  Detecting piano pedalling techniques in polyphonic music remains a
challenging task in music information retrieval. While other piano-related
tasks, such as pitch estimation and onset detection, have seen improvement
through applying deep learning methods, little work has been done to develop
deep learning models to detect playing techniques. In this paper, we propose a
transfer learning approach for the detection of sustain-pedal techniques, which
are commonly used by pianists to enrich the sound. In the source task, a
convolutional neural network (CNN) is trained for learning spectral and
temporal contexts when the sustain pedal is pressed using a large dataset
generated by a physical modelling virtual instrument. The CNN is designed and
experimented through exploiting the knowledge of piano acoustics and physics.
This can achieve an accuracy score of 0.98 in the validation results. In the
target task, the knowledge learned from the synthesised data can be transferred
to detect the sustain pedal in acoustic piano recordings. A concatenated
feature vector using the activations of the trained convolutional layers is
extracted from the recordings and classified into frame-wise pedal press or
release. We demonstrate the effectiveness of our method in acoustic piano
recordings of Chopin's music. From the cross-validation results, the proposed
transfer learning method achieves an average F-measure of 0.89 and an overall
performance of 0.84 obtained using the micro-averaged F-measure. These results
outperform applying the pre-trained CNN model directly or the model with a
fine-tuned last layer.
