To convert audio to amplitude using the MEL (Mel-frequency) spectrum, you can follow these general steps:
Preprocess the Audio:
- Load the audio file into memory.
- If the audio file is in a compressed format (e.g., MP3), decode it into a raw audio format (e.g., PCM) using an appropriate audio library.
Frame the Audio:
- Divide the audio signal into short frames or windows, typically around 20-50 milliseconds each. The choice of frame length may depend on the specific application and audio characteristics.
- Apply a window function (e.g., Hamming window) to each frame to reduce spectral leakage.
Compute the Fourier Transform:
- Apply a Fourier Transform (e.g., Fast Fourier Transform - FFT) to each frame to convert the audio from the time domain to the frequency domain.
- Obtain the magnitude spectrum by calculating the absolute value of the complex FFT result.
Apply the Mel Filterbank:
- Define a set of triangular filters that approximate the human auditory system's frequency response.
- Apply these filters to the magnitude spectrum obtained in the previous step.
- Sum the magnitudes within each filter to obtain the energy or amplitude in each Mel-frequency bin.
Non-linear Compression:
- Apply a non-linear compression (e.g., taking the logarithm) to the filterbank outputs to better represent the perceptual loudness characteristics of the audio.
Optional: Normalize or Standardize the Amplitudes:
- If desired, you can normalize or standardize the amplitude values across frames or across the entire audio to enhance comparability.
These steps outline the general process of converting audio to amplitude using the MEL spectrum. Implementation details may vary depending on the programming language or libraries you use. There are various audio processing libraries (e.g., Librosa in Python) that provide functions specifically designed for MEL spectrum computation, which can simplify the implementation process.