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ShareScore release 0.7.1
Dataset results
19 results for “Speech enhancement”
BinauRec: A dataset to test the influence of the use of room impulse responses on binaural speech enhancement
<p>BinauRec is a dataset for binaural speech enhancement. It is composed of real recordings, measured and simulated room impulse responses for the same audio scenes. Measurements are realized using behind-the-ears hearing aid shells, with and without a dummy head.</p>
A Database for Reasearch on Detection and Enhancement of Speech transmitted over HF links
<p>We present an open database for the development of detection and enhancement algorithms of speech transmitted over HF radio channels.<br> It consists of audio samples recorded by various receivers at different locations across Europe, all monitoring the same single-sideband modulated transmission from a base station in Paderborn, Germany. Transmitted and received speech signals are precisely time aligned to offer parallel data for supervised training of deep learning based detection and enhancement algorithms.</p>
WHISPER SET 1: a dataset for multi-channel, multi-device speech separation and speech enhancement
<p>This dataset is <code>WHISPER SET 1,</code> a dataset for speech enhancement and source separation recorded with a Wireless Acoustic Sensor Network (WASN) called WHISPER <a href="https://ieeexplore.ieee.org/abstract/document/8110202">Kiselev2018</a>. The dataset contains samples for up to 4 concurrent speakers and speech in noise. The dataset was recorded in a room with low reverberation (T_60 = 0.2 s) and using 16 microphones. In general, each track contains first a calibration phase where each of the speakers sequentially is active alone for 15 seconds. Followed by 15 seconds of all the speakers together (plus noise in some cases). </p> <p>If you use this dataset please cite:</p> <ul> <li><strong>E. Ceolini, I. Kiselev and S. Liu, "Evaluating multi-channel multi-device speech separation algorithms in the wild: a hardware-software solution," in <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em>.</strong></li> </ul> <p>===</p> <p>Each sample is a 16-channel wav file in which the order of the channel follows the following logic:</p> <p>0 - module 5 mic 1 1 - module 5 mic 2 2 - module 5 mic 3 3 - module 5 mic 4 4 - module 6 mic 1 5 - module 6 mic 2 6 - module 6 mic 3 7 - module 6 mic 4 8 - module 7 mic 1 9 - module 7 mic 2 10 - module 7 mic 3 11 - module 7 mic 4 12 - module 8 mic 1 13 - module 8 mic 2 14 - module 8 mic 3 15 - module 8 mic 4</p> <p>Refer to the <a href="https://github.com/SensorsAudioINI/WHISPER_SET_1/blob/master/WHISPER4_floor_annotated.png">floor plan</a> for a visual illustration of the microphone arrangement.</p> <p>The files are divided into two subfolders, one for the samples of speech enhancement and one for the samples of speech separation.</p> <ul> <li>In the folder of speech separation, the files are divided into subfolders defining the number of speakers in the mixtures (2, 3, or 4)</li> <li>In the folder of speech enhancement, the files are divided into subfolders following the SNR of the mixture (0, -5, -10 dB)</li> </ul> <p>Samples are ordered in folders. Each sample folder contains a 15 seconds 16-channels <code>mixture.wav</code> file, plus the 15 seconds 16-channels <code>calibX.wav</code> files one for each speaker alone or noise alone in the mixture. That is a sample with a mixture with 4 speakers will have 4 calibration files (calib1.wav, calib2.wav, calib3.wav, calib4.wav) and a mixture of a speaker plus noise will have 2 calibration files one for speech (calib1.wav) and one for noise (calib2.wav).</p> <p>== </p> <p>A Jupyter notebook is included to show an example of how to use the data of this dataset for speech separation and speech enhancement using beamforming. The notebook is dependent on <a href="https://github.com/Enny1991/beamformers">this beamforming library</a> and <a href="https://github.com/Enny1991/sep_eval">this tool</a> to evaluate the quality of the separation.</p> <p>==</p> <p>Refer to the README.md in the dataset for more information.</p> <p>For any question please contact enea.ceolini@gmail.com</p>
Open-Unmix for Speech Enhancement (UMX SE)
<p>Weights of Open-Unmix trained on the 28-speaker version of Voicebank+Demand (Sampling rate: 16kHz). The weights can be used with <a href="https://github.com/sigsep/open-unmix-nnabla">open-unmix-nnabla</a> and <a href="https://github.com/sigsep/open-unmix-pytorch">open-unmix-pytorch</a>.</p>
Enhanced RAVDESS Speech Dataset
