TFBSS: A Blind Source Separation Algorithm Based on Short-Time Fourier Transform Time-Frequency Analysis

In the field of signal processing, TFBSS is a widely adopted algorithm that utilizes short-time Fourier transform (STFT) for time-frequency analysis to effectively perform blind source separation on non-stationary signals. The algorithm operates under a convolutive mixture assumption, where mixed signals are modeled as convolutions of source signals with mixing filters. Through iterative computations—typically involving matrix factorization, independent component analysis (ICA), or optimization techniques—it disentangles individual independent sources from the observed mixture. Key implementation steps often include STFT-based spectrogram calculation, joint diagonalization of covariance matrices across frequency bins, and permutation alignment to resolve frequency-domain inconsistencies. Due to its efficiency and accuracy, TFBSS finds extensive applications in speech recognition (e.g., separating overlapping speakers), image processing (e.g., texture separation), biomedical signal analysis (e.g., EEG artifact removal), and beyond. As signal processing technologies advance, the TFBSS algorithm is anticipated to become increasingly prevalent and pivotal in handling complex real-world scenarios.