{ "id": "ep8cczh8wv", "slug": "audio-source-separation", "title": "Audio source separation", "updated": "2026-10-15", "topic_path": [ { "id": "technology", "label": "Technology and the built world", "api_url": "https://www.edgepedia.org/api/v1/topics/technology" }, { "id": "technology.computing", "label": "Computing and digital systems", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing" }, { "id": "technology.computing.ai", "label": "Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/topics/technology.computing.ai" } ], "geo": [ { "id": "geo.nongeo.t1946.technology.computing.ai", "label": "Non-geographic · 1946 to 2000: Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t1946", "label": "Non-geographic · 1946 to 2000", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946" }, { "id": "geo.nongeo.t1946.technology", "label": "Technology and the built world", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology" }, { "id": "geo.nongeo.t1946.technology.computing", "label": "Computing and digital systems", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing" }, { "id": "geo.nongeo.t1946.technology.computing.ai", "label": "Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t1946.technology.computing.ai" } ] }, { "id": "geo.nongeo.t2001.technology.computing.ai", "label": "Non-geographic · 2001 to 2020: Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology.computing.ai", "path": [ { "id": "geo.nongeo", "label": "Non-geographic", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo" }, { "id": "geo.nongeo.t2001", "label": "Non-geographic · 2001 to 2020", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001" }, { "id": "geo.nongeo.t2001.technology", "label": "Technology and the built world", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology" }, { "id": "geo.nongeo.t2001.technology.computing", "label": "Computing and digital systems", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology.computing" }, { "id": "geo.nongeo.t2001.technology.computing.ai", "label": "Artificial intelligence and data", "api_url": "https://www.edgepedia.org/api/v1/geo/geo.nongeo.t2001.technology.computing.ai" } ] } ], "excerpt": "Audio source separation, often called the cocktail-party problem, uses signal processing and machine learning to isolate individual voices or instruments from a mixed recording, serving speech enhancement, music demixing, and ASR front ends.", "snippet": "Audio source separation, often called the cocktail-party problem, uses signal processing and machine learning to isolate individual voices or instruments from a mixed recording, serving speech enhancement, music demixing, and ASR front ends.", "node": "technology.computing.ai", "markdown": "# Audio source separation\n\nAudio source separation is the family of signal processing and machine learning methods that recover individual sound sources, such as one speaker's voice or a single instrument, from a recording in which they are mixed. The task is often called the cocktail-party problem, after the human ability to follow one voice in a noisy room.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Depending on the method, the output is a set of isolated audio stems, a time-frequency mask, or an embedding representation from which sources are reconstructed.[2](https://source-separation.github.io/tutorial/basics/tf_and_masking.html) Typical uses include speech enhancement, music remixing and demixing, and front-end processing for automatic speech recognition (ASR).[1](https://www.merl.com/publications/docs/TR2025-036.pdf) • [3](https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf)\n\n| Key fact | Detail |\n| --- | --- |\n| Outputs | Isolated stems, time-frequency masks with values in [0.0, 1.0], or per-bin embeddings[2](https://source-separation.github.io/tutorial/basics/tf_and_masking.html) • [4](https://www.merl.com/publications/docs/TR2016-003.pdf) |\n| Classical principles | ICA models the mixture as \\( x = As \\); NMF decomposes the magnitude spectrum into a basis matrix times an activation matrix[3](https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf) • [1](https://www.merl.com/publications/docs/TR2025-036.pdf) |\n| Masking assumption | Binary masks assume each time-frequency bin is dominated by one source (W-disjoint orthogonality); soft masks usually sound better[2](https://source-separation.github.io/tutorial/basics/tf_and_masking.html) |\n| Landmark speech result | Conv-TasNet surpasses the ideal masks IBM, IRM, and WFM in SI-SNRi and SDRi with a smaller model[5](https://arxiv.org/pdf/1809.07454) |\n| Best reported WSJ0-2mix | SR-CorrNet-L + DM reaches 25.5 dB SI-SNRi (25.7 dB