Automatic music transcription is considered by many to be a key enabling technology in
music signal processing. However, the performance of transcription systems is still …

Automatic music transcription (AMT) is the task of transcribing audio recordings into
symbolic representations. Recently, neural network-based methods have been applied to …

A new method for the estimation of multiple concurrent pitches in piano recordings is
presented. It addresses the issue of overlap** overtones by modeling the spectral …

Music signal processing may appear to be the junior relation of the large and mature field of
speech signal processing, not least because many techniques and representations …

Multiple pitch estimation consists of estimating the fundamental frequencies and saliences of
pitched sounds over short time frames of an audio signal. This task forms the basis of …

In this paper a new approach for polyphonic piano note onset transcription is presented. It is
based on a recurrent neural network to simultaneously detect the onsets and the pitches of …

Due to the difficulty of creating pitch-labeled training data that cover the rich diversity found
in music signals, unsupervised feature-based approaches derived from signal processing …

Piano transcription systems are typically optimized to estimate pitch activity at each frame of
audio. They are often followed by carefully designed heuristics and post-processing …

Polyphonic pitch transcription consists of estimating the onset time, duration and pitch of
each note in a music signal. This task is difficult in general, due to the wide range of possible …

We present a neural network model that learns to produce music scores directly from audio
signals. Instead of employing commonplace processing steps, such as frequency transform …