Attention Mechanisms in Neural Source-Filter Models for MIDI-to-Audio Synthesis and Temporal Alignment Accuracy

Abstract

Abstract: In this paper, we present a neural network approach for synchronizing audio recordings of human piano performances with their corresponding loosely aligned MIDI files. The task is addressed using a Convolutional Recurrent Neural Network (CRNN) architecture, which effectively captures spectral and temporal features by processing an unaligned piano roll and a spectrogram as inputs to estimate the aligned piano roll. To train the network, we create a dataset of piano pieces with augmented MIDI files that simulate common human timing errors. The proposed model achieves up to 20\% higher alignment a

Research Question

How do attention mechanisms in neural source-filter models influence the temporal alignment accuracy (measured via DTW-based metrics) in MIDI-to-audio synthesis compared to autoregressive text-to-speech models like Tacotron 2?

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