Log Mel Spectrogram

A log-Mel spectrogram is a visual representation of audio data, transforming sound waves into a time-frequency image that emphasizes human auditory perception. Current research focuses on leveraging log-Mel spectrograms within various machine learning models, including convolutional neural networks (CNNs), vision transformers (ViTs), and autoencoders, to improve performance in tasks such as speech emotion recognition, sound event detection, and fatigue prediction from running audio. These applications highlight the spectrogram's utility in extracting meaningful features from audio for diverse applications, ranging from health monitoring to music synthesis. The effectiveness and relative computational efficiency of different model architectures and feature augmentation techniques are key areas of ongoing investigation.