Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation
Fig 5
Evaluation using temporally perturbed stimuli.
(A–C) Analysis using textured stimuli. (A) Identification analysis is conducted between the true or textured stimuli and lure candidates using extracted features. (B) The first row shows the original spectrograms of the stimulus sounds, while the second row illustrates the spectrograms of textured stimuli. (C) Each panel displays the identification accuracy for individual subjects (e.g., S3, S4). The dark blue bars represent the identification accuracy for reconstructed sounds using the original true stimuli, while the orange bars indicate accuracy for reconstructed sounds using the textured true stimuli. Error bars denote the 95% CI, calculated from 50 data points. (D–F) Analysis using temporally shuffled stimuli. (D) Identification analysis is performed between the true or shuffled true stimuli and lure candidates using extracted features. (E) The first row depicts the original spectrograms of the stimulus sounds, and the second row presents examples of temporally shuffled stimuli. Here, spectrograms are divided into equal-sized time windows (e.g., 48 ms), and the segments are randomly shuffled to introduce temporal perturbations. (F) Each panel shows the identification accuracy for individual subjects. The bars represent the mean identification accuracy for various segment sizes, with different colors indicating specific segment sizes. The data underlying this figure are provided in S2 Data.