Audio

The Heidelberg Spiking Datasets comprise two spike-based classification datasets: The Spiking Heidelberg Digits (SHD) dataset and the Spiking Speech Command (SSC) dataset. The latter is derived from Pete Warden's Speech Commands dataset (https://arxiv.org/abs/1804.03209), whereas the former is based on a spoken digit dataset recorded in-house and included in this repository. Both datasets were generated by applying a detailed inner ear model to audio recordings. We distribute the input spikes and target labels in HDF5 format.

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  • Machine Learning
  • Last Updated On: 
    Fri, 12/06/2019 - 13:12

    The dataset consists of EEG recordings obtained when subjects are listening to different utterances : a, i, u, bed, please, sad. A limited number of EEG recordings where also obtained when the three vowels were corrupted by white and babble noise at an SNR of 0dB. Recordings were performed on 8 healthy subjects.

    348 views
  • Brain
  • Last Updated On: 
    Mon, 08/12/2019 - 11:24

    This Group aims to improve the sound characteristics of the output signal from the audio viewpoint when the silicon transistor is used in the acoustic amplifier circuit in the acoustical equipment field.When using a silicon transistor in an acoustic amplifier circuit, the sound output is known to be cool, rough, sharp, and not abundant. So audio enthusiasts still like amplifiers that use vacuum tubes.

    205 views
  • Other
  • Last Updated On: 
    Wed, 07/17/2019 - 19:31

    We propose a new concept audio system, It is an audio system with slots for inserting function units in one main body. It is a group for producing the first product for standardization. (Network audio player, DDC, DAC, PHONO equalizer, PRE Amplifier, POWER Amplifier, POWER SUPPLY, etc.)    The internal main board has slots for inserting the unit, and the corresponding unit can be installed and replaced with another compatible unit.  Function units are made in card format and can be upgraded or replaced with other branded products in the future.

    123 views
  • Other
  • Last Updated On: 
    Tue, 05/14/2019 - 03:22

    The steganography and steganalysis of audio, especially compressed audio, have drawn increasing attention in recent years, and various algorithms are proposed. However, there is no standard public dataset for us to verify the efficiency of each proposed algorithm. Therefore, to promote the study field, we construct a dataset including 33038 stereo WAV audio clips with a sampling rate of 44.1 kHz and duration of 10s. And, all audio files are from the Internet through data crawling, which is for a better simulation of a real detection environment.

    753 views
  • Signal Processing
  • Last Updated On: 
    Tue, 11/12/2019 - 10:38