"Mel audio" refers to audio that has been analyzed or represented using the mel scale. It's not a different type of audio file format, but rather a method of processing audio to better align with human hearing perception.
The core concept is the mel scale. The mel scale is a perceptual scale that approximates the non-linear frequency response of the human ear. This means that equal distances on the mel scale correspond to frequencies that humans perceive as being roughly equally far apart, whereas on a standard linear frequency scale, our perception of distance changes. We are more sensitive to differences in lower frequencies than in higher frequencies.
Why Use the Mel Scale for Audio?
Analyzing audio using the mel scale offers significant advantages for tasks that involve human perception:
- Perceptual Relevance: By weighting frequencies according to how humans hear, mel-based representations are more relevant for tasks like speech recognition, music analysis, and audio classification where the goal is often to mimic or understand human interpretation.
- Feature Extraction: It provides a basis for extracting features that are robust and discriminative for these perceptual tasks.
How Mel Spectrograms Relate
A common representation using the mel scale is the mel spectrogram. A standard spectrogram shows the intensity of frequencies over time. A mel spectrogram does the same, but the frequency axis is warped according to the mel scale.
- To create a mel spectrogram, the STFT (Short-Time Fourier Transform) is used just like before, splitting the audio into short segments to obtain a sequence of frequency spectra.
- Instead of keeping the linear frequency bins from the STFT, these frequency bins are mapped onto the mel scale using a set of triangular filters.
- The energy within each mel-scaled filter band is then calculated, resulting in a sequence of mel-frequency energy vectors over time, forming the mel spectrogram.
This process effectively compresses the frequency information, particularly at higher frequencies, mirroring how our ears perceive sound.
Common Applications
Representations like mel spectrograms or features derived from them (such as Mel-Frequency Cepstral Coefficients - MFCCs) are widely used in:
- Speech recognition and speaker verification systems
- Music genre classification
- Audio event detection
- Environmental sound analysis
In summary, "mel audio" processing transforms raw audio data into a representation that better reflects how humans perceive pitch and frequency, primarily by applying the non-linear mel scale.
