EEG-Based Decoding of Auditory Attention to a Target Instrument in Polyphonic Music

Auditory attention decoding aims at determining which sound source a subject is "focusing on". In this work, we address the problem of EEG-based decoding of auditory attention to a target instrument in realistic polyphonic music. To this end, we exploit a stimulus reconstruction model which was proven to decode successfully the attention to speech in multi-speaker environments. To our knowledge, this model was never applied to musical stimuli for decoding attention. The task we consider here is quite complex as the stimuli used are polyphonic, including duets and trios, and are reproduced using loudspeakers instead of headphones. We consider the decoding of three different audio representations and investigate the influence on the decoding performance of multiple variants of musical stimuli, such as the number and type of instruments in the mixture, the spatial rendering, the music genre and the melody/rhythmical pattern that is played. We obtain promising results, comparable to those obtained on speech data in previous works, and confirm that it is possible to correlate the human brain activity with musically relevant features of the attended source.

• Publication year

  2019

• Venue

  IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

• Publication date

  2019-10-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/WASPAA.2019.8937219
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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