One-Click Audio Sample Organization Using a Hybrid Neural Network

Music producers often find themselves confronted with sprawling and disorganized sample libraries, which can cause inefficiencies in creative workflows. Sample libraries are collections of short audio files, which can be rendered or recorded samples of kicks, snares, vocals, synths, etc. Manual categorization of these audio files becomes time-consuming as the size of a collection grows, which can be remedied by using automated solutions such as sample managers.

However, sample managers are often fully-fledged programs which push producers to redefine existing workflows around the product they use. These programs are costly as well, some single payments upwards of $60 such as Samplism, Sononym, or relying on subscriptions like Splice.

This presentation discusses a minimalist and lightweight approach to sample management, utilizing a hybrid convolutional neural network (CNN) to classify audio samples into categories commonly used in music production. The model combines spectral feature extraction with time-domain analysis, using a 2D CNN and 1D CNN respectively.

By decoupling from bloated suites and focusing on a simple interface, we reduce cognitive load, letting producers focus on creativity-not software. The system is designed as a one-click solution-producers load their sample folder, click "Sort," and receive an organized library in seconds.

Audio Processing

Machine Learning

Audio Classification

Convolutional Neural Network

Hybrid Network

Spectral Analysis 

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2025