SDRi)[6](https://export.arxiv.org/pdf/2302.11824v1.pdf) • [7](https://www.sota2.com/research/sota/speech-separation-on-wsj0-2mix-anechoic-clean-mixture-test) |\n| Best reported MUSDB SDR | BS-RoFormer reaches 11.99 dB average SDR on MUSDB18-HQ, ahead of Hybrid Transformer Demucs's 9.20 dB[8](https://github.com/facebookresearch/demucs/) • [9](https://www.sota2.com/research/sota/music-source-separation-on-musdb18-hq-test) |\n| Standard metrics | SDR, SIR, and SAR in dB (bss_eval family); permutation-invariant SI-SNR for speech[10](https://sigsep.github.io/ismir2018_tutorial/index.html) • [11](https://doi.org/10.1109/tsa.2005.858005) |\n\n## How it works\n\n[Blind source separation](https://www.edgepedia.org/blind-source-separation) (BSS) is inherently ill-posed: many combinations of sources can produce the same mixture, so a method must assume something about the sources or the mixing process, most commonly statistical independence or sparsity.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) • [12](https://www.gipsa-lab.grenoble-inp.fr/~pierre.comon/FichiersPdf/HandBook.pdf) [Independent component analysis](https://www.edgepedia.org/independent-component-analysis) (ICA) models the mixture as \\( x = A \\cdot s \\), where \\( s \\) holds latent independent components and \\( A \\) is an unknown mixing matrix; separation estimates an unmixing matrix \\( W \\) so that \\( s = W \\cdot x \\).[3](https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf) Applied to acoustics in the mid 1990s, it assumes statistically independent components, with at most one Gaussian component; some variants also exploit nonstationarity or temporal structure.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) [Non-negative matrix factorization](https://www.edgepedia.org/non-negative-matrix-factorization) (NMF) instead decomposes the mixture's magnitude spectrum into a basis matrix of prototype spectra and an activation matrix, assuming source magnitudes are additive.[1](https://www.merl.com/publications/docs/TR2025-036.pdf)\n\nMask-based methods work in the time-frequency domain. A mask is a matrix the size of the spectrogram with values in [0.0, 1.0], applied by element-wise multiplication; binary masks (0.0 or 1.0) assume each bin is dominated by exactly one source, an assumption called W-disjoint orthogonality, while soft masks are more flexible and usually lead to better sounding results.[2](https://source-separation.github.io/tutorial/basics/tf_and_masking.html) [Deep clustering](https://www.edgepedia.org/deep-clustering) takes another route: a network projects every time-frequency point to a D-dimensional unit-normalized embedding so that points dominated by the same source cluster together, and K-means on the embeddings yields the source masks.[4](https://www.merl.com/publications/docs/TR2016-003.pdf) • [13](https://source-separation.github.io/tutorial/training/building_blocks.html) A network that maps inputs to fixed output slots faces the permutation problem, since any reordering of sources is equally valid; permutation invariant training (PIT) aligns estimated sources with true sources before computing the loss.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Time-domain models such as Conv-TasNet skip the spectrogram and are trained to maximize scale-invariant SNR (SI-SNR).[5](https://arxiv.org/pdf/1809.07454)\n\n## How it is done\n\nA supervised pipeline runs as follows. First, prepare training mixtures, either synthetic mixtures of isolated sources or a labeled dataset such as MUSDB18, which provides 150 full-length tracks (about 10 hours) with drums, bass, vocals, and other stems, split 100 songs for training and 50 for testing.[14](https://sigsep.github.io/datasets/musdb.html) Second, analyze the input with an STFT, or with a learned encoder in time-domain models. Third, run model inference to produce masks, embeddings, or waveforms. Fourth, reconstruct audio: for mask models, compute \\( S_{i} = \\hat{M}_{i} \\odot |Y| \\) and apply an inverse STFT that reuses the mixture phase.[2](https://source-separation.github.io/tutorial/basics/tf_and_masking.html) • [15](https://ar5iv.labs.arxiv.org/html/1909.01174) Fifth, post-process: the multichannel [Wiener filter](https://www.edgepedia.org/wiener-filter)'s iterations improve SIR (less interference) but worsen SAR (more distortion),[10](https://sigsep.github.io/ismir2018_tutorial/index.html) and the shift trick, averaging predictions over random input shifts, improves Demucs by 0.2 points of SDR at a proportional cost in speed.