Feature Learning for Chord Recognition: The Deep Chroma Extractor
Feature Learning for Chord Recognition: The Deep Chroma Extractor
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发表时间：
2016-12
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通讯作者：
Filip Korzeniowski;G. Widmer
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作者：
Filip Korzeniowski;G. Widmer
我们探索帧级音频特征学习的和弦识别使用人工神经网络。我们提出的论点，色度向量可能持有足够的信息来建模和弦识别音频的谐波内容，但标准的色度提取器计算太嘈杂的功能。这导致我们提出了一个学习的色度特征提取器的基础上人工神经网络。它被训练来计算色度特征，这些特征对和弦识别重要的谐波信息进行编码，同时对不相关的干扰具有鲁棒性。我们通过向网络提供具有上下文的音频频谱而不是单个帧作为输入来实现这一点。这样，网络可以学习选择性地补偿噪声并解决谐波模糊度。我们比较由此产生的功能，手工制作的，通过使用一个简单的线性框架分类器和弦识别各种数据集。结果表明，学习的特征提取器产生上级色度向量的和弦识别。
We explore frame-level audio feature learning for chord recognition using artificial neural networks. We present the argument that chroma vectors potentially hold enough information to model harmonic content of audio for chord recognition, but that standard chroma extractors compute too noisy features. This leads us to propose a learned chroma feature extractor based on artificial neural networks. It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences. We achieve this by feeding the network an audio spectrum with context instead of a single frame as input. This way, the network can learn to selectively compensate noise and resolve harmonic ambiguities. We compare the resulting features to hand-crafted ones by using a simple linear frame-wise classifier for chord recognition on various data sets. The results show that the learned feature extractor produces superior chroma vectors for chord recognition.