Informed approach for sound and music analysis
type: Dataset
description: In the field of audio signal processing, analysis is an essential step which allows interactions with existing signals. In fact, the quality of transformed or synthesized audio signals depends on the accuracy over the estimated model parameters. However, theoretical limits exist and show that the best accuracy which can be reached by a classic estimator can be insufficient for the most demanding applications (e.g. active listening of music). The work which is developed in this thesis revisits well known audio analysis problems like spectral analysis, automatic transcription of music and audio sources separation using the novel ``informed'' approach. This approach takes advantage of a specific configuration where the parameters of the elementary signals which compose a mixture are known before the mixing process. Using the tools which are proposed in this thesis, the minimal side information is computed and transmitted with the mixture signal. This allows any kind of transformation of the mixture signal with a constraint over the resulting quality. When the compatibility with existing audio formats is required, the side information is embedded directly into the analyzed audio signal using a watermarking technique. This work describes several theoretical and practical aspects of audio signal processing. We show that a classic estimator combined with the sufficient side information can obtain better performances than classic approaches (classic estimation or pure coding).
landing page: https://theses.hal.science/tel-00954965
identifier: NNT: 2013BOR14973
keyword: analyse-spectrale
keyword: audio
keyword: audio-coding
keyword: codage-audio
keyword: estimation
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: music-automatic-transcription
keyword: separation-de-sources
keyword: sources-separation
keyword: spectral-analysis
keyword: theses
keyword: transcription-automatique-de-la-musique
issued: 2026-05-06T03:57:07.958176
modified: 2026-05-06T03:57:07.958180
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Informed approach for sound and music analysis
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00954965
issued: 2026-05-06T03:57:07.963943
modified: 2026-05-06T03:57:07.925747