This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performing model in the 2017 MIREX Multiple Fundamental Frequency Estimation evaluation. This class of models shares parameters in the log-frequency domain, which exploits the frequency invariance of music to reduce the number of model parameters and avoid overfitting to the training data. All models in this paper were trained with supervision by labeled data from the MusicNet dataset, augmented by random label-preserving pitch-shift transformations.
Labels on music recordings are typically obtained in one of two ways. One approach is to perform music on instruments that are wired to record a MIDI transcription as they are played. These transcriptions are precise time-aligned labels for a performance. The commercially available Yamaha Disklavier piano is wired in this way, and has lead to the creation of datasets such as MAPS @cite_15 . The second approach is to solve an alignment problem, warping a musical score onto a given recording. This alignment can be constructed using an optimal-alignment protocol, as in the SyncRWC @cite_5 , Lakh @cite_6 , and MusicNet @cite_12 datasets. Or it can be constructed using information supplied by a human annotator, for example the Su dataset used for the MIREX evaluation @cite_8 .
@cite_8 @cite_6 @cite_5 @cite_15 @cite_12
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While recent years have witnessed large progress in the algorithm of automatic music transcription (AMT), the development of general and sizable datasets for AMT evaluation is relatively stagnant, predominantly due to the fact that manually annotating and checking such datasets is labor-intensive and time-consuming. In this paper we propose a novel note-level annotation method for building AMT datasets by utilizing human’s ability in following music in real-time. To test the quality of the annotation, we further propose an efficient method in qualifying an AMT dataset based on the concepts of onset error difference and the tolerance computed from the evaluation result. According to the experiments on five piano solos and four woodwind quintets, we claim that the proposed annotation method is reliable for evaluation of AMT algorithms. This paper describes the design policy and specifications of the RWC Music Database, a copyright-cleared music database (DB) compiled specifically for research purposes. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. We therefore recently constructed the RWC Music Database comprising four original component DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database (15 pieces), Classical Music Database (50 pieces), and Jazz Music Database (50 pieces). In this paper we report the construction of two additional component DBs: the Music Genre Database (100 pieces) and Musical Instrument Sound Database (50 instruments). For all 315 musical pieces, we prepared original audio signals, corresponding standard MIDI files, and text files of lyrics (for songs). For all 50 instruments, we recorded individual sounds at half-tone intervals with several variations of playing styles, dynamics, instrument manufacturers, and musicians. It is our hope that our DB will make a significant contribution to future advances in the field of music information processing. A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic and sub-harmonic erroneous pitch estimations. This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series. The pitch set which maximizes an approximate likelihood is selected from among a restricted number of possible pitch combinations as the one. Tests have been conducted on a large homemade database called MAPS, composed of piano recordings from a real upright piano and from high-quality samples. This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end-to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.