Variational Mode Decomposition and GARCH Model](https://mlsanity.com/paper/2111.08620/) (2021)
- [Similarity-Based Predictive Maintenance Framework for Rotating Machinery](https://mlsanity.com/paper/2212.14550/) (2022)
- [On the Peak-to-Average Power Ratio of Vibration Signals: Analysis and Signal Companding for an Efficient Remote Vibration-Based Condition Monitoring](https://mlsanity.com/paper/2310.01718/) (2023)

## Built on by (most cited first, 1 of 1)

- [Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis](https://mlsanity.com/paper/2404.15880/) (2024)

## Abstract (arXiv)

Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.