# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Comparative Technical Analysis of Machine Learning Methods  
Beyond the general review of applications, a deeper technical comparison of machine learning techniques used in vibration and acoustic signal analysis is essential to understand their practical strengths and trade-offs. The most widely used methods include classical algorithms such as support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (kNNs), as well as deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM). From the perspective of computational complexity, classical methods such as SVM and DT offer fast training and low resource consumption, making them suitable for embedded diagnostic systems and real-time applications. However, they often rely on manual feature engineering, which can limit their performance when dealing with complex or noisy datasets. In contrast, deep learning methods like CNN and LSTM can automatically extract relevant features from raw signals and achieve superior classification accuracy, particularly for time-series and time-frequency data. CNNs have demonstrated over 95% accuracy in image-based spectrogram diagnostics [7; 29], while LSTM networks have been successful in modeling the temporal evolution of damage in bridge structures and rotating machinery [14; 37]. However, these models require significantly more training data and computational resources, which may pose challenges in real-world deployment. Autoencoders and unsupervised clustering (e.g., K-means) are increasingly used in scenarios where labeled data are scarce. They are effective in anomaly detection and noise filtering but typically lack interpretability and require careful tuning to avoid false positives. In terms of robustness to noise and environmental variability, studies have shown that CNNs and hybrid models combining multiple sensors (vibration, acoustic, current) can achieve accuracy gains of up to 15-20% compared to single-sensor SVM-based classifiers [5; 9; 32]. Additionally, ensemble methods such as random forests and gradient boosting machines (e.g., XGBoost) provide resilience against overfitting and offer built-in feature importance metrics. Ultimately, the choice of method depends on the specific application constraints--real-time responsiveness, explainability, sensor types, data volume, and operational variability. Table 1 summarizes key characteristics of selected machine learning methods in the context of vibration and acoustic diagnostics. A nuanced understanding of the technical characteristics of various machine learning methods is paramount for their effective application in vibroacoustic diagnostics, as the choice often involves a fundamental trade-off between interpretability, computational efficiency, and raw performance. Classical algorithms, including Support Vector Machines (SVMs), Decision Trees (DTs), and k-Nearest Neighbors (kNNs), are favored for their low computational overhead and inherent interpretability, making them suitable for embedded diagnostic systems and real-time applications where resources are constrained. However, their reliance on meticulous manual feature engineering can limit their efficacy when dealing with complex or noisy datasets. Conversely, deep learning architectures like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks excel in automatically extracting hierarchical representations directly from raw signals, achieving superior accuracy--often exceeding 95% for image-based diagnostics--and demonstrating high robustness to noise. Nevertheless, these models demand significantly larger training datasets and greater computational resources, posing challenges for deployment in resource-limited environments. This inherent dichotomy necessitates a context-dependent selection strategy, aligning the chosen method with specific application constraints such as real-time responsiveness, the need for model explainability, available data volume, and operational variability.