Wavelet transforms are mathematical techniques used to analyze signals and data by breaking them down into different frequency components. Unlike traditional methods, which often use sine and cosine functions, wavelets can capture both frequency and location information, making them particularly useful for non-stationary signals, such as audio or images. These transforms allow for multi-resolution analysis, meaning they can provide detailed information at various scales. This property is beneficial in fields like image compression, signal processing, and data analysis, where understanding both the overall structure and fine details of the data is essential for effective interpretation and manipulation.