Artificial Intelligence for Acoustics and Audio Signal Processing

Dear Colleagues,

The processing of audio signals through Artificial Intelligence (AI)-driven approaches has gained significant importance in recent years, thanks to its ability to enhance human–machine interaction in the most natural and immediate form: sound. A wide range of applications has emerged—from fault prediction and acoustic-based defect detection in cultural heritage, to medical auscultation, modelling of sound response architecture, or acoustics, music interpretation and generation and speech emotion recognition, among many others.

In these tasks, time–frequency representations have proven to be a crucial step in preprocessing raw signals, enabling effective input formatting for neural networks. Whether using linear, logarithmic, mel-scale or mel-frequency cepstral coefficients, these 2D projections offer a rich domain where AI models can extract meaningful features for classification, detection, or synthesis.

This Special Issue aims to explore and extend the field of spectrogram-based and time–frequency neural recognition, by inviting high-quality original research contributions related (but not limited) to:

• AI and deep learning applied to time–frequency analysis of audio signals
• Spectrogram-based classification and pattern recognition
• Audio signal preprocessing for neural network input
• Audio-based anomaly or defect detection in industrial or cultural heritage domains
• Speech-based emotion or health state recognition
• Music genre recognition and melody and song generation
• Multimodal fusion involving time–frequency features
• Explainable AI for time–frequency models
• Novel architectures for spectrogram understanding (e.g., CNNs, Transformers, Attention models)

We look forward to receiving your submissions and advancing the state of the art in AI-based sound analysis.

Dr. Michele Lo Giudice
Prof. Giosue Caliano
Prof. Alessandro Salvini
Guest Editors

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