Extracting MFCCs from audio samples with TensorFlow
Acoustic models heavily rely on hand-crafted engineering input features to achieve high accuracy. Mel Frequency Cepstral Coefficients (MFCCs) are extensively utilized in audio applications and have demonstrated remarkable success in various use cases, including music genre classification.
In this recipe, we will show you how to extract MFCCs in Python using the TensorFlow signal processing functions (https://www.tensorflow.org/versions/r2.11/api_docs/python/tf/signal):
Getting ready
The primary goal of MFCCs is to combine the temporal information and spectral characteristics of the audio signal in a very compact manner.
In Chapter 4, Using Edge Impulse and Arduino Nano to Control LEDs with Voice Commands, we gave a high-level summary of this feature extraction method. Here, in this chapter, we will delve deeper into its underlying compute blocks for implementing it with the TensorFlow signal processing functions.
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