Fundamentally, audio data is time-series data. Thus AI uses a time-series algorithm, such as the autoregressive integrated moving average (ARIMA) or exponential smoothing (ES) algorithm for audio classification. However, there is a better method. You use the Spectrogram as an image representing the audio sound, not the time-series numerical array, for input. Using images as the input data, you can leverage the robust neural network algorithm to classify audio more accurately.
Strictly speaking, this topic does not directly pertain to new audio augmentation techniques. Still, it is an essential topic for data scientists to understand. However, Pluto will not write Python code for building a neural network model using Spectrograms as input.
Deep learning image classification, also known as the machine learning model that uses the artificial neural networks algorithm, achieved an unprecedented accuracy level that exceeds 98% accuracy recently. Many AI scientists...