[16](https://github.com/facebookresearch/demucs/tree/v2) [Evaluation](https://www.edgepedia.org/evaluation) uses SDR, SIR, and SAR in dB from the bss_eval family,[10](https://sigsep.github.io/ismir2018_tutorial/index.html) • [11](https://doi.org/10.1109/tsa.2005.858005) and permutation-invariant SI-SNR, the most common evaluation and training metric for [speaker separation](https://www.edgepedia.org/speaker-separation).[17](https://www.mathworks.com/help/audio/ug/end-to-end-deep-speech-separation.html)\n\n## Origin\n\nThe field grew out of blind source separation, whose genesis the Handbook of Blind Source Separation traces to a biological problem.[12](https://www.gipsa-lab.grenoble-inp.fr/~pierre.comon/FichiersPdf/HandBook.pdf) ICA was rigorously defined,[3](https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf) and applied to determined and over-determined acoustic mixtures in the mid 1990s.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Earlier single-channel ideas include the [computational auditory scene analysis](https://www.edgepedia.org/computational-auditory-scene-analysis) (CASA) technique,[18](https://pmc.ncbi.nlm.nih.gov/articles/PMC7181150/) factorial HMMs using multi-dimensional dynamic programming, REPET for repeating backgrounds, and harmonic-percussive separation by median filtering.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) The deep learning turn came when Wang and Wang trained DNNs on spectral features to predict the ideal binary mask for speech enhancement from simulated clean/noisy pairs.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Deep clustering appeared in a 2015 paper by Hershey and colleagues,[19](https://doi.org/10.48550/arxiv.1508.04306) • [4](https://www.merl.com/publications/docs/TR2016-003.pdf) with PIT addressing the permutation problem.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) TasNet (Luo and Mesgarani, 2017) moved separation to the time domain,[20](https://doi.org/10.48550/arxiv.1711.00541) and the same authors' Conv-TasNet (2019) made it fully convolutional.[21](https://doi.org/10.1109/taslp.2019.2915167) • [5](https://arxiv.org/pdf/1809.07454) For music, Wave-U-Net (Stoller, Ewert, and Dixon, 2018) adapted the U-Net to one-dimensional waveforms,[22](https://doi.org/10.48550/arxiv.1806.03185) • [23](https://webspace.eecs.qmul.ac.uk/s.e.dixon/pub/2018/StollerEwertDixon-ISMIR2018.pdf) Demucs (Défossez and colleagues, 2019) added recurrent layers,[24](https://doi.org/10.48550/arxiv.1909.01174) • [15](https://ar5iv.labs.arxiv.org/html/1909.01174) Hybrid Demucs (Défossez, 2021) combined spectrogram and waveform branches,[25](https://doi.org/10.48550/arxiv.2111.03600) and Hybrid Transformer Demucs (Rouard, Massa, and Défossez, 2022) added transformers.[26](https://doi.org/10.48550/arxiv.2211.08553)\n\n## Variants\n\nSingle-channel models process one microphone; multichannel models exploit spatial cues. For underdetermined stereo music, energy-versus-angle algorithms such as DUET, ADRess, and PROJET rely on W-disjoint orthogonality and are often lightweight enough to run in real time.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Demucs' semi-supervised remixing of 2,000 unlabeled songs performed almost on par with spectrogram models trained on five times more labeled data.[15](https://ar5iv.labs.arxiv.org/html/1909.01174) Speech models are benchmarked on WSJ0-style mixtures; music models output four stems (drums, bass, vocals, other).[15](https://ar5iv.labs.arxiv.org/html/1909.01174) Architecturally, the past decade shifted from TF masking with mixture phase toward complex-spectrum and time-domain estimation; DPRNN inspired dual-path state-of-the-art designs, and transformer models include SepFormer and MossFormer.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) • [6](https://export.arxiv.org/pdf/2302.11824v1.pdf) The Chimera network combines a deep clustering head and a mask-inference head in one multi-task model and outperforms either component alone.[27](https://pmc.ncbi.nlm.nih.gov/articles/PMC5791533/) KUIELab-MDX-Net blends four TFC-TDF U-Net models with a pretrained Demucs,[28](https://doi.org/10.48550/arxiv.2111.12203) and CLIPSep enables text-queried separation trained on noisy unlabeled videos.[29](https://doi.org/10.48550/arxiv.2212.07065) Target speaker extraction, which isolates one registered speaker, is another major direction.[1](https://www.merl.com/publications/docs/TR2025-036.pdf)\n\n## Applications\n\nSpeech enhancement was a major breakthrough use of mask-predicting DNNs, trained on simulated clean/noisy pairs to predict the ideal binary mask.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) As ASR front ends, deep clustering-based separation reduced word error rate from 89.1% to 30.8% on two-speaker mixtures.