Using convolutional neural networks to select the optimal wavelet for audio compression

The vast amount of data generated and stored across domains is overwhelming. Noteworthy examples include the Event Horizon Telescope using 3.5 petabytes for the first black hole image, LIGO amassing 4.5 petabytes, and CERN with 487 petabytes. This research addresses the challenge of data storage and transmission using machine learning and wavelet analysis. The goal is to create an efficient data compression method that significantly reduces storage needs without compromising data integrity. Wavelets, mathematical tools which break down signals into coefficients, show promise for compression. Choosing the right wavelet is crucial; our research demonstrates that machine learning can effectively solve this problem. By training a convolutional neural network on spectrograms of audio files, we identify the optimal wavelet for data compression, achieving up to 50% compression with minimal loss.