Work

Music recognition based on diffractive neural networks

Year: 2026

Type: article

Abstract: Diffractive neural networks offer high-speed and energy-efficient computation for artificial intelligence hardware, yet their application to time-series signal processing remains underexplored. Here, we propose an electromagnetic diffractive neural network framework for music style and sentiment classification. Time-series audio signals are converted into log-Mel spectrograms and processed using a multilayer metasurface-based DNN. Experiments on the GTZAN dataset achieved classification accuracies of 90.3% for five genres and 96.33% for three genres. Furthermore, sentiment classification on the POP909 dataset achieved 90.56% accuracy. These results demonstrate that diffractive neural networks can effectively process time-series data and provide a promising hardware-efficient solution for intelligent signal processing in communication and sensing applications.

Source: iScience

DOI: 10.1016/j.isci.2026.115528

Authors: Xingcen Ge, Zirui Feng, Qian Ma

Institutions: Communication University of China, Nanjing, State Key Laboratory of Millimeter Waves, Southeast University

Language: English

Topic: Music and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,240