The lore has it that, during the making of the record, Hendrix cherished a sort of erupting spontaneity. And on “Hey Joe,” or on “I Don’t Live Today,” you can hear all those lessons efflorescing in one brilliant plume. It was his debut album with drummer Mitch Mitchell and bassist Noel Redding, but it came after a run of rigorous schooling in blues and rock’n’roll: Hendrix had played behind the likes of Sam Cooke, Jackie Wilson, and B.B. This was the fundamental and most thrilling promise of psychedelia, and this album still delivers it without a hint of dated goofiness. Here, too, there is something fundamental to Hendrix, the sense that he was here to dissolve upon contact.Īre You Experienced is the sound of a brilliant, humming engine of a mind allowing itself to overheat and melt down on purpose. When Jimi’s voice arrives soon after, he sounds a thousand miles away, swathed in so much reverb that he seems to be in danger of slipping away. The bleary riff that opens “Purple Haze” is the sound of psychedelia distilled-built on the most ear-troubling and unnatural-sounding interval in all of Western music, the introductory tritone contains all of the era’s unstable energy in a few notes.
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We propose a novel semantic segmentation algorithm by learning a deconvolution network. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods with more complete strokes and less noise in the generated character images. Through random font generation tasks and extraction of glyph content and style separately, the font generator learns the prior knowledge of character structure in the meta-training stage, and then quickly adapts to the generation of new fonts with a few samples by fine-tuning of adversarial training. The meta-training makes the font generator easy to fine-tune for new font generation tasks. To apply meta-learning to font generation, we present a meta-training strategy based on Model-Agnostic Meta-Learning (MAML) and a task organization method for font generation. Existing deep meta-learning methods mainly focus on few-shot image classification. To address these problems, this paper proposes a novel deep meta-learning-based font generation method (MLFont) for few-shot Chinese font generation, which leverages existing fonts to improve the generalization capability of the model for new fonts. When there are insufficient reference samples for the target font, existing deep learning-based methods cannot avoid overfitting caused by too few samples, resulting in blurred glyphs and incomplete strokes. The automatic generation of Chinese fonts is challenging due to the large quantity and complex structure of Chinese characters. Follow the instructions to complete the installation. Continue by unlocking any unapproved Android settings. 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