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  2. Sample-rate conversion - Wikipedia

    en.wikipedia.org/wiki/Sample-rate_conversion

    This was based on a TV rate camera viewing a monitor displaying the Apollo slow-scan images. [6] Movies (shot at 24 frames per second) are converted to television (roughly 50 or 60 fields [B] per second). To convert a 24 frame/sec movie to 60 field/sec television, for example, alternate movie frames are shown 2 and 3 times, respectively.

  3. Spectrogram - Wikipedia

    en.wikipedia.org/wiki/Spectrogram

    In deep learning-keyed speech synthesis, spectrogram (or spectrogram in mel scale) is first predicted by a seq2seq model, then the spectrogram is fed to a neural vocoder to derive the synthesized raw waveform. By reversing the process of producing a spectrogram, it is possible to create a signal whose spectrogram is an arbitrary image.

  4. Sampling (signal processing) - Wikipedia

    en.wikipedia.org/wiki/Sampling_(signal_processing)

    When it is necessary to capture audio covering the entire 20–20,000 Hz range of human hearing [6] such as when recording music or many types of acoustic events, audio waveforms are typically sampled at 44.1 kHz , 48 kHz, 88.2 kHz, or 96 kHz. [7] The approximately double-rate requirement is a consequence of the Nyquist theorem. Sampling rates ...

  5. Reconstruction filter - Wikipedia

    en.wikipedia.org/wiki/Reconstruction_filter

    Both idealized Dirac pulses, zero-order held steps and other output pulses, if unfiltered, would contain spurious high-frequency replicas, "or images" of the original bandlimited signal. Thus, the reconstruction filter smooths the waveform to remove image frequencies (copies) above the Nyquist limit. In doing so, it reconstructs the continuous ...

  6. Deep learning speech synthesis - Wikipedia

    en.wikipedia.org/wiki/Deep_learning_speech_synthesis

    A stack of dilated casual convolutional layers used in WaveNet [1]. In September 2016, DeepMind proposed WaveNet, a deep generative model of raw audio waveforms, demonstrating that deep learning-based models are capable of modeling raw waveforms and generating speech from acoustic features like spectrograms or mel-spectrograms.

  7. WaveNet - Wikipedia

    en.wikipedia.org/wiki/WaveNet

    WaveNet is a deep neural network for generating raw audio. It was created by researchers at London-based AI firm DeepMind.The technique, outlined in a paper in September 2016, [1] is able to generate relatively realistic-sounding human-like voices by directly modelling waveforms using a neural network method trained with recordings of real speech.

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  9. WavPack - Wikipedia

    en.wikipedia.org/wiki/WavPack

    WavPack also incorporates a "hybrid" mode, which still provides the features of lossless compression, but creates two files: a relatively small, high-quality, lossy file (.wv) that can be used by itself; and a "correction" file (.wvc) that, when combined with the lossy file, provides full lossless restoration.