用卷积神经网络和常数Q变换实现钢琴音频自动转谱
Automatic Music Transcription using Convolutional Neural Networks and Constant-Q transform
- 先用常数Q变换提取音频特征，再输入卷积神经网络
- 可将单声道钢琴.wav音频转为乐谱表示
- 适合音乐信息检索与智能作曲研究者
自动音乐转录（AMT）是分析一段音乐录音并检测演奏音符的任务，尤其在多声部音乐中极具挑战性。目标是通过分析包含多个同时演奏音符的音频信号，生成对应的乐谱表示。本文设计了一种处理流程，可将. wav格式的古典钢琴音频转换为乐谱表示。音频信号的特征通过常数Q变换提取，所得系数作为卷积神经网络（CNN）模型的输入。
原文摘要 · Abstract (English)
Automatic music transcription (AMT) is the problem of analyzing an audio recording of a musical piece and detecting notes that are being played. AMT is a challenging problem, particularly when it comes to polyphonic music. The goal of AMT is to produce a score representation of a music piece, by analyzing a sound signal containing multiple notes played simultaneously. In this work, we design a processing pipeline that can transform classical piano audio files in .wav format into a music score representation. The features from the audio signals are extracted using the constant-Q transform, and the resulting coefficients are used as an input to the convolutional neural network (CNN) model.
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