Automatic Music Transcription: An Overview <s> MLMs <s> We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription. <s> BIB001 </s> Automatic Music Transcription: An Overview <s> MLMs <s> We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model . The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network-based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yield the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications. <s> BIB002
As outlined in the "Analogies to Other Fields" section, AMT is closely related to ASR. In the same way that a typical ASR system consists of an acoustic component and a language component, an AMT system can model both the acoustic sequences and the underlying sequence of notes and other music cues over time. AMT systems have thus incorporated MLMs for modeling sequences of notes in a polyphonic context, with the aim of improving transcription performance. The capabilities of deep-learning methods toward modeling high-dimensional sequences have recently made polyphonic music sequence prediction possible. Boulanger-Lewandowski et al. BIB001 combined a restricted Bolzmann machine (RBM) with an RNN for polyphonic music prediction, which was used to postprocess the acoustic output of an AMT system. Sigtia et al. BIB002 also used the aforementioned RNN-RBM as an MLM and combined the acoustic and language predictions using a probabilistic graphical model. While these initial works showed promising results, there are several directions for future research in MLMs. These include creating unified acoustic and language models (as opposed to using MLMs as postprocessing steps) and modeling other musical cues, such as chords, key, and meter (as opposed to simply modeling note sequences).