monetjoe/Piano-Classification
This study converts piano recordings to mel spectrogram and classifies them by SOTA pre-trained neural network backbones in CV. Comparative experiments show that SqueezeNet achieves a best classification accuracy of 92.37%.|该项目将钢琴录音转为为mel频谱图,使用微调后的前沿计算机视觉领域预训练深度学习骨干网络对其进行分类,对比实验可知SqueezeNet作为最优网络正确率可达92.37%
PythonMIT
Issues
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未来考虑构建一个用软音源渲染得到的钢琴数据集
#6 opened - 0
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cover
#4 opened - 0
需要保证数据集 8 1 1 分配后的文件夹中每个类别至少保证有一个数据
#3 opened - 0
alexnet 的输入格式与其它预训练模型不同
#2 opened - 0
优化acc计算公式,增加f1-score统计等
#1 opened