Automatic Music Transcription: An Overview <s> Context-specific transcription <s> This paper presents a novel approach to automatic transcription of piano music in a context-dependent setting. This approach employs convolutional sparse coding to approximate the music waveform as the summation of piano note waveforms (dictionary elements) convolved with their temporal activations (onset transcription). The piano note waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. During transcription, the note waveforms are fixed and their temporal activations are estimated and post-processed to obtain the pitch and onset transcription. This approach works in the time domain, models temporal evolution of piano notes, and estimates pitches and onsets simultaneously in the same framework. Experiments show that it significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments. <s> BIB001 </s> Automatic Music Transcription: An Overview <s> Context-specific transcription <s> Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant interest within the research community, several studies have reported on a “glass ceiling” effect, an apparent limit on the transcription accuracy that current methods seem incapable of overcoming. In this paper, we explore how much this effect can be mitigated by focusing on a specific instrument class and making use of additional information on the recording conditions available in studio or home recording scenarios. In particular, exploiting the availability of single note recordings for the instrument in use, we develop a novel signal model employing variable-length spectro-temporal patterns as its central building blocks-tailored for pitched percussive instruments such as the piano. Temporal dependencies between spectral templates are modeled, resembling characteristics of factorial scaled hidden Markov models FS-HMM and other methods combining nonnegative matrix factorization with Markov processes. In contrast to FS-HMMs, our parameter estimation is developed in a global, relaxed form within the extensible alternating direction method of multipliers framework, which enables the systematic combination of basic regularizers propagating sparsity and local stationarity in note activity with more complex regularizers imposing temporal semantics. The proposed method achieves an f -measure of 93-95% for note onsets on pieces recorded on a Yamaha Disklavier MAPS DB. <s> BIB002
While the creation of a blind multi-instrument AMT system without specific knowledge of the music style, instruments, and recording conditions is yet to be achieved, considerable progress has been reported on the problem of context-specific transcription, where prior knowledge of the sound of the specific instrument model or manufacturer and the recording environment is available. For context-specific piano transcription, multipitch detection accuracy can exceed 90% BIB001 , BIB002 , making such systems appropriate for user-facing applications. Open work in this topic includes the creation of context-specific AMT systems for multiple instruments.