[30](https://www.isca-archive.org/interspeech_2016/isik16_interspeech.pdf) Music demixing is organized around challenges: the hybrid Demucs won the Music Demixing Challenge 2021 organized by Sony,[31](https://mdx-workshop.github.io/proceedings/defossez.pdf) and KUIELab-MDX-Net took second place on leaderboard A and third on leaderboard B at the ISMIR 2021 edition.[28](https://doi.org/10.48550/arxiv.2111.12203) Target speaker extraction serves cases where one registered speaker must be isolated from a mixture.[1](https://www.merl.com/publications/docs/TR2025-036.pdf)\n\n## Limitations and alternatives\n\nReverberation breaks the sparsity and orthogonality assumptions of monaural mask-based separation, which makes dereverberation harder than denoising.[32](https://link.springer.com/article/10.1007/s10462-023-10612-2) Full-rank spatial covariance analysis (FCA) with a multichannel Wiener filter handles cases where W-disjoint orthogonality fails, such as reverberant environments and music.[1](https://www.merl.com/publications/docs/TR2025-036.pdf) Pre-deep-learning techniques are tailored to closed-set speakers and generalize poorly to unseen ones,[32](https://link.springer.com/article/10.1007/s10462-023-10612-2) and PIT cannot handle output-dimension mismatch when the number of speakers at inference differs from training; models handle invalid sources by outputting silence or the mixture.[32](https://link.springer.com/article/10.1007/s10462-023-10612-2) Mask models reuse the mixture phase, so sources sharing a pitch leak artifacts, as when the singer's vibrato is applied to the guitar.[15](https://ar5iv.labs.arxiv.org/html/1909.01174) Transposed strided convolutions cause aliasing (high-frequency buzzing noise), which Wave-U-Net avoids with linear interpolation.[23](https://webspace.eecs.qmul.ac.uk/s.e.dixon/pub/2018/StollerEwertDixon-ISMIR2018.pdf)\n\nAgainst alternatives: statistical beamformers such as MVDR and GEV estimate their weights from signal statistics, and their performance depends on the acoustic environment; on real room impulse responses with two speakers, neural beamforming with dereverberation (BSSD) beat Conv-TasNet with WPE (WER 48.71%) and spatial PIT (WER 42.27%), and most dereverberation methods build on the WPE algorithm.[33](https://ar5iv.labs.arxiv.org/html/2103.13443) Since late 2023, two directions stand out. An AAAI-26 paper by Shi and colleagues frames single-channel separation as a probabilistic inverse problem needing only diffusion priors trained on individual sources, with separation achieved by reconstruction guidance during inverse denoising; initializing from an augmented mixture instead of pure Gaussian noise significantly improves results.[34](https://ojs.aaai.org/index.php/AAAI/article/view/39728) TF-Locoformer (Saijo and colleagues, 2024) combines transformer global modeling with convolutional local modeling for [speech separation](https://www.edgepedia.org/speech-separation) and enhancement.[35](https://doi.org/10.48550/arxiv.2408.03440)\n\n## References\n\n1. [30+ Years of Source Separation Research: Achievements and Future Challenges (MERL, March 2025)](https://www.merl.com/publications/docs/TR2025-036.pdf)\n2. [TF Representations and Masking, Open-Source Tools & Data for Music Source Separation](https://source-separation.github.io/tutorial/basics/tf_and_masking.html)\n3. [Independent Component Analysis: Algorithms and Applications (Hyvärinen & Oja, Neural Networks, 2000)](https://www.cs.helsinki.fi/u/ahyvarin/papers/NN00new.pdf)\n4. [Deep clustering: Discriminative embeddings for segmentation and separation (Hershey, Chen, Le Roux, Watanabe)](https://www.merl.com/publications/docs/TR2016-003.pdf)\n5. [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation (Luo & Mesgarani)](https://arxiv.org/pdf/1809.07454)\n6. [MossFormer: Pushing the Performance Limit of Monaural Speech Separation Using Gated Single-Head Transformer](https://export.arxiv.org/pdf/2302.11824v1.pdf)\n7. [Speech Separation on WSJ0-2Mix anechoic clean mixture (test) benchmark leaderboard · SOTA2 Research](https://www.sota2.com/research/sota/speech-separation-on-wsj0-2mix-anechoic-clean-mixture-test)\n8. [facebookresearch/demucs (v4, Hybrid Transformer Demucs)](https://github.com/facebookresearch/demucs/)\n9. [Music Source Separation on MUSDB18 HQ (test) benchmark leaderboard · SOTA2 Research](https://www.sota2.com/research/sota/music-source-separation-on-musdb18-hq-test)\n10. [Music separation with DNNs: making it work (SiSEC/ISMIR 2018 tutorial)](https://sigsep.github.io/ismir2018_tutorial/index.html)\n11. [E. Vincent, R. Gribonval, C. Fevotte (2006). Performance measurement in blind audio source separation. IEEE Transactions on Audio Speech