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6 results for “auditory scene”
Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task"
<p>Raw data for the simulation study " Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task" [1].</p> <p>[1] Josupeit, A., Schoenmaker, E., van de Par, S., & Hohmann, V. (2018). Sparse periodicity‐based auditory features explain human performance in a spatial multitalker auditory scene analysis task. <em>European Journal of Neuroscience</em>, https://doi.org/10.1111/ejn.13981.</p>
Dataset supplementing "Low-high-low or high-low-high? Pattern effects on sequential auditory scene analysis."
<p>These data supplement the paper</p> <p>Thomassen, S., Hartung, K., Einhäuser, W., & Bendixen, A. (2022). Low-high-low or high-low-high? Pattern effects on sequential auditory scene analysis. <em>Journal of the Acoustical Society of America, 152</em>(5), 2758-2768.<a href="https://doi.org/10.1121/10.0015054"> https://doi.org/10.1121/10.0015054</a></p> <p><br> figure2.m, figure3.m and figure4.m reproduce the respective figures of the paper, using the data of dataExp1.mat, dataExp2.mat and dataExp3.mat, respectively (Figure 1 is not a Results figure).</p> <p>Please note that the legend command may throw a warning or an error in some matlab versions, when interpreting the underscore (_) as command character. These warnings can be ignored or the legend command commented out.</p> <p><br> The mat-files contain the following data:</p> <p>dataExp1.mat:</p> <p>The 18 x 4 x 3 (subjects x Delta f x pattern) cell arrays</p> <p> time_buttonPress_integrated <br> time_buttonPress_segregated <br> time_buttonRelease_integrated <br> time_buttonRelease_segregated </p> <p>contain the time points (in ms relative to block onset) at which the button for integrated / segregated percept is pressed / released. These matrices are already sorted by condition (in Delta f and pattern), and the buttons organized by reported percept.</p> <p> </p> <p>The 18 x 4 x 3 (subjects x Delta f x pattern) matrix</p> <p> time_blockEnd</p> <p>contains the timepoint (in ms after onset) of the end of each block.</p> <p><br> The 18 x 4 x 3 (subjects x Delta f x pattern) matrix<br> <br> originalBlockNum </p> <p>contains the serial order of the block of this condition during the experiment.</p> <p><br> The 1 x 4 vector</p> <p> Df</p> <p>contains the Delta f levels used (in order as used in the matrices' and cells' second dimension).</p> <p><br> The 1 x 3 cell<br> <br> patternLabel </p> <p>contains the patterns used (in order as used in the matrices' and cells' third dimension).</p> <p>see figure2.m for an example</p> <p> </p> <p>dataExp2.mat:</p> <p>The 18 x 2 x 2 x 2 x 20 cell</p> <p> buttonTimes</p> <p>contains the times (in ms relative to trial onset) at which the button status changed.</p> <p><br> The 18 x 2 x 2 x 2 x 20 cell</p> <p> buttonStates</p> <p>contains the new state of the button at the corresponding time in buttonTimes.</p> <p>The dimensions of both cell arrays are (in order):<br> subject (18 levels)<br> convergence (2 levels: divergent, convergent)<br> pattern (2 levels: HLH_, LHL_)<br> duration (2 levels: short/fast [10s], long/slow [15s])<br> instance (20 repetitions).</p> <p>see figure3.m for an example</p> <p> </p> <p>dataExp3.mat</p> <p>The 20 x 2 x 4 x 30 (subjects x pattern x Delta f x repetition) cell arrays</p> <p> time_buttonPress_integrated </p> <p> time_buttonPress_segregated </p> <p>contain the time points (in s relative to block onset) at which the button for integrated / segregated percept is pressed.</p> <p><br> The 1 x 2 cell</p> <p> patternLabel </p> <p>contains the patterns used (in order as used in the cells' second dimension).</p> <p>The 1 x 4 vector</p> <p> Df</p> <p>contains the Delta f levels used (in order as used in the cells' third dimension).</p> <p><br> Note that the order of dimensions and of patterns deviates between the three experiments, but is consistent within each experiment.</p> <p> </p>
Data from: FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes
<p>This data set was analysed for the publication "FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes" by Hausfeld, Hamers, and Formisano (<em>Communications Biology</em>, 2024). Anatomical and functional MRI was acquired at 7 Tesla. Participants listened to speech of 1 or 2 (concurrent) audiobooks. To analyze fMRI-based speech tracking, participants were asked to listen to one speaker by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data [single particpant folders S1-15] (preprocessed) and individual speech tracking maps are contained in the participant-specific files S[participant_ID].zip in folder "MRI"</p> <p>- Stimulus descriptions (i.e., envelopes) are included in the folder "ENVELOPES"</p> <p>- Individual results (tracking map similarities and behavioral outcomes) are included in "INDIV_RESULTS"</p> <p>- Code to recreate figures is provided in folder "CODE" </p> <p>- the README contains information on the repository's content</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the <em>single speaker </em>and<em> concurrent speakers</em> conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p> <p> </p>
