The objective of the proposed research is to develop a unified knowledge-guided framework for audio processing that integrates audio segmentation, target music extraction (TME), and cinematic audio source separation (CASS). Real-world audio recordings exhibit highly diverse and overlapping acoustic conditions, where precise temporal boundaries are ambiguous, target sources are not clearly separated, and many sound events are rare or poorly represented in training data. Conventional data-driven approaches struggle under such conditions due to limited supervision and the lack of structured guidance. This research investigates how auxiliary knowledge, such as speech transcripts, musical scores, instrument and artist information, and language-universal speech attributes, can be systematically incorporated to constrain and guide model learning, particularly in resource-limited and real-world scenarios.