<p>This is a modified version of the speech audio contained within the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. The original dataset can be found <a href="https://zenodo.org/record/1188976#.YKvCMKhKhPY">here</a>. The unmodified version of just the speech audio used as source material for this dataset can be found <a href="https://www.kaggle.com/uwrfkaggler/ravdess-emotional-speech-audio">here</a>. This dataset performs speech enhancement and bandwidth extension on the original speech using HiFi-GAN. HiFi-GAN produces high-quality speech at 48 kHz that contains significantly less noise and reverb relative to the original recordings.</p> <p>If you use this work as part of an academic publication, please cite the papers corresponding to both the original dataset as well as HiFi-GAN:</p> <blockquote> <p>Livingstone SR, Russo FA (2018) The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5): e0196391. <a href="https://doi.org/10.1371/journal.pone.0196391">https://doi.org/10.1371/journal.pone.0196391</a>.</p> <p>Su, Jiaqi, Zeyu Jin, and Adam Finkelstein. "HiFi-GAN: High-fidelity denoising and dereverberation based on speech deep features in adversarial networks." <em>Proc. Interspeech</em>. October 2020.</p> </blockquote> <p>Note that there are two recent papers with the name "HiFi-GAN". Please be sure to cite the correct paper as listed here.</p>
Data from: Neural correlates of multisensory enhancement in audiovisual narrative speech perception: a fMRI investigation
<p>This fMRI study investigated the effect of seeing articulatory movements of a speaker while listening to a naturalistic narrative stimulus. It had the goal to identify regions of the language network showing multisensory enhancement under synchronous audiovisual conditions. We expected this enhancement to emerge in regions known to underlie the integration of auditory and visual information such as the posterior superior temporal gyrus as well as parts of the broader language network, including the semantic system. To this end we presented 53 participants with a continuous narration of a story in auditory alone, visual alone, and both synchronous and asynchronous audiovisual speech conditions while recording brain activity using BOLD fMRI. We found multisensory enhancement in an extensive network of regions underlying multisensory integration and parts of the semantic network as well as extralinguistic regions not usually associated with multisensory integration, namely the primary visual cortex and the bilateral amygdala. Analysis also revealed involvement of thalamic brain regions along the visual and auditory pathways more commonly associated with early sensory processing. We conclude that under natural listening conditions, multisensory enhancement not only involves sites of multisensory integration but many regions of the wider semantic network and includes regions associated with extralinguistic sensory, perceptual and cognitive processing.</p>
Video Assisted Speech Technology to Enhance Motor Planning for Speech
ClinicalTrials.gov study NCT04764539. IPD Sharing: NO. Countries: 1. Publications: 7.
Data from: Neural correlates of multisensory enhancement in audiovisual narrative speech perception: a fMRI investigation
Open the record for dataset details and reuse information.
Effect of Speech Enhancer on Listening Effort With EEG
ClinicalTrials.gov study NCT06049199. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of the Non-invasive Brain Stimulation on the Speech Fluency Enhancement
ClinicalTrials.gov study NCT03990168. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Speech Production Enhancement Using Augmentative Communication for Kids
ClinicalTrials.gov study NCT07173049. IPD Sharing: YES. Countries: 1. Publications: 6.
Fall Risk Assessment and Speech Intelligibility Enhancement Using In-ear Device
ClinicalTrials.gov study NCT05365646. IPD Sharing: NO. Countries: 1. Publications: 2.
staRt: Enhancing Speech Treatment With Smartphone-delivered Biofeedback
ClinicalTrials.gov study NCT04474691. IPD Sharing: Not stated. Countries: 1. Publications: 0.
New Algorithms to Signal Processing for Speech Enhancement in Adult Cochlear Implant Recipients.
ClinicalTrials.gov study NCT06100393. IPD Sharing: NO. Countries: 1. Publications: 0.
Targeted TDCS to Enhance Speech-Language Treatment Outcome in Persons With Chronic Post-Stroke Aphasia.
ClinicalTrials.gov study NCT04432883. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Real-time Artificial Intelligence-based Speech Enhancement Methods for Hearing Aid Improvement
ClinicalTrials.gov study NCT05985473. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Motor Imagery for Treatment Enhancement and Efficacy in Persons With Apraxia of Speech
ClinicalTrials.gov study NCT06467136. IPD Sharing: YES. Countries: 1. Publications: 0.
Enhancing Speech Intelligibility Using Transcranial Alternating Current Stimulation (tACS)
ClinicalTrials.gov study NCT04036630. IPD Sharing: YES. Countries: 1. Publications: 0.
Enhancing Speech Fluency With Non-invasive Brain Stimulation in Developmental Stuttering
ClinicalTrials.gov study NCT02288598. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.