*.wav
In-house raw audio data is collected from road traffic areas in Durgapur, a sub-urban city in India. We have developed a customized android application for collecting the data from the environment. Approximately 124km road traffic area is covered to do the same. The android application helps us to monitor the noise data in different sampling rates and bit rates. The sampling rate can be set to one of 8000 Hz, 16 000 Hz, 32 000 Hz, 44 100 Hz, 48 000 Hz. On the other hand, the bit rate can be set to one of 24 kbps, 48 kbps, 96 kbps, 128 kbps, 192 kbps.
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Asthma is a common, usually long-term respiratory disease with negative impact on society and the economy worldwide. Treatment involves using medical devices (inhalers) that distribute medicationto the airways, and its efficiency depends on the precision of the inhalation technique. Health monitoring systems equipped with sensors and embedded with sound signal detection enable the recognition of drug actuation and could be powerful tools for reliable audio content analysis.
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The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers.
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SDU-Haier-ND (Shandong University-Haier-Noise Detection) is a sound dataset jointly constructed by Shandong University and Haier, which contains the operating sound of the internal air conditioner collected during the product quality inspection. We collected and marked a batch of quality inspection sounds of air conditioners in real production environments to form this data set, including normal sound samples and abnormal sound samples.
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Crowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. Crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations.
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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c
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Noisy speech and ideal binary mask estimates for the SPN-ASI repository.
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The training, validation, and test set used for Deep Xi (https://github.com/anicolson/DeepXi).
Training set:
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This is the noisy-speech test set used in the original Deep Xi paper: https://doi.org/10.1016/j.specom.2019.06.002. The clean speech and noise used to create the noisy-speech set are also available.
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