When building audio classification models, whether for voice-control applications or ambient noise monitoring, the quality and diversity of the dataset are key elements to train a robust machine learning model. Collecting and labeling audio data in varied and challenging noise environments can be both time-consuming and resource-intensive. In particular, Signal-to-Noise Ratio (SNR) of audio inputs can greatly influence the performance of models. To address this challenge, we introduce a new synthetic data generation block designed to accelerate the creation of robust audio datasets by mixing audio samples with noise files at specified Signal-to-Noise Ratio (SNR) levels.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/audio-classification-with-the-audio-noise-generator-block