On-the-fly audio source separation

Dalia El Badawy (1) , Ngoc Q. K. Duong (1) , Alexey Ozerov (1)

1 Technicolor R & I [Cesson Sévigné]

This paper addresses the challenging task of single channel audio source separation. We introduce a novel concept of on-the-fly audio source separation which greatly simplifies the user's interaction with the system compared to the state-of-the-art user-guided approaches. In the proposed framework, the user is only asked to listen to an audio mixture and type some keywords (e.g. "dog barking", "wind", etc.) describing the sound sources to be separated. These keywords are then used as text queries to search for audio examples from the internet to guide the separation process. In particular, we propose several approaches to efficiently exploit these retrieved examples, including an approach based on a generic spectral model with group sparsity-inducing constraints. Finally, we demonstrate the effectiveness of the proposed framework with mixtures containing various types of sounds.

Mots clés: On-the-fly source separation; user-guided; non-negative matrix factorization; group sparsity; universal spectral model

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Dalia El Badawy, Ngoc Q. K. Duong, Alexey Ozerov. On-the-fly audio source separation. the 24th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2014), Sep 2014, Reims, France. ⟨hal-01023221⟩