Blind Source Separation via Time‐Frequency Localization on Sound Spectrogram for Environmental Sound Source Attribution

ABSTRACT As urban sound environments grow more complex, environmental monitoring increasingly demands not just sound‐level measurement but also source attribution. Current source recognition techniques can identify which sources are present in a mixture, but cannot measure each source's energy contribution, making accountability impossible. Sound source separation overcomes this by isolating individual sources, yet most deep learning methods assume a fixed number of sources, making them unsuitable for environmental attribution. This paper introduces a blind source separation framework based on spectrogram time–frequency localisation, inspired by the human auditory cortex (organ of Corti). The framework dynamically determines the number of separable sources via encoder‐conditioned queries and isolates each source using its time–frequency coordinates. Two key challenges—mirroring and masking—are analysed. Extensive experiments show that the proposed paradigm significantly outperforms state‐of‐the‐art methods in separation accuracy, computational efficiency and memory usage.