# A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation

arXiv: https://arxiv.org/abs/2602.21476 · HTML: https://mlsanity.com/paper/2602.21476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 47 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity](https://mlsanity.com/paper/1909.08494/) (2019)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed](https://mlsanity.com/paper/1909.01174/) (2019)
- [All for One and One for All: Improving Music Separation by Bridging Networks](https://mlsanity.com/paper/2010.04228/) (2020)
- [The Cocktail Fork Problem: Three-Stem Audio Separation for Real-World Soundtracks](https://mlsanity.com/paper/2110.09958/) (2021)
- [SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision](https://mlsanity.com/paper/2510.02797/) (2025)
- [Separate and Reconstruct: Asymmetric Encoder-Decoder for Speech Separation](https://mlsanity.com/paper/2406.05983/) (2024)
- [Source separation with weakly labelled data: An approach to computational auditory scene analysis](https://mlsanity.com/paper/2002.02065/) (2020)
- [Music Separation Enhancement with Generative Modeling](https://mlsanity.com/paper/2208.12387/) (2022)
- [A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation](https://mlsanity.com/paper/2309.02539/) (2023)
- [Melodic Phrase Segmentation By Deep Neural Networks](https://mlsanity.com/paper/1811.05688/) (2018)
- [Benchmarks and leaderboards for sound demixing tasks](https://mlsanity.com/paper/2305.07489/) (2023)
- [The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation](https://mlsanity.com/paper/2205.08579/) (2022)
- [Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual Support](https://mlsanity.com/paper/2407.07275/) (2024)
- [Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3](https://mlsanity.com/paper/2306.09382/) (2023)
- [DnR-nonverbal: Cinematic Audio Source Separation Dataset Containing Non-Verbal Sounds](https://mlsanity.com/paper/2506.02499/) (2025)

## Abstract (arXiv)

We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. "Knowledge" here denotes information associated with the data, such as music scores. "Model" here refers to tool that can be used for audio segmentation and recognition, such as hidden Markov models. In contrast to conventional learning that often relies on annotated data with given segment categories and their corresponding boundaries to guide the learning process, the proposed framework does not depend on any pre-segmented training data and learns directly from the input audio and its related knowledge sources to build all necessary models autonomously. Evaluation on simulation data shows that score-guided learning achieves very good music segmentation and separation results. Tested on movie track data for cinematic audio source separation also shows that utilizing sound category knowledge achieves better separation results than those obtained with data-driven techniques without using such information.