Data from: Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex
<p>This data set was analysed for the publication "Acoustic and higher-level representations of naturalistic auditory scenes in human auditory and frontal cortex" by Lars Hausfeld, Lars Riecke and Elia Formisano (<a href="https://doi.org/10.1016/j.neuroimage.2018.02.065">10.1016/j.neuroimage.2018.02.065</a>). Anatomical and functional MRI was acquired at 7 Tesla and over the course of 3 sessions for each participant. Participants listened to natural auditory scenes consisting of three sound sources: voice, instrument and pure tone. To reveal effects of selective attention, participants were asked to listen to one of the three sources by performing a task. </p> <p>The dataset is arranged as follows:</p> <p>- MRI data for each participant are contained in the participant-specific folders S[participant id]_MRIdata.zip</p> <p>- Information and design, stimulation protocols and sounds are contained in DesignProtocolsSounds.zip</p> <p>- README files contain necessary information on the MRI acquisition, experimental design and the coding of conditions</p> <p> </p> <p>Please note additional information in the original publication</p> <p> </p> <p>Abstract of corresponding manuscript</p> <p>In everyday life, we are often confronted with auditory scenes comprising multiple simultaneous sounds. When listening to two simultaneous talkers, the neural representation of the attended talker is selectively enhanced in auditory cortex. However, it remains unknown whether and how this selective attention mechanism operates on representations of different natural sound categories. In this high-field fMRI study we presented participants with simultaneous voices and musical instruments while manipulating their focus of attention. We found an attentional enhancement of neural sound representations in temporal cortex at locations that depended on the attended category (i.e., voices or instruments). In contrast, we found that in frontal cortex the site of enhancement was independent of the attended category and the same regions could flexibly represent any attended sound regardless of its category. These results are relevant to elucidate the interacting mechanisms of bottom-up and top-down processing during real-life audition.</p> <p> </p> <p>Author contact: lars.hausfeld@maastrichtuniversity.nl</p>
Auditory Scene Analysis dataset (Multichannel universal sound separation & polyphonic audio classification)
<p>We constructed a new dataset for <strong>multichannel universal sound separation</strong> and <strong>polyphonic audio classification</strong> tasks.</p> <p>We constructed a new dataset for multichannel USS and polyphonic audio classification tasks. The proposed dataset is designed to reflect various conditions, including moving sources with temporal onsets and offsets. For foreground sound sources, signals from 13 audio classes were selected from open-source databases (Pixabay and FSD50K, Librispeech, MUSDB18, Vocalsound). These signals were resampled to 16 kHz and pre-processed by either padding zeros or cropping to 4 seconds. Each sound source has a 75% probability of being a moving source, with speeds ranging from 0 to 3 m/s. The dataset features between 2 to 4 foreground sound sources, along with one background noise from the diffused TAU-SNoise dataset with a signal-to-noise ratio (SNR) ranging from 6 to 30 dB. The simulations were conducted using gpuRIR. Room dimensions were set to a width and length between 5 and 8 meters, and a height between 3 and 4 meters, with reverberation times ranging from 0.2 to 0.6 seconds. These parameters were sampled from uniform distributions. We simulated spatialized sound sources using a 4-channel tetrahedral microphone array with a radius of 4.2 cm. The procedure for dataset generation and details about class configuration and durations of audio clips are provided in the paper. This dataset poses a significant challenge for separation tasks due to the inclusion of moving sources, onset and offset conditions, overlapped in-class sources, and noisy reverberant environments.</p> <p>The procedure for dataset generation and details about class configuration and durations of audio clips are provided in the paper. This dataset poses a significant challenge for separation tasks due to the inclusion of moving sources, onset and offset conditions, overlapped in-class sources, and noisy reverberant environments.</p>
Electroencephalography dataset from a visual-scene and auditory-linguistic N400 study
<p>Forty participants were presented with auditory-linguistic, as well as visual-scene semantic inconsistencies within the same experiment.</p> <p>This dataset includes raw data, epoched data, as well as ICA and event objects.</p> <p>More information as well as the analysis code can be found in the related repository: https://github.com/DejanDraschkow/n3n4. </p>
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International Brain Laboratory public data
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OpenNeuro
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