and Language Processing.](https://doi.org/10.1109/tsa.2005.858005)\n12. [Handbook of Blind Source Separation (Comon & Jutten, eds.)](https://www.gipsa-lab.grenoble-inp.fr/~pierre.comon/FichiersPdf/HandBook.pdf)\n13. [Coding up model architectures, Open-Source Tools & Data for Music Source Separation](https://source-separation.github.io/tutorial/training/building_blocks.html)\n14. [MUSDB18 dataset (SigSep)](https://sigsep.github.io/datasets/musdb.html)\n15. [Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed (Défossez, Usunier, Bottou, Bach, 2019)](https://ar5iv.labs.arxiv.org/html/1909.01174)\n16. [facebookresearch/demucs v2 branch README](https://github.com/facebookresearch/demucs/tree/v2)\n17. [Train End-to-End Speaker Separation Model, MATLAB & Simulink](https://www.mathworks.com/help/audio/ug/end-to-end-deep-speech-separation.html)\n18. [Single Channel Source Separation with ICA-Based Time-Frequency Decomposition (2020)](https://pmc.ncbi.nlm.nih.gov/articles/PMC7181150/)\n19. [Hershey, John R. and colleagues (2015). Deep clustering: Discriminative embeddings for segmentation and separation. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.1508.04306)\n20. [Luo, Yi, Mesgarani, Nima (2017). TasNet: time-domain audio separation network for real-time, single-channel speech separation. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.1711.00541)\n21. [Yi Luo, Nima Mesgarani (2019). Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation. IEEE/ACM Transactions on Audio Speech and Language Processing.](https://doi.org/10.1109/taslp.2019.2915167)\n22. [Stoller, Daniel, Ewert, Sebastian, Dixon, Simon (2018). Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.1806.03185)\n23. [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation (ISMIR 2018)](https://webspace.eecs.qmul.ac.uk/s.e.dixon/pub/2018/StollerEwertDixon-ISMIR2018.pdf)\n24. [Défossez, Alexandre and colleagues (2019). Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.1909.01174)\n25. [Défossez, Alexandre (2021). Hybrid Spectrogram and Waveform Source Separation. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.2111.03600)\n26. [Rouard, Simon, Massa, Francisco, Défossez, Alexandre (2022). Hybrid Transformers for Music Source Separation. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.2211.08553)\n27. [Deep Clustering and Conventional Networks for Music Separation: Stronger Together (Chimera network)](https://pmc.ncbi.nlm.nih.gov/articles/PMC5791533/)\n28. [Kim, Minseok and colleagues (2021). KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.2111.12203)\n29. [Dong, Hao-Wen and colleagues (2022). CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.2212.07065)\n30. [Single-Channel Multi-Speaker Separation Using Deep Clustering (Isik et al., Interspeech 2016)](https://www.isca-archive.org/interspeech_2016/isik16_interspeech.pdf)\n31. [Hybrid Spectrogram and Waveform Source Separation (Hybrid Demucs, MDX Workshop)](https://mdx-workshop.github.io/proceedings/defossez.pdf)\n32. [Deep neural network techniques for monaural speech enhancement and separation: state of the art analysis (Artificial Intelligence Review, Springer)](https://link.springer.com/article/10.1007/s10462-023-10612-2)\n33. [Blind Speech Separation and Dereverberation using Neural Beamforming (BSSD)](https://ar5iv.labs.arxiv.org/html/2103.13443)\n34. [Unsupervised Single-Channel Audio Separation with Diffusion Source Priors (AAAI-26)](https://ojs.aaai.org/index.php/AAAI/article/view/39728)\n35. [Saijo, Kohei and colleagues (2024). TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement. arXiv (Cornell University).](https://doi.org/10.48550/arxiv.2408.03440)\n\n---\n*Topic: Encyclopedia › Technology and the built world › Computing and digital systems › Artificial intelligence and data*\n\n*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*\n\n*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*\n\nLicense: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license\n", "same_as": [], "url": "https://www.edgepedia.org/audio-source-separation", "markdown_url": "https://www.edgepedia.org/audio-source-separation.md", "license": { "name": "Edgepedia Community License 1.0", "url": "https://www.edgepedia.org/edgepedia/license", "summary": "Free with credit, commercial use included. AI training is open to everyone. 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Edgepedia Community License 1.0.", "speakable": "Audio source separation, often called the cocktail-party problem, uses signal processing and machine learning to isolate individual voices or instruments from a mixed recording, serving speech enhancement, music demixing, and ASR front ends." }