Abstract
Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.
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