Mel Analysis Implementation in Audio Fingerprint Systems

In audio fingerprinting systems, performing Mel analysis using MATLAB tools constitutes a critical step. Mel analysis is a signal processing technique that transforms raw audio signals into more manageable spectral representations. MATLAB implementation typically involves several key functions: the `melSpectrogram` function for computing Mel-frequency spectrograms, `mfcc` for extracting Mel-frequency cepstral coefficients (MFCCs), and auxiliary functions for pre-processing like `buffer` for frame segmentation and `hamming` for windowing. The algorithm generally follows these steps: pre-emphasis filtering, frame blocking, windowing, Fast Fourier Transform (FFT), Mel-filter bank application, and logarithm compression. MATLAB's visualization capabilities through `imagesc` or `spectrogram` functions enable effective analysis of spectral features and pattern recognition. This comprehensive toolset facilitates deeper investigation into audio fingerprinting system performance and effectiveness, making MATLAB-based Mel analysis an indispensable component in audio processing pipelines.