MFCCien

MFCCien is a software framework designed to compute and manipulate Mel-frequency cepstral coefficients (MFCCs) and related features for speech and audio analysis. It emphasizes robustness, scalability, and practical integration into speech processing pipelines.

The core pipeline implemented by MFCCien follows the standard MFCC approach with additional options for robustness. It performs pre-emphasis, frames the signal into short segments (typically 20-25 ms), applies a window (commonly Hamming), computes the FFT and power spectrum, applies Mel filter banks, takes logarithms of the filter energies, and uses a discrete cosine transform to obtain MFCCs. Optional features include delta and delta-delta coefficients, liftering, and various normalization schemes such as per-utterance or per-frame mean and variance normalization, including cepstral mean and variance normalization (CMVN). The framework also provides noise-robust variants and configurable floor values to stabilize low-energy frames.

Implementation and interfaces include both high-performance C++ core with user-friendly bindings for Python and Rust. MFCCien is designed for low-latency processing and supports batch and streaming workflows, parallel execution, and easy integration with NumPy-like data structures. It offers configurable pipelines, plug-in filter banks, and modular normalization and augmentation options to tailor feature extraction to specific tasks.

Applications encompass automatic speech recognition, speaker verification, emotion recognition, language identification, and broader audio analytics where robust MFCC-based features are advantageous. The project emphasizes portability, cross-platform support, and open contribution.

History and development notes indicate that MFCCien originated in an academic research setting in the early 2020s, with initial open-source releases and ongoing community updates leading to successive version enhancements.

See also: MFCC, Mel scale, Cepstrum, Cepstral analysis, Speech feature extraction.