# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 2 Materials and Methods
### Search and Select Documents  
To identify scientific publications on the application of machine learning in the analysis of vibration and acoustic signals, the authors employed a systematic analytical approach. The process involved formulating and executing a query in Scopus, processing and classifying bibliographic data, and analyzing content based on defined thematic and methodological criteria. The selection of articles was carried out using a rational query logic aimed at narrowing the results to technical documents relevant to practical applications of machine learning methods in engineering. The search was conducted using a query that included the following components:  
TITLE (vibration OR acoustics) AND TITLE-ABS-KEY (\"machine learning\")  
The results were limited to publications from the years 2015-2024, published in English, and at the final stage of publication. To narrow the thematic scope, only works containing selected machine learning techniques in the keywords were included, such as Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory, Autoencoders, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Random Forests, and K means Clustering. These nine algorithms also form the basis of the classification framework used throughout the review, ensuring full alignment between the keyword filtering process and the analytical categorization. Additionally, publications from non-engineering-related fields such as medicine, psychology, social sciences, business, arts, agriculture, chemistry, mathematics, neuroscience, theoretical computer science, earth sciences, environmental sciences, and related areas were excluded from the results. Based on this query, 152 documents were obtained and subsequently underwent a selection process. Publications affiliated with countries outside a selected group of twelve nations were excluded: Brazil, China, Germany, India, Iraq, Italy, Japan, Malaysia, Poland, Republic of Korea, UK, and USA. These twelve countries were selected based on preliminary bibliometric analysis, which showed that they collectively accounted for over 90% of all English-language publications retrieved in the initial Scopus query. By focusing on this group, the review preserves broad geographical diversity while minimizing the risk of excluding influential research. This filtering step ensured relevance without compromising the representativeness of the sample. This step allowed for the preservation of geographical diversity while focusing on research-active centers in the field of vibration and acoustic diagnostics. The final number of analyzed publications was 96. The procedure related to data collection and preparation for the article is presented in Figure 1.  
The collected bibliographic data were imported into a relational PostgreSQL database. The source file included fields such as: \"Title\", \"Authors\", \"Affiliation\", \"Year of Publication\", \"Document Type\", \"Keywords\", \"DOI\", \"Abstract\", \"Citations\", \"Publication Stage\", and others. This enabled the structured processing of information and the creation of SQL (Structured Query Language) queries for further aggregation and classification of results. For the purpose of analysis, Python software 3.12.2 was also used along with the following libraries: pandas (for data operations), matplotlib and seaborn (for visualization). The data were prepared in tabular form and categorized according to defined classification criteria. Although machine learning methods are often categorized based on their learning paradigm such as supervised, unsupervised, or deep learning in this review, a unified classification approach was deliberately chosen. Instead of strictly separating algorithms by architectural depth or formal learning type, methods were grouped in a way that reflects their actual use in vibration and acoustic signal analysis. This decision stems from the observation that, in engineering applications, the boundaries between classical and deep learning methods are frequently blurred.