Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Remixing music using source separation algorithms to improve the musical experience of cochlear implant users
| Title | Remixing music using source separation algorithms to improve the musical experience of cochlear implant users |
| Publication Type | Journal Article |
| Year of Publication | 2016 |
| Authors | Pons, J. , Janer J. , Rode T. , & Nogueira W. |
| Journal Title | The Journal of the Acoustical Society of America |
| Volume | 140 |
| Issue | 6 |
| Pages | 4338--4349 |
| Journal Date | 12/2016 |
| Abstract | Music perception remains rather poor for many Cochlear Implant (CI) users due to the users' deficient pitch perception. However, comprehensible vocals and simple music structures are well perceived by many CI users. In previous studies researchers re-mixed songs to make music more enjoyable for them, favoring the preferred music elements (vocals or beat) attenuating the others. However, mixing music requires the individually recorded tracks (multitracks) which are usually not accessible. To overcome this limitation, Source Separation (SS) techniques are proposed to estimate the multitracks. These estimated multitracks are further re-mixed to create more pleasant music for CI users. However, SS may introduce undesirable audible distortions and artifacts. Experiments conducted with CI users (N = 9) and normal hearing listeners (N = 9) show that CI users can have different mixing preferences than normal hearing listeners. Moreover, it is shown that CI users' mixing preferences are user dependent. It is also shown that SS methods can be successfully used to create preferred re-mixes although distortions and artifacts are present. Finally, CI users' preferences are used to propose a benchmark that defines the maximum acceptable levels of SS distortion and artifacts for two different mixes proposed by CI users. |
| preprint/postprint document | http://hdl.handle.net/10230/37267 |
| Final publication | http://dx.doi.org/10.1121/1.4971424 |
Additional material:
Code:
- Deep Recurrent Neural Networks for Audio Source Separation: https://github.com/jordipons/DRNN4ASS
- Web-app for audio remixing: https://github.com/jordipons/MT5
